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Mathematics, Volume 12, Issue 1 (January-1 2024) – 168 articles

Cover Story (view full-size image): The authors introduce a novel option pricing model by adding stochastic interest rates and pure jump Lévy processes to an underlying price process driven by stochastic string shocks. They consider four different jump processes leading to different versions of the model: lognormal and double-exponential jump diffusions, CGMY, and generalized hyperbolic Lévy motion. In each case, they obtain closed or semi-closed form expressions for European call option prices. Moreover, they empirically evaluate the model's performance against S&P 500 call options. The findings indicate that (a) model performance is enhanced with the inclusion of jumps; (b) the model outperforms the alternative models with the same jumps; and (c) the model with CGMY jump offers the best fit across volatility regimes. View this paper
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20 pages, 4598 KiB  
Article
Ship Infrared Automatic Target Recognition Based on Bipartite Graph Recommendation: A Model-Matching Method
by Haoxiang Zhang, Chao Liu, Jianguang Ma and Hui Sun
Mathematics 2024, 12(1), 168; https://doi.org/10.3390/math12010168 - 4 Jan 2024
Viewed by 1098
Abstract
Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared [...] Read more.
Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS). Full article
(This article belongs to the Section Computational and Applied Mathematics)
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<p>Example images and bounding box localization demonstration of the dataset for cruise ship (<b>a</b>), warship (<b>b</b>), and container freighter (<b>c</b>). (<b>b</b>,<b>c</b>) The diversity of target imaging brightness variations and posture distribution using warship and container freighter as examples, respectively.</p>
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<p>The generation process of masking the target background area.</p>
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<p>The basic framework of the SIATR-BGR method.</p>
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<p>The acquisition method of knowledge construction in the SIATR-BGR method. (<b>a</b>) The calculation process of the weight matrix <math display="inline"><semantics> <mrow> <mi>U</mi> <mi>P</mi> </mrow> </semantics></math>; (<b>b</b>) three candidate model examples and the acquisition method of the weights <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> for a single sample with a certain label set to 0.</p>
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<p>Illustration of model-adaptive recommendation for the SIATR-BGR method. (<b>a</b>) Candidate model selection in the form of a bipartite graph, and (<b>b</b>) the corresponding matrix numerical computation method.</p>
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<p>The impact of the penalty factors <math display="inline"><semantics> <mi>α</mi> </semantics></math>(<math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>W</mi> <mi>P</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>) and <math display="inline"><semantics> <mi>β</mi> </semantics></math>(<math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>W</mi> <mi>P</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>) on the <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>c</mi> <mi>c</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>D</mi> <mi>M</mi> <mi>S</mi> </mrow> </semantics></math> of the SIATR-BGR recommendation system under different search values.</p>
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<p>Heat map matrix of various methods under multi-class scenarios. The numerical values in each cell of the figure represent the <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>c</mi> <mi>c</mi> </mrow> </semantics></math> of the method in the corresponding scenarios.</p>
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<p>The confusion matrix of the SIATR-BGR method in this paper when <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>W</mi> <mi>P</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>). (<b>a</b>) The form of class-wise counts; (<b>b</b>) the corresponding percentage form.</p>
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<p>Heat map matrix of various methods under multi-class scenarios. The numerical values in each cell of the figure represent the <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>D</mi> <mi>M</mi> <mi>S</mi> </mrow> </semantics></math> of the method in the corresponding scenarios.</p>
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<p>The impact of varying values of <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>U</mi> </msub> </mrow> </semantics></math> on the <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>c</mi> <mi>c</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>D</mi> <mi>M</mi> <mi>S</mi> </mrow> </semantics></math> performance of the SIATR-BGR recommendation system under conditions <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>.</p>
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<p>A statistical chart of the recommended frequencies concerning candidate models relative to their sizes. (<b>a</b>) The recommended frequencies of models under conditions where <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>P</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>W</mi> <mi>P</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>b</b>) the resource consumption statistics of each model.</p>
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17 pages, 362 KiB  
Article
Finite Difference Models of Dynamical Systems with Quadratic Right-Hand Side
by Mikhail Malykh, Mark Gambaryan, Oleg Kroytor and Alexander Zorin
Mathematics 2024, 12(1), 167; https://doi.org/10.3390/math12010167 - 4 Jan 2024
Viewed by 912
Abstract
Difference schemes that approximate dynamic systems are considered discrete models of the same phenomena that are described by continuous dynamic systems. Difference schemes with t-symmetry and midpoint and trapezoid schemes are considered. It is shown that these schemes are dual to each [...] Read more.
Difference schemes that approximate dynamic systems are considered discrete models of the same phenomena that are described by continuous dynamic systems. Difference schemes with t-symmetry and midpoint and trapezoid schemes are considered. It is shown that these schemes are dual to each other, and, from this fact, we derive theorems on the inheritance of quadratic integrals by these schemes (Cooper’s theorem and its dual theorem on the trapezoidal scheme). Using examples of nonlinear oscillators, it is shown that these schemes poses challenges for theoretical research and practical application due to the problem of extra roots: these schemes do not allow one to unambiguously determine the final values from the initial values and vice versa. Therefore, we consider difference schemes in which the transitions from layer to layer in time are carried out using birational transformations (Cremona transformations). Such schemes are called reversible. It is shown that reversible schemes with t-symmetry can be easily constructed for any dynamical system with a quadratic right-hand side. As an example of such a dynamic system, a top fixed at its center of gravity is considered in detail. In this case, the discrete theory repeats the continuous theory completely: (1) the points of the approximate solution lie on some elliptic curve, which at Δt0 turns into an integral curve; (2) the difference scheme can be represented using quadrature; and (3) the approximate solution can be represented using an elliptic function of a discrete argument. The last section considers the general case. The integral curves are replaced with closures of the orbits of the corresponding Cremona transformation as sets in the projective space over R. The problem of the dimension of this set is discussed. Full article
(This article belongs to the Section Dynamical Systems)
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<p>Approximate solution of system (<a href="#FD9-mathematics-12-00167" class="html-disp-formula">9</a>), found using the reversible scheme (<a href="#FD11-mathematics-12-00167" class="html-disp-formula">11</a>) at <span class="html-italic">A</span> = 1, <span class="html-italic">B</span> = 2, <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>. The figure shows a circle (red), which is an integral curve for <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, and an ellipse (green), on which the points of the approximate solution lie.</p>
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<p>Change of quadrature increment (<a href="#FD19-mathematics-12-00167" class="html-disp-formula">19</a>) at <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>B</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mi>C</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; initial data <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mi>q</mi> <mo>=</mo> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; and step <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="false"> <mfrac> <mn>1</mn> <mn>10</mn> </mfrac> </mstyle> </mrow> </semantics></math>.</p>
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<p>Exact (red) and approximate (blue) solutions of Equation (<a href="#FD21-mathematics-12-00167" class="html-disp-formula">21</a>), satisfying the initial condition <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Approximate solution of a top whose center of gravity is shifted relative to the anchor point, found using a reversible scheme.</p>
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20 pages, 343 KiB  
Article
Cohomology and Deformations of Relative Rota–Baxter Operators on Lie-Yamaguti Algebras
by Jia Zhao and Yu Qiao
Mathematics 2024, 12(1), 166; https://doi.org/10.3390/math12010166 - 4 Jan 2024
Viewed by 885
Abstract
In this paper, we establish the cohomology of relative Rota–Baxter operators on Lie-Yamaguti algebras via the Yamaguti cohomology. Then, we use this type of cohomology to characterize deformations of relative Rota–Baxter operators on Lie-Yamaguti algebras. We show that if two linear or formal [...] Read more.
In this paper, we establish the cohomology of relative Rota–Baxter operators on Lie-Yamaguti algebras via the Yamaguti cohomology. Then, we use this type of cohomology to characterize deformations of relative Rota–Baxter operators on Lie-Yamaguti algebras. We show that if two linear or formal deformations of a relative Rota–Baxter operator are equivalent, then their infinitesimals are in the same cohomology class in the first cohomology group. Moreover, an order n deformation of a relative Rota–Baxter operator can be extended to an order n+1 deformation if and only if the obstruction class in the second cohomology group is trivial. Full article
16 pages, 2197 KiB  
Article
Shear Waves in an Elastic Plate with a Hole Resting on a Rough Base
by Anatoly Nikolaevich Filippov
Mathematics 2024, 12(1), 165; https://doi.org/10.3390/math12010165 - 4 Jan 2024
Viewed by 1132
Abstract
The article is devoted to the analytical and numerical study of the pattern of propagation and attenuation, due to Coulomb friction, of shear waves in an infinite elastic thin plate with a circular orifice of radius r0 lying on a rough base. [...] Read more.
The article is devoted to the analytical and numerical study of the pattern of propagation and attenuation, due to Coulomb friction, of shear waves in an infinite elastic thin plate with a circular orifice of radius r0 lying on a rough base. Considering the friction forces and their influence on the sample of wave propagation in extended rods or thin plates is important for calculating the stress–strain state in them and the size of the area of motion. An exact analytical solution of a nonlinear boundary value problem for tangential stresses and velocities is obtained in quadratures by the Laplace transform, with respect to time. It turned out that the complete exhaustion of the wave front of a strong rupture occurs at a finite distance r* from the center of the orifice, and an elementary formula is given for this distance (the case of tangential shock stresses suddenly applied to the orifice boundary is considered). For various ratios of the magnitude of the limiting friction force to the amplitude of the applied load, the stopping (trailing) wave fronts are calculated. After passing them, a state of static equilibrium between the elastic and friction forces with a nonlinear distribution of residual stresses is established in the region r0rr*. For the first time, a precise analytical solution was obtained for the boundary value problem of the propagation of elastic shear waves in an infinite isotropic space with a cylindrical cavity, when a tangential shock load is set on its surface. Full article
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<p>Plate with a circular orifice on the rough base: (<b>a</b>) Plate cross section. (<b>b</b>) View from above.</p>
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<p>The contour of integration in the complex plane <span class="html-italic">p</span>.</p>
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<p>Elastic shear wave stopping fronts (trailing wave fronts) depending on the limiting value of the friction force (parameter <span class="html-italic">α</span>) in the phase plane (<span class="html-italic">r</span>,<span class="html-italic">t</span>).</p>
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<p>The distribution of residual stresses <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>*</mo> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> after the passage of the stopping front of the elastic shear wave, depending on the limiting friction force (parameter α).</p>
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<p>Distribution of accelerations <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>φ</mi> </msub> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> at the stopping front of the elastic shear wave depending on the limiting friction force (parameter <span class="html-italic">α</span>).</p>
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<p>The distribution of the dimensionless residual friction force <span class="html-italic">κ</span>(<span class="html-italic">r</span>) after passing the stopping front of the elastic shear wave as a function of the limiting friction force (parameter <span class="html-italic">α</span>).</p>
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21 pages, 682 KiB  
Article
GSRec: A Graph-Sequence Recommendation System Based on Reverse-Order Graph and User Embedding
by Xulin Ma, Jiajia Tan, Linan Zhu, Xiaoran Yan and Xiangjie Kong
Mathematics 2024, 12(1), 164; https://doi.org/10.3390/math12010164 - 4 Jan 2024
Viewed by 1139
Abstract
At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the [...] Read more.
At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do not pay attention to the spatial signals of the data, which will also affect the model’s accuracy. This paper proposes a graph sequence-based model called GSRec that combines Graph Convolutional Network (GCN) and Transformer to solve these problems. In the GCN part we designed a reverse-order graph, and in the Transformer part we introduced the user embedding. The reverse-order graph and the user embedding can make the combination of GCN and Transformer more efficient. Experiments on six datasets show that GSRec outperforms the current state-of-the-art (SOTA) models. Full article
(This article belongs to the Special Issue Applied Network Analysis and Data Science)
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<p>The architecture of the GSRec.</p>
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<p>The process of the adjacency matrix.</p>
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<p>Influence of the embedding dimension.</p>
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<p>Influence of the depth of the GCN layer.</p>
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<p>Influence of the number of Transformer blocks.</p>
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14 pages, 372 KiB  
Article
Distributed Interval Observers with Switching Topology Design for Cyber-Physical Systems
by Junchao Zhang, Jun Huang and Changjie Li
Mathematics 2024, 12(1), 163; https://doi.org/10.3390/math12010163 - 4 Jan 2024
Cited by 1 | Viewed by 795
Abstract
In this paper, the distributed interval estimation problem for networked Cyber-Physical systems suffering from both disturbances and noise is investigated. In the distributed interval observers, there are some connected interval observers built for the corresponding subsystems. Then, due to the communication burden in [...] Read more.
In this paper, the distributed interval estimation problem for networked Cyber-Physical systems suffering from both disturbances and noise is investigated. In the distributed interval observers, there are some connected interval observers built for the corresponding subsystems. Then, due to the communication burden in Cyber-Physical systems, we consider the case where the communication among distributed interval observers is switching topology. A novel approach that combines L methodology with reachable set analysis is proposed to design distributed interval observers. Finally, the performance of the proposed distributed interval observers with switching topology is verified through a simulation example. Full article
(This article belongs to the Special Issue Dynamical System and Stochastic Analysis)
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<p>Longitudinal axis system of UAV.</p>
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<p>Three switching communication topology of UAVs.</p>
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<p>The change in the switching signal of distributed interval observers.</p>
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<p>Angle of attack and interval estimates of UAVs.</p>
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<p>Pitch rate and interval estimates of UAVs.</p>
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<p>Observation error of angle of attack and pitch rate of UAVs.</p>
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12 pages, 251 KiB  
Article
Empirical-Likelihood-Based Inference for Partially Linear Models
by Haiyan Su and Linlin Chen
Mathematics 2024, 12(1), 162; https://doi.org/10.3390/math12010162 - 4 Jan 2024
Viewed by 720
Abstract
Partially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements. This study aims to establish statistical inference for the parametric component effects within these models, employing a [...] Read more.
Partially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements. This study aims to establish statistical inference for the parametric component effects within these models, employing a nonparametric empirical likelihood approach. The proposed method involves a projection step to eliminate the nuisance nonparametric component and utilizes an empirical-likelihood-based technique, along with the Bartlett correction, to enhance the coverage probability of the confidence interval for the parameter of interest. This method demonstrates robustness in handling normally and non-normally distributed errors. The proposed empirical likelihood ratio statistic converges to a limiting chi-square distribution under certain regulations. Simulation studies demonstrate that this method provides better inference in terms of coverage probabilities compared to the conventional normal-approximation-based method. The proposed method is illustrated by analyzing the Boston housing data from a real study. Full article
(This article belongs to the Special Issue Parametric and Nonparametric Statistics: From Theory to Applications)
21 pages, 10922 KiB  
Article
An Improved Rock Damage Characterization Method Based on the Shortest Travel Time Optimization with Active Acoustic Testing
by Jing Zhou, Lang Liu, Yuan Zhao, Mengbo Zhu, Ruofan Wang and Dengdeng Zhuang
Mathematics 2024, 12(1), 161; https://doi.org/10.3390/math12010161 - 4 Jan 2024
Cited by 2 | Viewed by 768
Abstract
Real-time evaluation of the damage location and level of rock mass is essential for preventing underground engineering disasters. However, the heterogeneity of rock mass, which results from the presence of layered rock media, faults, and pores, makes it difficult to characterize the damage [...] Read more.
Real-time evaluation of the damage location and level of rock mass is essential for preventing underground engineering disasters. However, the heterogeneity of rock mass, which results from the presence of layered rock media, faults, and pores, makes it difficult to characterize the damage evolution accurately in real time. To address this issue, an improved method for rock damage characterization is proposed. This method optimizes the solution of the global shortest acoustic wave propagation path in the medium and verifies it with layered and defective media models. Based on this, the relationship between the inversion results of the wave velocity field and the distribution of rock damage is established, thereby achieving quantitative characterization of rock damage distribution and degree. Thus, the improved method is more suitable for heterogeneous rock media. Finally, the proposed method was used to characterize the damage distribution evolution process of rock media during uniaxial compression experiments. The obtained results were compared and analyzed with digital speckle patterns, and the influencing factors during the use of the proposed method are discussed. Full article
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<p>The principle of the shortest path calculation using the Bellman–Ford algorithm. (<b>a</b>) is a schematic diagram of the straight line propagation path and the refraction propagation path. (<b>b</b>) is the Bellman Ford algorithm to calculate the shortest propagation path considering refraction.</p>
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<p>Propagation path with the shortest time of several common models obtained by the Bellman–Ford algorithm. (<b>a</b>–<b>c</b>) are the propagation path diagrams of the improved algorithm in homogeneous media, layered media and defective media respectively.</p>
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<p>The corresponding functional relationship between the damage and the measured wave velocity.</p>
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<p>Results of the wave velocity field distribution: (<b>a</b>) is obtained by the improved method; (<b>b</b>) is obtained by the initial method.</p>
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<p>The relative error rate of all the calculation cells.</p>
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<p>The relative error rate of each calculation unit when the number of measurement error paths is different.</p>
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<p>(<b>a</b>) is the experimental equipment and samples; (<b>b</b>) is one surface of the sample for industrial camera photography; (<b>c</b>) is the other surface of the sample for acoustic test and AE monitoring.</p>
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<p>(<b>a</b>) The loading force changes with time and the time of the acoustic wave test; (<b>b</b>) is each channel’s transmitted and received waveform. The red box is the excitation signal; the rest are the received signals.</p>
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<p>In the initial state and when the loading force is 30 kN, sensor 2 transmits acoustic waves, and sensor 8 receives waveforms.</p>
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<p>Parameter selection.</p>
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<p>Damage characterization results and digital speckle results with different loading forces.</p>
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<p>Damage characterization results and digital speckle results with different loading forces.</p>
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<p>The granite slab sample with a crack. (<b>a</b>) is the relative position of the sensor and the crack on the flat sample; (<b>b</b>) is the size of the crack and the granite slab sample.</p>
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<p>The emitting wave with frequencies of 200 kHz, 400 kHz, 600 kHz, 800 kHz, and 1 MHz, respectively.</p>
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<p>The spectrum analysis results of the waveform received by sensor #3. (<b>1</b>)–(<b>5</b>) are the spectrum analysis results of the waveform received by sensor #3 when the transmitted waveform was 200 kHz, 400 kHz, 600 kHz, 800 kHz, and 1 MHz.</p>
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<p>The different emitting waveforms.</p>
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<p>The spectrum analysis results of the receiving waveform. (<b>1</b>)–(<b>3</b>) are the spectrum diagrams of the received waveforms when the transmitted waveforms are half-cycle sine wave, rectangular wave, and triangular wave respectively.</p>
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<p>The waveforms received by No. 1 and No. 2 sensors with different frequencies. (<b>1</b>) to (<b>5</b>) represent the received waveforms when the transmitted waveform frequency is 200 kHz, 400 kHz, 600 kHz, 800 kHz, and 1000 kHz.</p>
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<p>The spectrograms of the waves with different source frequencies received by sensor 1 and sensor 2 after propagating in the sample with the defect.</p>
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<p>The spectrograms of the waves with different source frequencies received by sensor 1 and sensor 2 after propagating in the sample with the defect.</p>
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16 pages, 12663 KiB  
Article
Modeling Study of Factors Determining Efficacy of Biological Control of Adventive Weeds
by Yuri V. Tyutyunov, Vasily N. Govorukhin and Vyacheslav G. Tsybulin
Mathematics 2024, 12(1), 160; https://doi.org/10.3390/math12010160 - 4 Jan 2024
Viewed by 881
Abstract
We model the spatiotemporal dynamics of a community consisting of competing weed and cultivated plant species and a population of specialized phytophagous insects used as the weed biocontrol agent. The model is formulated as a PDE system of taxis–diffusion–reaction type and computer-implemented for [...] Read more.
We model the spatiotemporal dynamics of a community consisting of competing weed and cultivated plant species and a population of specialized phytophagous insects used as the weed biocontrol agent. The model is formulated as a PDE system of taxis–diffusion–reaction type and computer-implemented for one-dimensional and two-dimensional cases of spatial habitat for the Neumann zero-flux boundary condition. In order to discretize the original continuous system, we applied the method of lines. The obtained system of ODEs is integrated using the Runge–Kutta method with a variable time step and control of the integration accuracy. The numerical simulations provide insights into the mechanism of formation of solitary population waves (SPWs) of the phytophage, revealing the factors that determine the efficacy of combined application of the phytophagous insect (classical biological method) and cultivated plant (phytocenotic method) to suppress weed foci. In particular, the presented results illustrate the stabilizing action of cultivated plants, which fix the SPW effect by occupying the free area behind the wave front so that the weed remains suppressed in the absence of a phytophage. Full article
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<p>Dynamics in homogeneous case, <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.0004</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.00050729</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math>.</p>
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<p>Basins of attraction for equilibria <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (red) and <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (blue) computed for three different initial values <math display="inline"><semantics> <msub> <mi>Z</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Basins of attraction for stationary states <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>P</mi> <mo>=</mo> <mi>Z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (red) and <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mi>Z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math> (blue) computed for <math display="inline"><semantics> <mrow> <msub> <mi>Z</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.0006</mn> </mrow> </semantics></math> without taxis (<b>left</b> panel) and with taxis: <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0.002</mn> </mrow> </semantics></math> (<b>central</b> panel); <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0.005</mn> </mrow> </semantics></math> (<b>right</b> panel).</p>
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<p>Spatiotemporal dynamics of the model, illustrating successful suppression of weed due to synergistic effect of the combined application of phytophagous insects and cultivated plant competing with the weed.</p>
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<p>Numerical simulation corresponding to scenario in which result of the phytophage SPW passage is not fixed by the cultivated plant competing with the weed.</p>
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<p>Numerical result obtained for the scenario, which envisages unsuccessful attempt to suppress the weed by cultivated plant without the use of phytophage.</p>
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<p>Stabilization of uniformity in space and periodicity in time dynamics; <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Stabilization of nonuniformity in space and periodicity in time dynamics; <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0.14</mn> </mrow> </semantics></math>.</p>
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<p>Stabilization of nonuniformity in space and nonperiodicity in time dynamics; <math display="inline"><semantics> <mrow> <mi>κ</mi> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math>.</p>
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<p>Phase portrait of the point model (<a href="#FD9-mathematics-12-00160" class="html-disp-formula">9</a>). Locally stable equilibria are represented by solid dots; unstable equlibria are represented by hollow dots. The dashed line is the separatrix, dividing the positive quadrant of the phase plane into basins of attraction of axial equlibria corresponding to exclusion of either weed or cultivated plant.</p>
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<p>Snapshots of spatial distribution of population densities at the indicated moments of time, computed with model (<a href="#FD1-mathematics-12-00160" class="html-disp-formula">1</a>)–(<a href="#FD6-mathematics-12-00160" class="html-disp-formula">6</a>) for hypothetical heterogeneous spatial domain <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mo>=</mo> <mn>4000</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">m</mi> <mo>×</mo> <mn>3000</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
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20 pages, 936 KiB  
Article
Tensor Conjugate Gradient Methods with Automatically Determination of Regularization Parameters for Ill-Posed Problems with t-Product
by Shi-Wei Wang, Guang-Xin Huang and Feng Yin
Mathematics 2024, 12(1), 159; https://doi.org/10.3390/math12010159 - 3 Jan 2024
Viewed by 782
Abstract
Ill-posed problems arise in many areas of science and engineering. Tikhonov is a usual regularization which replaces the original problem by a minimization problem with a fidelity term and a regularization term. In this paper, a tensor t-production structure preserved Conjugate-Gradient (tCG) method [...] Read more.
Ill-posed problems arise in many areas of science and engineering. Tikhonov is a usual regularization which replaces the original problem by a minimization problem with a fidelity term and a regularization term. In this paper, a tensor t-production structure preserved Conjugate-Gradient (tCG) method is presented to solve the regularization minimization problem. We provide a truncated version of regularization parameters for the tCG method and a preprocessed version of the tCG method. The discrepancy principle is used to automatically determine the regularization parameter. Several examples on image and video recover are given to show the effectiveness of the proposed methods by comparing them with some previous algorithms. Full article
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<p>(<b>a</b>) Frontal slices <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mrow> <mo>(</mo> <mo>:</mo> <mo>,</mo> <mo>:</mo> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> </msub> </semantics></math>, (<b>b</b>) lateral slices <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mrow> <mo>(</mo> <mo>:</mo> <mo>,</mo> <mi>j</mi> <mo>,</mo> <mo>:</mo> <mo>)</mo> </mrow> </msub> </semantics></math> and (<b>c</b>) tube fibers <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <mo>:</mo> <mo>)</mo> </mrow> </msub> </semantics></math>.</p>
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<p>Twist squeeze.</p>
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<p>Example 1: Comparison of convergence between (<b>a</b>) relative errors verus the iteration number <span class="html-italic">k</span> and (<b>b</b>) relative errors verus the CPU time for the auto-tCG, auto-ttCG and auto-ttpCG methods with the noise level <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Example 1: (<b>a</b>) The original image and (<b>b</b>) the blurred and noised image, reconstructed images by (<b>c</b>) the tCG method (SNR = 12.21, CPU = 9.87), (<b>d</b>) the A-tCG-FFT method (SNR = 18.63, CPU = 88.02), (<b>e</b>) the A-CGLS-FFT method (SNR = 18.63, CPU = 82.23), (<b>f</b>) the auto-tCG method (SNR = 22.36, CPU = 109.87), (<b>g</b>) the auto-ttCG method (SNR = 22.41, CPU = 80.93) and (<b>h</b>) the auto-ttpCG method (SNR = 22.48, CPU = 33.98) according to the noise level <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> in <a href="#mathematics-12-00159-t002" class="html-table">Table 2</a>.</p>
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<p>Example 2: (<b>a</b>) The original image <span class="html-italic">Lena</span>, (<b>b</b>) the blurred and noised image and reconstructed images by (<b>c</b>) the tCG method, (<b>d</b>) the A-tCG-FFT method, (<b>e</b>) the A-CGLS-FFT method, (<b>f</b>) the auto-tCG method, (<b>g</b>) the auto-ttCG and (<b>h</b>) the auto-ttpCG method according to the noise level <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> in <a href="#mathematics-12-00159-t003" class="html-table">Table 3</a>.</p>
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<p>Example 2: Comparison of convergence between (<b>a</b>) relative errors verus the iteration number <span class="html-italic">k</span> and (<b>b</b>) relative errors verus the CPU time for the auto-tCG, auto-ttCG and auto-ttpCG methods with the noise level <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Example 3: Comparison of convergence between (<b>a</b>) relative errors verus the iteration number <span class="html-italic">k</span> and (<b>b</b>) relative errors verus the CPU time for the auto-tCG, auto-ttCG and auto-ttpCG methods with the noise level <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Example 3: (<b>a</b>) The second frame image of the original video, (<b>b</b>) the blurred and noisy image and recovered images by (<b>c</b>) the tCG method, (<b>d</b>) the A-tCG-FFT method, (<b>e</b>) the A-CGLS-FFT method, (<b>f</b>) the auto-tCG method, (<b>g</b>) the auto-ttCG and (<b>h</b>) the auto-ttpCG method according to the noise level <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> in <a href="#mathematics-12-00159-t004" class="html-table">Table 4</a>.</p>
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14 pages, 345 KiB  
Article
Feedback Stabilization Applied to Heart Rhythm Dynamics Using an Integro-Differential Method
by Asher Yahalom and Natalia Puzanov
Mathematics 2024, 12(1), 158; https://doi.org/10.3390/math12010158 - 3 Jan 2024
Viewed by 1167
Abstract
In this paper, we applied a chaos control method based on integro-differential equations for stabilization of an unstable cardiac rhythm, which is described by a variation of the modified Van der Pol equation. Chaos control with this method may be useful for stabilization [...] Read more.
In this paper, we applied a chaos control method based on integro-differential equations for stabilization of an unstable cardiac rhythm, which is described by a variation of the modified Van der Pol equation. Chaos control with this method may be useful for stabilization of irregular heartbeat using a small perturbation. This method differs from other stabilization strategies by the absence of adjustable parameters and the lack of rough approximations in determining control functions whose control parameters are fixed by the properties of the unstable system itself. Full article
(This article belongs to the Special Issue Nonlinear Stochastic Dynamics and Control and Its Applications)
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<p>(<b>a</b>) Chaotic pacemaker activity. Time dependence for <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>⩽</mo> <mi>t</mi> <mo>⩽</mo> <mn>500</mn> </mrow> </semantics></math>. (<b>b</b>) Chaotic pacemaker activity. Phase space. Time interval <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>⩽</mo> <mi>t</mi> <mo>⩽</mo> <mn>1000</mn> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Chaotic pacemaker activity. Time dependence for <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>⩽</mo> <mi>t</mi> <mo>⩽</mo> <mn>500</mn> </mrow> </semantics></math>. (<b>b</b>) Chaotic pacemaker activity. Phase space. Time interval <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>⩽</mo> <mi>t</mi> <mo>⩽</mo> <mn>1000</mn> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Numerical results of the stabilization of a modified Van der Pol Equation (<a href="#FD16-mathematics-12-00158" class="html-disp-formula">16</a>) for <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>6.5</mn> <mo>,</mo> <mi>β</mi> <mo>=</mo> <mn>6.3</mn> </mrow> </semantics></math>. (<b>b</b>) Numerical results of the stabilization of a modified Van der Pol Equation (<a href="#FD16-mathematics-12-00158" class="html-disp-formula">16</a>) for <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>10.5</mn> <mo>,</mo> <mi>β</mi> <mo>=</mo> <mn>10.3</mn> </mrow> </semantics></math>. (<b>c</b>) Numerical results of the stabilization of a modified Van der Pol Equation (<a href="#FD16-mathematics-12-00158" class="html-disp-formula">16</a>) for <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>20.5</mn> <mo>,</mo> <mi>β</mi> <mo>=</mo> <mn>20.3</mn> </mrow> </semantics></math>.</p>
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<p>The phase trajectory of the solution of System (<a href="#FD16-mathematics-12-00158" class="html-disp-formula">16</a>) in the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> plane; time interval is taken to be <math display="inline"><semantics> <mrow> <mn>1000</mn> <mo>⩽</mo> <mi>t</mi> <mo>⩽</mo> <mn>1500</mn> </mrow> </semantics></math>.</p>
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<p>Control function <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> compared to the stabilized process <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>,</mo> <mi>y</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Numerical results of the stabilization of a modified Van der Pol Equation (<a href="#FD16-mathematics-12-00158" class="html-disp-formula">16</a>) for <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
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<p>Numerical results of the stabilization of a modified Van der Pol Equation (<a href="#FD16-mathematics-12-00158" class="html-disp-formula">16</a>) for <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>A sample path of the Wiener process <math display="inline"><semantics> <mrow> <mi>w</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>A sample path of the random process <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Numerical results of the stabilization of a modified stochastic Van der Pol Equation (<a href="#FD25-mathematics-12-00158" class="html-disp-formula">25</a>).</p>
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<p>A sample path of the random process <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Numerical results of the stabilization of a modified stochastic Van der Pol Equation (<a href="#FD25-mathematics-12-00158" class="html-disp-formula">25</a>).</p>
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<p>A sample path of random process <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> related to the Ornstein–Uhlenbeck process.</p>
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<p>Numerical results of the stabilization of a modified stochastic Van der Pol Equation (<a href="#FD25-mathematics-12-00158" class="html-disp-formula">25</a>).</p>
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<p>A sample path of random process <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> with uniform distribution.</p>
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<p>Numerical results of the stabilization of a modified stochastic Van der Pol Equation (<a href="#FD25-mathematics-12-00158" class="html-disp-formula">25</a>).</p>
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11 pages, 258 KiB  
Article
On Enriched Suzuki Mappings in Hadamard Spaces
by Teodor Turcanu and Mihai Postolache
Mathematics 2024, 12(1), 157; https://doi.org/10.3390/math12010157 - 3 Jan 2024
Viewed by 839
Abstract
We define and study enriched Suzuki mappings in Hadamard spaces. The results obtained here are extending fundamental findings previously established in related research. The extension is realized with respect to at least two different aspects: the setting and the class of involved operators. [...] Read more.
We define and study enriched Suzuki mappings in Hadamard spaces. The results obtained here are extending fundamental findings previously established in related research. The extension is realized with respect to at least two different aspects: the setting and the class of involved operators. More accurately, Hilbert spaces are particular Hadamard spaces, while enriched Suzuki nonexpansive mappings are natural generalizations of enriched nonexpansive mappings. Next, enriched Suzuki nonexpansive mappings naturally contain Suzuki nonexpansive mappings in Hadamard spaces. Besides technical lemmas, the results of this paper deal with (1) the existence of fixed points for enriched Suzuki nonexpansive mappings and (2) Δ and strong (metric) convergence of Picard iterates of the α-averaged mapping, which are exactly Krasnoselskij iterates for the original mapping. Full article
(This article belongs to the Special Issue Fixed Point, Optimization, and Applications II)
17 pages, 459 KiB  
Article
Scale Mixture of Exponential Distribution with an Application
by Jorge A. Barahona, Yolanda M. Gómez, Emilio Gómez-Déniz, Osvaldo Venegas and Héctor W. Gómez
Mathematics 2024, 12(1), 156; https://doi.org/10.3390/math12010156 - 3 Jan 2024
Cited by 2 | Viewed by 903
Abstract
This article presents an extended distribution that builds upon the exponential distribution. This extension is based on a scale mixture between the exponential and beta distributions. By utilizing this approach, we obtain a distribution that offers increased flexibility in terms of the kurtosis [...] Read more.
This article presents an extended distribution that builds upon the exponential distribution. This extension is based on a scale mixture between the exponential and beta distributions. By utilizing this approach, we obtain a distribution that offers increased flexibility in terms of the kurtosis coefficient. We explore the general density, properties, moments, asymmetry, and kurtosis coefficients of this distribution. Statistical inference is performed using both the moments and maximum likelihood methods. To show the performance of this new model, it is applied to a real dataset with atypical observations. The results indicate that the new model outperforms two other extensions of the exponential distribution. Full article
(This article belongs to the Special Issue Computational Statistical Methods and Extreme Value Theory)
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<p>Densities SME(3, 1) (solid line), SME(3, 5) (dashed line), and E(3) (dotted line).</p>
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<p>Hazard function SME(1, 1) (solid line), SME(1, 5) (dashed line), SME(1,10) (dotted line), and SME(1,∞) = E(1) (horizontal dashed line).</p>
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<p>Plots of the asymmetry and kurtosis coefficients of the SME distribution.</p>
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<p>SME (solid line), GE (dashed line), and Weibull (dotted line).</p>
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<p>QQ-plots for repair time of 46 airborne communications receivers dataset: (<b>left</b>) Weibull model; (<b>center</b>) GE model; (<b>right</b>) SME model.</p>
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19 pages, 475 KiB  
Article
Generalized Halanay Inequalities and Asymptotic Behavior of Nonautonomous Neural Networks with Infinite Delays
by Dehao Ruan and Yao Lu
Mathematics 2024, 12(1), 155; https://doi.org/10.3390/math12010155 - 3 Jan 2024
Viewed by 735
Abstract
This paper focuses on the asymptotic behavior of nonautonomous neural networks with delays. We establish criteria for analyzing the asymptotic behavior of nonautonomous recurrent neural networks with delays by means of constructing some new generalized Halanay inequalities. We do not require to constructi [...] Read more.
This paper focuses on the asymptotic behavior of nonautonomous neural networks with delays. We establish criteria for analyzing the asymptotic behavior of nonautonomous recurrent neural networks with delays by means of constructing some new generalized Halanay inequalities. We do not require to constructi any complicated Lyapunov function and our results improve some existing works. Lastly, we provide some illustrative examples to demonstrate the effectiveness of the obtained results. Full article
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<p><math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>1</mn> </msub> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> of Example 1.</p>
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<p><math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> of Example 2 and their estimates.</p>
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<p><math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> of Example 2.</p>
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<p><math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">|</mo> </mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo>−</mo> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo stretchy="false">|</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">|</mo> </mrow> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo>−</mo> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo stretchy="false">|</mo> </mrow> </mrow> </semantics></math> of Example 2 and their estimates.</p>
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<p><math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> of Example 3 and their estimates.</p>
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<p><math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> of Example 3.</p>
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<p><math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">|</mo> </mrow> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo>−</mo> <msubsup> <mi>q</mi> <mn>1</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo stretchy="false">|</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">|</mo> </mrow> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo>−</mo> <msubsup> <mi>q</mi> <mn>2</mn> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mrow> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> <mo stretchy="false">|</mo> </mrow> </mrow> </semantics></math> of Example 3 and their estimates.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>1</mn> </msub> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>q</mi> <mn>2</mn> </msub> <mrow> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math> of Example 4 and their estimate.</p>
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11 pages, 935 KiB  
Article
On the Bessel Solution of Kepler’s Equation
by Riccardo Borghi
Mathematics 2024, 12(1), 154; https://doi.org/10.3390/math12010154 - 3 Jan 2024
Cited by 2 | Viewed by 973
Abstract
Since its introduction in 1650, Kepler’s equation has never ceased to fascinate mathematicians, scientists, and engineers. Over the course of five centuries, a large number of different solution strategies have been devised and implemented. Among them, the one originally proposed by J. L. [...] Read more.
Since its introduction in 1650, Kepler’s equation has never ceased to fascinate mathematicians, scientists, and engineers. Over the course of five centuries, a large number of different solution strategies have been devised and implemented. Among them, the one originally proposed by J. L. Lagrange and later by F. W. Bessel still continue to be a source of mathematical treasures. Here, the Bessel solution of the elliptic Kepler equation is explored from a new perspective offered by the theory of the Stieltjes series. In particular, it has been proven that a complex Kapteyn series obtained directly by the Bessel expansion is a Stieltjes series. This mathematical result, to the best of our knowledge, is a new integral representation of the KE solution. Some considerations on possible extensions of our results to more general classes of the Kapteyn series are also presented. Full article
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Figure 1

Figure 1
<p>The geometry of Kepler’s Equation.</p>
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<p>Behaviour of the function <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, according to Equation (<a href="#FD23-mathematics-12-00154" class="html-disp-formula">23</a>), for <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>10</mn> </mrow> </semantics></math> (solid curve), <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> (dashed curve), and <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (dotted curve).</p>
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<p>Behaviour of the relative error against <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>∈</mo> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mi>π</mi> <mo>)</mo> </mrow> </semantics></math>. In the above experiments, the “exact value” of the KE solution was evaluated by solving Equation (<a href="#FD1-mathematics-12-00154" class="html-disp-formula">1</a>) via Mathematica’s native command <tt>FindRoot</tt> with the parameter <tt>WorkingPrecision</tt> set to 50 and the initial guess of <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <mi>π</mi> </mrow> </semantics></math>. Function <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϵ</mi> <mo>;</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> was evaluated by implementing Equation (<a href="#FD36-mathematics-12-00154" class="html-disp-formula">36</a>) through the native Mathematica command <tt>NIntegrate</tt> with different degrees of accuracy, measured by the parameter <tt>WorkingPrecision</tt>, which was set to 10 (<b>a</b>), 15 (<b>b</b>), 20 (<b>c</b>), and  25 (<b>d</b>).</p>
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<p>The same as in <a href="#mathematics-12-00154-f003" class="html-fig">Figure 3</a>, but for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>∈</mo> <mfenced separators="" open="[" close="]"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>1</mn> <mn>1000</mn> </mfrac> </mstyle> <mo>,</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>1</mn> <mn>10</mn> </mfrac> </mstyle> </mfenced> </mrow> </semantics></math>. Note that now the function <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϵ</mi> <mo>;</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> is thought of as the imaginary part of <math display="inline"><semantics> <mrow> <mi mathvariant="double-struck">S</mi> <mo>(</mo> <mi>ϵ</mi> <mo>;</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math>, which is computed, similarly to that in <a href="#mathematics-12-00154-t001" class="html-table">Table 1</a>, via the Weniger <math display="inline"><semantics> <mi>δ</mi> </semantics></math>-transformation with an order of 20 (black circles), 30 (open circles), 40 (open squares), and 50 (black squares).</p>
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22 pages, 18365 KiB  
Article
Instance Segmentation Frustum–PointPillars: A Lightweight Fusion Algorithm for Camera–LiDAR Perception in Autonomous Driving
by Yongsheng Wang, Xiaobo Han, Xiaoxu Wei and Jie Luo
Mathematics 2024, 12(1), 153; https://doi.org/10.3390/math12010153 - 3 Jan 2024
Cited by 2 | Viewed by 1387
Abstract
The fusion of camera and LiDAR perception has become a research focal point in the autonomous driving field. Existing image–point cloud fusion algorithms are overly complex, and processing large amounts of 3D LiDAR point cloud data requires high computational power, which poses challenges [...] Read more.
The fusion of camera and LiDAR perception has become a research focal point in the autonomous driving field. Existing image–point cloud fusion algorithms are overly complex, and processing large amounts of 3D LiDAR point cloud data requires high computational power, which poses challenges for practical applications. To overcome the above problems, herein, we propose an Instance Segmentation Frustum (ISF)–PointPillars method. Within the framework of our method, input data are derived from both a camera and LiDAR. RGB images are processed using an enhanced 2D object detection network based on YOLOv8, thereby yielding rectangular bounding boxes and edge contours of the objects present within the scenes. Subsequently, the rectangular boxes are extended into 3D space as frustums, and the 3D points located outside them are removed. Afterward, the 2D edge contours are also extended to frustums to filter the remaining points from the preceding stage. Finally, the retained points are sent to our improved 3D object detection network based on PointPillars, and this network infers crucial information, such as object category, scale, and spatial position. In pursuit of a lightweight model, we incorporate attention modules into the 2D detector, thereby refining the focus on essential features, minimizing redundant computations, and enhancing model accuracy and efficiency. Moreover, the point filtering algorithm substantially diminishes the volume of point cloud data while concurrently reducing their dimensionality, thereby ultimately achieving lightweight 3D data. Through comparative experiments on the KITTI dataset, our method outperforms traditional approaches, achieving an average precision (AP) of 88.94% and bird’s-eye view (BEV) accuracy of 90.89% in car detection. Full article
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Figure 1
<p>The overall process of the ISF–PointPillars algorithm is shown above. In the subfigures of Frustum Filtered Pointcloud, different colors of the point cloud represent different z-axis heights. In the subfigures of the Original Pointcloud and the Result, different colors of the point cloud represent different density.</p>
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<p>This figure illustrates the extension of an instance segmentation edge contour into a frustum in 3D space. Only the points within the frustum are preserved, thus leading to a significant reduction in point count and consequently decreasing the computational load in subsequent steps of the pipeline. The green rays depicted in the above image are referred to as visual rays, thereby representing straight lines projected from a point on the object to the camera along the line of sight. All rays intersect at the origin of the camera coordinate system.</p>
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<p>The ray originating from point A intersects with the edge at one point. Following the ray casting method, point A is determined to be inside the frustum. Similarly, for point D, the number of intersection points is 2, which is an even number, thereby leading to the conclusion that point D is outside the frustum. The judgment method for points B and C are analogous to those described above.</p>
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<p>Visualization of point cloud filtering. (<b>a</b>,<b>b</b>) correspond to point clouds from two different perspectives of the same road scene. The different colors of the point clouds reflect their z-axis height.</p>
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<p>Structural block diagram of EMA attention mechanism.</p>
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<p>The C2F (the faster version of CSP bottleneck with two convolutions) module and FasterNet with EMA (FE) module are integrated into the network architecture of YOLOv8 to form a new module called C2F-FE.</p>
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<p>The slim-neck architecture is implemented to reshape the head of YOLOv8, wherein the VoVGSCSP module replaces the original C2F module, and GSConv substitutes the former Conv layer. The layer indices on the left side of the diagram correspond to the outputs of different backbone layers. Serial numbers 11 to 21 represent the Layer numbers of the neural network.</p>
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<p>Pillar-based feature extraction network. The different colors of the point cloud in the image reflect its z-axis height.</p>
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<p>BEV results. A substantial reduction in the quantity of point cloud data was achieved, with foreground points being well preserved, while background points were largely eliminated. The green frames represent the ground truth labels of the obstacles, and the red frames are the results predicted by our algorithm.</p>
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<p>(<b>1</b>) Raw point cloud; (<b>2</b>) point cloud filtered using the Gaussian method; (<b>3</b>) point cloud filtered using our method. Different colors in (<b>1</b>) represent the point cloud density. The different colors in (<b>2</b>) and (<b>3</b>) reflect the height of the point cloud along the z-axis.</p>
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<p>Comparison of Gaussian distance-based method (<b>a</b>) and instance segmentation-based method (<b>b</b>).</p>
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<p>Optimization of YOLOv8 instance segmentation. Column (<b>a</b>) is the instance segmentation results of YOLOv8. It can be found that the ground was not completely removed. Column (<b>b</b>) displays the instance segmentation detection result utilized in our study, thus showing improved pixel-level separation of the ground.</p>
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<p>The entire algorithmic process visualized in different stages. (<b>a</b>–<b>c</b>) Three distinct road scenarios. From (1) to (7) are the visualizations of the output data at each algorithm stage. The green rays in (2) and (5) represent the line of sight. They all intersect at one point, which is the position of the camera. The red boxes in (2) represent the frustums generated by extending the 2D rectangular boxes of obstacles into the 3D space. The red boxes in (5) represent the frustums generated by extending the 2D instance segmentation masks of obstacles into 3D space.</p>
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33 pages, 9935 KiB  
Article
Computer Model for an Intelligent Adjustment of Weather Conditions Based on Spatial Features for Soil Moisture Estimation
by Luis Pastor Sánchez-Fernández, Diego Alberto Flores-Carrillo and Luis Alejandro Sánchez-Pérez
Mathematics 2024, 12(1), 152; https://doi.org/10.3390/math12010152 - 2 Jan 2024
Cited by 1 | Viewed by 989
Abstract
In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the [...] Read more.
In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the weather conditions at the point where an estimate is made. Therefore, IWeCASF’s aim is to determine these weather conditions. The procedure begins measuring them at only one checkpoint, called the primary checkpoint. The model determines the weather conditions anywhere within a region through image processing algorithms and fuzzy inference systems. The results are compared with the measurement records and with a spatial interpolation method. The performance is similar to or better than interpolation, especially in the rain, where the model developed is more accurate due to the certainty of replication. Additionally, IWeCASF does not require more than one measurement point. Therefore, it is a more appropriate approach to complement the point estimation model for enabling a regional soil moisture estimation. Full article
(This article belongs to the Special Issue Data Analytics in Intelligent Systems)
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<p>Operation of a soil moisture regional estimation based on the point estimation model [<a href="#B35-mathematics-12-00152" class="html-bibr">35</a>].</p>
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<p>Scheme of a soil moisture regional estimation based on point estimates.</p>
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<p>Work overview.</p>
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<p>Feature matrix <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>M</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>(soil type) of the region of interest.</p>
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<p>Region of interest under decorrelation. (<b>a</b>) For obtaining grassland, tree-covered areas, and buildings. (<b>b</b>) For obtaining elevation and spatial configuration.</p>
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<p>(<b>a</b>) Landscape matrix <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (grassland). (<b>b</b>) Landscape matrix <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>F</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> (buildings).</p>
Full article ">Figure 6 Cont.
<p>(<b>a</b>) Landscape matrix <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (grassland). (<b>b</b>) Landscape matrix <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>F</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> (buildings).</p>
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<p>Eight neighborhoods of sector <math display="inline"><semantics> <mrow> <mi>s</mi> <mo stretchy="false">(</mo> <mn>19,24</mn> <mo stretchy="false">)</mo> </mrow> </semantics></math>.</p>
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<p>Fuzzy adjustment.</p>
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<p>Landscape adjustment.</p>
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<p>Error <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> comparison of weather conditions (rain): adjusted <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>C</mi> <mi>a</mi> </mrow> <mrow> <mn>2</mn> </mrow> <mrow> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> and interpolated <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>C</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> <mrow> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> at checkpoints <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>5</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>8</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Errors of weather conditions adjusted <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>C</mi> <mi>a</mi> </mrow> <mrow> <mi>i</mi> </mrow> <mrow> <mn>15</mn> </mrow> </msubsup> </mrow> </semantics></math> at checkpoint <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mn>15</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Error <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> of weather conditions adjusted <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>C</mi> <mi>a</mi> </mrow> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>5</mn> </mrow> <mrow> <mi>r</mi> </mrow> </msubsup> </mrow> </semantics></math> at checkpoints <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>17</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Membership functions of fuzzy weather conditions vector <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mover accent="true"> <mrow> <mi>C</mi> </mrow> <mo stretchy="false">~</mo> </mover> </mrow> <mrow> <mi>i</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msubsup> <mo stretchy="false">(</mo> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math>. (<b>b</b>) Output’s (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>r</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>) membership functions.</p>
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<p>Aggregation for output <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>r</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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20 pages, 798 KiB  
Article
The Synchronisation Problem of Chaotic Neural Networks Based on Saturation Impulsive Control and Intermittent Control
by Zhengran Cao, Chuandong Li and Man-Fai Leung
Mathematics 2024, 12(1), 151; https://doi.org/10.3390/math12010151 - 2 Jan 2024
Cited by 1 | Viewed by 983
Abstract
This paper primarily focuses on the chaos synchronisation analysis of neural networks (NNs) under a hybrid controller. Firstly, we design a suitable hybrid controller with saturated impulse control, combined with time-dependent intermittent control. Both controls are low-energy consumption and discrete, aligning well with [...] Read more.
This paper primarily focuses on the chaos synchronisation analysis of neural networks (NNs) under a hybrid controller. Firstly, we design a suitable hybrid controller with saturated impulse control, combined with time-dependent intermittent control. Both controls are low-energy consumption and discrete, aligning well with industrial development needs. Secondly, the saturation function in the chaotic neural network is addressed using the polyhedral representation method and the sector nonlinearity method, respectively. By integrating the Lyapunov stability theory, Jensen’s inequality, the mathematical induction method, and the inequality reduction technique, we establish suitable time-dependent Lyapunov generalised equations. This leads to the estimation of the domain of attraction and the derivation of local exponential stability conditions for the error system. The validity of the achieved theoretical criteria is eventually demonstrated through numerical experiment simulations. Full article
(This article belongs to the Special Issue Advances and Applications of Artificial Intelligence Technologies)
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Figure 1
<p>Driven system with initial value <math display="inline"><semantics> <mrow> <msub> <mi>ξ</mi> <mn>0</mn> </msub> <mo>=</mo> <mrow> <mo>[</mo> <mn>1.7</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mn>2.4</mn> <mo>,</mo> <mo>−</mo> <mn>3.3</mn> <mo>]</mo> </mrow> </mrow> </semantics></math> (<a href="#FD5-mathematics-12-00151" class="html-disp-formula">5</a>) Chaotic behaviour.</p>
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<p>The evolutionary trajectories with initial values of <math display="inline"><semantics> <mrow> <msub> <mi>ξ</mi> <mn>0</mn> </msub> <mo>=</mo> <mrow> <mo>[</mo> <mn>0.8</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mn>1.1</mn> <mo>,</mo> <mo>−</mo> <mn>0.76</mn> <mo>]</mo> </mrow> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>ξ</mi> <mn>1</mn> </msub> </semantics></math> evolutionary trajectory; (<b>b</b>) <math display="inline"><semantics> <msub> <mi>ξ</mi> <mn>2</mn> </msub> </semantics></math> evolutionary trajectory; (<b>c</b>) <math display="inline"><semantics> <msub> <mi>ξ</mi> <mn>3</mn> </msub> </semantics></math> evolutionary trajectory.</p>
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<p>Evolutionary trajectories of the error system in the controller (<a href="#FD7-mathematics-12-00151" class="html-disp-formula">7</a>): (<b>a</b>) Evolutionary trajectory of the error system with saturated impulse intermittent control; (<b>b</b>) Evolutionary trajectory of the error system with impulse intermittent control (<a href="#FD14-mathematics-12-00151" class="html-disp-formula">14</a>); (<b>c</b>) Evolutionary trajectory of the error system with varying saturation parameter impulse intermittent control. (<b>d</b>) Evolutionary trajectory of the error system (<a href="#FD14-mathematics-12-00151" class="html-disp-formula">14</a>) without impulse action.</p>
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<p>Theorem-1 and Theorem-2 Initial Value Conditional Estimates of the domains of attraction <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">E</mi> <mn>1</mn> </msub> <mrow> <mo>{</mo> <msub> <mi mathvariant="script">G</mi> <mn>1</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mn>2</mn> <mo>}</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">E</mi> <mn>2</mn> </msub> <mrow> <mo>{</mo> <msub> <mi mathvariant="script">G</mi> <mn>2</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mn>2</mn> <mo>}</mo> </mrow> </mrow> </semantics></math> where the red part represents the domain of attraction <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">E</mi> <mn>1</mn> </msub> <mrow> <mo>{</mo> <msub> <mi mathvariant="script">G</mi> <mn>1</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mn>2</mn> <mo>}</mo> </mrow> </mrow> </semantics></math>, and the blue part represents the domain of attraction <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">E</mi> <mn>2</mn> </msub> <mrow> <mo>{</mo> <msub> <mi mathvariant="script">G</mi> <mn>2</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <mn>2</mn> <mo>}</mo> </mrow> </mrow> </semantics></math>.</p>
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5 pages, 159 KiB  
Editorial
“Differential Equations of Mathematical Physics and Related Problems of Mechanics”—Editorial 2021–2023
by Hovik A. Matevossian
Mathematics 2024, 12(1), 150; https://doi.org/10.3390/math12010150 - 2 Jan 2024
Viewed by 853
Abstract
Based on the published papers in this Special Issue of the elite scientific journal Mathematics, we herein present the Editorial for “Differential Equations of Mathematical Physics and Related Problems of Mechanics”, the main topics of which are fundamental and applied research on [...] Read more.
Based on the published papers in this Special Issue of the elite scientific journal Mathematics, we herein present the Editorial for “Differential Equations of Mathematical Physics and Related Problems of Mechanics”, the main topics of which are fundamental and applied research on differential equations in mathematical physics and mechanics [...] Full article
22 pages, 306 KiB  
Article
Exact Results for the Distribution of Randomly Weighted Sums
by Thomas Hitchen and Saralees Nadarajah
Mathematics 2024, 12(1), 149; https://doi.org/10.3390/math12010149 - 2 Jan 2024
Viewed by 732
Abstract
Dependent random variables play a crucial role in various fields, from finance and statistics to engineering and environmental sciences. Often, interest lies in understanding the aggregate sum of a collection of dependent variables with random weights. In this paper, we provide a comprehensive [...] Read more.
Dependent random variables play a crucial role in various fields, from finance and statistics to engineering and environmental sciences. Often, interest lies in understanding the aggregate sum of a collection of dependent variables with random weights. In this paper, we provide a comprehensive study of the distribution of the aggregate sum with random weights. Expressions derived include those for the cumulative distribution function, probability density function, conditional expectation, moment generating function, characteristic function, cumulant generating function, moments, variance, skewness, kurtosis, cumulants, value at risk and the expected shortfall. Real data applications are discussed. Full article
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<p>Fitting quantile function of <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <msub> <mi>W</mi> <mn>1</mn> </msub> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>+</mo> <mo>⋯</mo> <mo>+</mo> <msub> <mi>W</mi> <mn>5</mn> </msub> <msub> <mi>X</mi> <mn>5</mn> </msub> </mrow> </semantics></math> versus <span class="html-italic">p</span>.</p>
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<p>Fitting quantile function of <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>=</mo> <msub> <mi>W</mi> <mn>1</mn> </msub> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>+</mo> <mo>⋯</mo> <mo>+</mo> <msub> <mi>W</mi> <mn>7</mn> </msub> <msub> <mi>X</mi> <mn>7</mn> </msub> </mrow> </semantics></math> versus <span class="html-italic">p</span>.</p>
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29 pages, 5953 KiB  
Article
Analysis of Transmission System Stability with Distribution Generation Supplying Induction Motor Loads
by Minal S. Salunke, Ramesh S. Karnik, Angadi B. Raju and Vinayak N. Gaitonde
Mathematics 2024, 12(1), 148; https://doi.org/10.3390/math12010148 - 2 Jan 2024
Viewed by 939
Abstract
A distributed-power-generating source (DPGS) is intended to locally supply the increased power demand at a load bus. When applied in small amounts, a DPGS offers many technical and economic benefits. However, with large DPGS penetrations, the stability of the transmission system becomes a [...] Read more.
A distributed-power-generating source (DPGS) is intended to locally supply the increased power demand at a load bus. When applied in small amounts, a DPGS offers many technical and economic benefits. However, with large DPGS penetrations, the stability of the transmission system becomes a significant issue. This paper investigates the stability of a transmission system equipped with a DPGS at load centres supplying power to both a constant power (CP) and induction motor (IM) load. The DPGSs considered in the present study are microturbine and diesel turbine power generators (MTGS and DTGS), both interfaced with synchronous generators. The influence of an IM load supplied by the DPGS on small-signal stability is studied by a critical damping ratio analysis. On the other hand, time-domain indicators of the transient response following a short circuit are employed in the analysis. Further, a variance analysis test (VAT) is performed to determine the contribution of IM and CP loads on the system stability. The study revealed that large penetration levels of IM loads significantly affect the stability and depend on the kind of DPGS technology used. Full article
(This article belongs to the Special Issue Modeling, Simulation, and Analysis of Electrical Power Systems)
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<p>Distributed-power-generating system model.</p>
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<p>General block–diagram of MTGS model.</p>
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<p>Transfer function model of MTGS.</p>
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<p>General block–diagram of DTGS model.</p>
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<p>Simplified transfer function model of DTGS.</p>
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<p>Methodology of stability simulation in Matlab/Simulink environment.</p>
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<p>15–Bus test transmission system with interfaced DPGS.</p>
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<p>Critical damping ratio variations: (<b>a</b>) Case-I (All DTGS); (<b>b</b>) Case-II (All MTGS).</p>
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<p>Small–signal response for Case-I (All DTGS) (<b>a</b>) MSG; (<b>b</b>) DTGS; (<b>c</b>) IM.</p>
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<p>Small–signal response for Case-II (All MTGS) (<b>a</b>) MSG; (<b>b</b>) MTGS; (<b>c</b>) IM.</p>
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<p>Mean response plots: (<b>a</b>) Case-I (All DTGS); (<b>b</b>) Case-II (All MTGS).</p>
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<p>Case-I: Slip deviation response for a 3-phase self-clearing fault of 200 ms duration: (<b>a</b>) MSG; (<b>b</b>) DTGS; (<b>c</b>) IM.</p>
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<p>Time response specifications as per <span class="html-italic">L</span><sub>25</sub> OA: (<b>a</b>) <span class="html-italic">MSD</span> of MSG; (<b>b</b>) <span class="html-italic">MSD</span> of DTGS; (<b>c</b>) <span class="html-italic">ST</span> of MSG; (<b>d</b>) <span class="html-italic">ST</span> of DTGS.</p>
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<p>Case-II: Slip deviation response for a 3-phase self-clearing fault of 200 ms duration: (<b>a</b>) MSG; (<b>b</b>) MTGS; (<b>c</b>) IM.</p>
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<p>Time response specifications as per <span class="html-italic">L<sub>2</sub></span><sub>5</sub> <span class="html-italic">OA</span>: (<b>a</b>) <span class="html-italic">MSD</span> of MSG; (<b>b</b>) <span class="html-italic">MSD</span> of MTGS; (<b>c</b>) <span class="html-italic">ST</span> of MSG; (<b>d</b>) <span class="html-italic">ST</span> of MTGS.</p>
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<p>Fault response of the system with all MTGS (<b>a</b>) Slip deviation of MSG<sub>2</sub> and MTGS<sub>3</sub> (<b>b</b>) Slip deviation of IM<sub>3</sub> (<b>c</b>) Terminal voltage at Bus 9 (<b>d</b>) Real power generated by MTGS<sub>3</sub> and real power drawn by IM<sub>3</sub>.</p>
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27 pages, 11944 KiB  
Article
The Co-Processing Combustion Characteristics of Municipal Sludge within an Industrial Cement Decomposition Furnace via Computational Fluid Dynamics
by Ling Zhu, Ya Mao, Kang Liu, Chengguang Tong, Quan Liu and Qiang Xie
Mathematics 2024, 12(1), 147; https://doi.org/10.3390/math12010147 - 2 Jan 2024
Viewed by 793
Abstract
Dealing with municipal sludge in an effective way is crucial for urban development and environmental protection. Co-processing the sludge by burning it in a decomposition furnace in the cement production line has been found to be a viable solution. This work aims to [...] Read more.
Dealing with municipal sludge in an effective way is crucial for urban development and environmental protection. Co-processing the sludge by burning it in a decomposition furnace in the cement production line has been found to be a viable solution. This work aims to analyze the effects of the co-disposal of municipal sludge on the decomposition reactions and NOx emissions in the decomposing furnaces. Specifically, a practical 6000 t/d decomposition furnace was taken as the research object. To achieve this, ANSYS FLUENT with a UDF (user-defined function) was applied to establish a numerical model coupling the limestone decomposition reaction, fuel combustion, and NOx generation and reduction reactions. The flow, temperature, and component field distributions within the furnace with no sludge were firstly simulated with this model. Compared with site test results, the model was validated. Then, with sludge involved, the structure and operation parameters of the decomposition furnace for the co-disposal of municipal sludge were investigated by simulating the flow field, temperature field, and component field distributions. Parametric studies were carried out in three perspectives, i.e., sludge mixing ratio, preheating furnace arrangement height, and sludge particle size. The results show that all three aspects have great importance in the discomposing process. A set of preferable values, including a sludge mixing ratio of 10%, preheating furnace height of 21.5 m, and sludge particle diameter of 1.0 mm, was obtained, which resulted in a raw material decomposition rate of 89.9% and a NO volume fraction of 251 ppm at the furnace outlet. Full article
(This article belongs to the Special Issue CFD Simulation of Heat Transfer and Applications)
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<p>Geometric modeling of decomposition furnace system.</p>
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<p>Decomposition furnace internal phase coupling relationship.</p>
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<p>Grids for decomposition furnace system and their independence check.</p>
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<p>The temperature distributions in the furnace.</p>
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<p>The NOx distributions inside the decomposition furnace.</p>
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<p>Volume fraction distributions of (<b>a</b>) <math display="inline"><semantics> <msub> <mi>O</mi> <mn>2</mn> </msub> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, (<b>c</b>) volatile component, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>a</mi> <mi>C</mi> <msub> <mi>O</mi> <mn>3</mn> </msub> </mrow> </semantics></math> in the decomposition furnace.</p>
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<p>The test instruments of (<b>a</b>) Multifunctional gas analyzer, (<b>b</b>) Single platinum rhodium thermocouple, (<b>c</b>) Automatic smoke tester, (<b>d</b>) Anti-clogging pitot tube and (<b>e</b>) the field site photo.</p>
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<p>Schematic diagram of sampling points at a cross-section.</p>
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<p>The temperature distributions under different sludge mixing ratios.</p>
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<p>Volume fractions under different sludge mixing ratios.</p>
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<p>Volume fractions under different sludge mixing ratios.</p>
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<p>The raw material decomposition rate and <span class="html-italic">NO</span> fraction at outlet under different sludge mixing ratios.</p>
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<p>Temperature distributions under different preheating furnace heights.</p>
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<p>Volume fractions under different preheating furnace heights.</p>
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<p>Volume fractions under different preheating furnace heights.</p>
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<p>Raw material decomposition rate and <span class="html-italic">NO</span> fraction at outlet under different preheating furnace heights.</p>
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<p>Temperature distributions under different sludge particle diameters.</p>
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<p>Volume fractions under different sludge particle diameters.</p>
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<p>Volume fractions under different sludge particle diameters.</p>
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<p>Raw material decomposition rate and <span class="html-italic">NO</span> fraction at outlet under different sludge diameters.</p>
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25 pages, 439 KiB  
Article
Merging Intuitionistic and De Morgan Logics
by Minghui Ma and Juntong Guo
Mathematics 2024, 12(1), 146; https://doi.org/10.3390/math12010146 - 2 Jan 2024
Viewed by 1226
Abstract
We introduce De Morgan Heyting logic for Heyting algebras with De Morgan negation (DH-algebras). The variety DH of all DH-algebras is congruence distributive. The lattice of all subvarieties of DH is distributive. We show the discrete dualities between De Morgan frames and DH-algebras. [...] Read more.
We introduce De Morgan Heyting logic for Heyting algebras with De Morgan negation (DH-algebras). The variety DH of all DH-algebras is congruence distributive. The lattice of all subvarieties of DH is distributive. We show the discrete dualities between De Morgan frames and DH-algebras. The Kripke completeness and finite approximability of some DH-logics are proven. Some conservativity of DH expansion of a Kripke complete superintuitionistic logic is shown by the construction of frame expansion. Finally, a cut-free terminating Gentzen sequent calculus for the DH-logic of De Morgan Boolean algebras is developed. Full article
(This article belongs to the Special Issue Algebraic Modal Logic and Proof Theory)
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<p>The relation between some DH-logics.</p>
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<p>Expansion of the I-frame <math display="inline"><semantics> <mi mathvariant="double-struck">F</mi> </semantics></math>.</p>
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32 pages, 4663 KiB  
Article
Influence of Homo- and Hetero-Junctions on the Propagation Characteristics of Radially Propagated Cylindrical Surface Acoustic Waves in a Piezoelectric Semiconductor Semi-Infinite Medium
by Xiao Guo, Yilin Wang, Chunyu Xu, Zibo Wei and Chenxi Ding
Mathematics 2024, 12(1), 145; https://doi.org/10.3390/math12010145 - 2 Jan 2024
Cited by 2 | Viewed by 839
Abstract
This paper theoretically investigates the influence of homo- and hetero-junctions on the propagation characteristics of radially propagated cylindrical surface acoustic waves in a piezoelectric semiconductor semi-infinite medium. First, the basic equations of the piezoelectric semiconductor semi-infinite medium are mathematically derived. Then, based on [...] Read more.
This paper theoretically investigates the influence of homo- and hetero-junctions on the propagation characteristics of radially propagated cylindrical surface acoustic waves in a piezoelectric semiconductor semi-infinite medium. First, the basic equations of the piezoelectric semiconductor semi-infinite medium are mathematically derived. Then, based on these basic equations and the transfer matrix method, two equivalent mathematical models are established concerning the propagation of radially propagated cylindrical surface acoustic waves in this piezoelectric semiconductor semi-infinite medium. Based on the surface and interface effect theory, the homo- or hetero-junction is theoretically treated as a two-dimensional electrically imperfect interface in the first mathematical model. To legitimately confirm the interface characteristic lengths that appear in the electrically imperfect interface conditions, the homo- or hetero-junction is equivalently treated as a functional gradient thin layer in the second mathematical model. Finally, based on these two mathematical models, the dispersion and attenuation curves of radially propagated cylindrical surface acoustic waves are numerically calculated to discuss the influence of the homo- and hetero-junctions on the dispersion and attenuation characteristics of radially propagated cylindrical surface acoustic waves. The interface characteristic lengths are legitimately confirmed through the comparison of dispersion and attenuation curves calculated using the two equivalent mathematical models. As piezoelectric semiconductor energy harvesters usually work under elastic deformation, the establishment of mathematical models and the revelation of physical mechanisms are both fundamental to the analysis and optimization of micro-scale surface acoustic wave resonators, energy harvesters, and acoustic wave amplification based on the propagation of surface acoustic waves. Full article
(This article belongs to the Special Issue Advances in Applied Mathematics, Mechanics and Engineering)
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<p>Schematic of a PSC semi−infinite medium.</p>
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<p>Schematic of the functional gradient thin layer divided into a finite number of thin layers.</p>
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<p>Distribution of steady charge carrier concentrations (<b>a</b>,<b>b</b>) and geometrical thicknesses (<b>c</b>–<b>e</b>) of the homo−junction with varying doping concentrations in the upper n−type ZnO/lower p−type ZnO semi−infinite medium.</p>
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<p>The dispersion curves (<b>a</b>) and attenuation curves (<b>b</b>) of radially propagated cylindrical SAW in the upper n−type ZnO/lower p−type ZnO semi−infinite medium with variation of the <math display="inline"><semantics> <mrow> <mi>r</mi> </mrow> </semantics></math> coordinate.</p>
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<p>Dispersion curves (<b>a</b>,<b>c</b>) and attenuation curves (<b>b</b>,<b>d</b>) of radially propagated cylindrical SAW in the upper n−type ZnO/lower p−type ZnO semi−infinite medium with varying doping concentrations.</p>
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<p>Relative changes in the radial wave speed (<b>a</b>,<b>c</b>) and the dimensionless attenuation coefficient (<b>b</b>,<b>d</b>) of radially propagated cylindrical SAW in the upper n−type ZnO/lower p−type ZnO semi−infinite medium with varying doping concentrations.</p>
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<p>Numerical comparations of the dispersion curves (<b>a</b>,<b>c</b>) and attenuation curves (<b>b</b>,<b>d</b>) of radially propagated cylindrical SAW in the upper n−type ZnO/lower p−type ZnO semi−infinite medium calculated by the two equivalent mathematical models.</p>
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<p>Distribution of steady charge carrier concentrations (<b>a</b>,<b>b</b>) and geometrical thicknesses (<b>c</b>–<b>e</b>) of the hetero−junction with varying doping concentrations in the upper n−type GaN/lower p−type ZnO semi−infinite medium.</p>
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<p>Dispersion curves (<b>a</b>) and attenuation curves (<b>b</b>) of radially propagated cylindrical SAW in the upper n−type GaN/lower p−type semi−infinite medium with varying doping concentrations.</p>
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<p>Relative changes of the radial wave speed (<b>a</b>) and the dimensionless attenuation coefficient (<b>b</b>) of radially propagated cylindrical SAW in the upper n−type GaN/lower p−type ZnO semi−infinite medium with varying doping concentrations.</p>
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<p>Numerical comparison of the dispersion curves (<b>a</b>) and attenuation curves (<b>b</b>) of radially propagated cylindrical SAW in the upper n−type GaN/lower p−type ZnO semi−infinite medium calculated by the two equivalent mathematical models.</p>
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0 pages, 288 KiB  
Article
Ricci Vector Fields Revisited
by Hanan Alohali, Sharief Deshmukh and Gabriel-Eduard Vîlcu
Mathematics 2024, 12(1), 144; https://doi.org/10.3390/math12010144 - 1 Jan 2024
Cited by 1 | Viewed by 849 | Correction
Abstract
We continue studying the σ-Ricci vector field u on a Riemannian manifold (Nm,g), which is not necessarily closed. A Riemannian manifold with Ricci operator T, a Coddazi-type tensor, is called a T-manifold. [...] Read more.
We continue studying the σ-Ricci vector field u on a Riemannian manifold (Nm,g), which is not necessarily closed. A Riemannian manifold with Ricci operator T, a Coddazi-type tensor, is called a T-manifold. In the first result of this paper, we show that a complete and simply connected T-manifold(Nm,g), m>1, of positive scalar curvature τ, admits a closed σ-Ricci vector field u such that the vector uσ is an eigenvector of T with eigenvalue τm1, if and only if it is isometric to the m-sphere Sαm. In the second result, we show that if a compact and connected T-manifold(Nm,g), m>2, admits a σ-Ricci vector field u with σ0 and is an eigenvector of a rough Laplace operator with the integral of the Ricci curvature Ricu,u that has a suitable lower bound, then (Nm,g) is isometric to the m-sphere Sαm, and the converse also holds. Finally, we show that a compact and connected Riemannian manifold (Nm,g) admits a σ-Ricci vector field u with σ as a nontrivial solution of the static perfect fluid equation, and the integral of the Ricci curvature Ricu,u has a lower bound depending on a positive constant α, if and only if (Nm,g) is isometric to the m-sphere Sαm. Full article
(This article belongs to the Special Issue Special (Pseudo-) Riemannian Manifolds)
9 pages, 253 KiB  
Article
An Approach to Multidimensional Discrete Generating Series
by Svetlana S. Akhtamova, Tom Cuchta and Alexander P. Lyapin
Mathematics 2024, 12(1), 143; https://doi.org/10.3390/math12010143 - 1 Jan 2024
Viewed by 2001
Abstract
We extend existing functional relationships for the discrete generating series associated with a single-variable linear polynomial coefficient difference equation to the multivariable case. Full article
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<p>Illustration of the sets <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>⩾</mo> <mi>m</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>⩽</mo> <mi>m</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>≱</mo> <mi>m</mi> </mrow> </semantics></math>.</p>
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30 pages, 15588 KiB  
Article
Machine Recognition of DDoS Attacks Using Statistical Parameters
by Juraj Smiesko, Pavel Segec and Martin Kontsek
Mathematics 2024, 12(1), 142; https://doi.org/10.3390/math12010142 - 31 Dec 2023
Viewed by 1257
Abstract
As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and [...] Read more.
As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic. Full article
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<p>Description of IP flow: (<b>a</b>) cumulative time, (<b>b</b>) increments in time.</p>
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<p>Compute windows: (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>c</mi> <mi>w</mi> <mo>|</mo> <mo>=</mo> <mn>10</mn> <mspace width="4pt"/> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>c</mi> <mi>w</mi> <mo>|</mo> <mo>=</mo> <mn>2000</mn> <mspace width="4pt"/> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math>.</p>
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<p>Compute windows pair: (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>c</mi> <mi>w</mi> <mo>|</mo> <mo>=</mo> <mn>10</mn> <mspace width="4pt"/> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math>, shift <math display="inline"><semantics> <mrow> <mn>1</mn> <mspace width="4pt"/> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>|</mo> <mi>c</mi> <mi>w</mi> <mo>|</mo> <mo>=</mo> <mn>2800</mn> <mspace width="4pt"/> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math>, shift <math display="inline"><semantics> <mrow> <mn>300</mn> <mspace width="4pt"/> <mi>t</mi> <mi>s</mi> </mrow> </semantics></math>.</p>
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<p>Increments of normal Distributed Denial of Service (DDoS) attack, (<b>a</b>) N8, (<b>b</b>) N6.</p>
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<p>Increments of normal Distributed Denial of Service (DDoS) attack, (<b>a</b>) N7 and (<b>b</b>) N2.</p>
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<p>Increments of special Distributed Denial of Service (DDoS) attack, (<b>a</b>) S5 and (<b>b</b>) S1.</p>
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<p>Increments of (<b>a</b>) Low-rate Distributed Denial of Service (DDoS) attack LR3 and (<b>b</b>) Problem attack P4.</p>
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<p>Attack N8. (<b>a</b>) Moving average and sample deviation, (<b>b</b>) moving sample variation.</p>
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<p>Attack LR3. (<b>a</b>) Moving average and sample deviation, (<b>b</b>) moving sample variation.</p>
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<p>Attack N8. (<b>a</b>) Moving kurtosis coefficient. (<b>b</b>) Moving skewness coefficient.</p>
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<p>Attack S1. (<b>a</b>) Moving kurtosis coefficient. (<b>b</b>) Moving skewness coefficient.</p>
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<p>Increments and moving Entropy, (<b>a</b>) record N8, (<b>b</b>) record N6.</p>
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<p>Hurst exponent. (<b>a</b>) Attack LR3. (<b>b</b>) Attack N8.</p>
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<p>Autoregression and correlation coefficients. (<b>a</b>) Attack LR3. (<b>b</b>) Attack N6.</p>
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<p>Kullback–Leibler divergence. (<b>a</b>) Attack S1. (<b>b</b>) Attack LR3.</p>
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<p>The upper limit of predicting the tunnel for incremets <math display="inline"><semantics> <msub> <mi>a</mi> <mi>t</mi> </msub> </semantics></math> of records: (<b>a</b>) N8, (<b>b</b>) S1.</p>
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<p>The 3<math display="inline"><semantics> <mi>σ</mi> </semantics></math>-Tunnel for Hurst exponent (without RS). (<b>a</b>) N2, (<b>b</b>) LR3.</p>
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<p>The 3D visualization of parameter values from <a href="#mathematics-12-00142-t002" class="html-table">Table 2</a>. (<b>a</b>) F (False), (<b>b</b>) R (Recognize) Attack.</p>
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<p>Variation, autoregressive, and correlation coefficients. (<b>a</b>) Record S1, (<b>b</b>) Record N2.</p>
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<p>Logical functions <math display="inline"><semantics> <mrow> <msub> <mi>Υ</mi> <mi>V</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>Υ</mi> <mi>c</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>Υ</mi> <mi>ϱ</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>a</b>) Record S1, (<b>b</b>) Record N2.</p>
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<p>Detection function <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>V</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. (<b>a</b>) Record S1, (<b>b</b>) Record N2.</p>
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<p>Incremets of flow <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, kurtosis coefficient <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, upper limits of <math display="inline"><semantics> <mrow> <mn>3</mn> <mi>σ</mi> </mrow> </semantics></math>-Tunnel of <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, and function <math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>K</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for (<b>a</b>) Record S1, (<b>b</b>) Record N7.</p>
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<p>Record S1. Exponential smoothing with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math> and Holt-exponential smoothing with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> <mo>,</mo> <mspace width="4pt"/> <mi>β</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>. (<b>a</b>) Kurtosis coefficient <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) Correlation coefficient <math display="inline"><semantics> <mrow> <mi>ϱ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Exponential smoothing <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math> and Holt-exponential smoothing <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> <mo>,</mo> <mspace width="4pt"/> <mi>β</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math> of autoregressive coefficient <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>: (<b>a</b>) record S1, (<b>b</b>) record LR3.</p>
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<p>Record P4, <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>σ</mi> </mrow> </semantics></math>-tunnel for: (<b>a</b>) correlation coefficient <math display="inline"><semantics> <mrow> <mi>ϱ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) kurtosis coeffiecient <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>Record P4, <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>σ</mi> </mrow> </semantics></math>-tunnel for (<b>a</b>) skewness coefficient <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>k</mi> <mi>w</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, (<b>b</b>) variation coefficient <math display="inline"><semantics> <mrow> <mi>V</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) The coefficients <math display="inline"><semantics> <mrow> <mi>V</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>k</mi> <mi>w</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>ϱ</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> for record P4, (<b>b</b>) multiplicative detection function <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>M</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for record P4.</p>
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15 pages, 5164 KiB  
Article
LIDAR Point Cloud Augmentation for Dusty Weather Based on a Physical Simulation
by Haojie Lian, Pengfei Sun, Zhuxuan Meng, Shengze Li, Peng Wang and Yilin Qu
Mathematics 2024, 12(1), 141; https://doi.org/10.3390/math12010141 - 31 Dec 2023
Viewed by 1564
Abstract
LIDAR is central to the perception systems of autonomous vehicles, but its performance is sensitive to adverse weather. An object detector trained by deep learning with the LIDAR point clouds in clear weather is not able to achieve satisfactory accuracy in adverse weather. [...] Read more.
LIDAR is central to the perception systems of autonomous vehicles, but its performance is sensitive to adverse weather. An object detector trained by deep learning with the LIDAR point clouds in clear weather is not able to achieve satisfactory accuracy in adverse weather. Considering the fact that collecting LIDAR data in adverse weather like dusty storms is a formidable task, we propose a novel data augmentation framework based on physical simulation. Our model takes into account finite laser pulse width and beam divergence. The discrete dusty particles are distributed randomly in the surrounding of LIDAR sensors. The attenuation effects of scatters are represented implicitly with extinction coefficients. The coincidentally returned echoes from multiple particles are evaluated by explicitly superimposing their power reflected from each particle. Based on the above model, the position and intensity of real point clouds collected from dusty weather can be modified. Numerical experiments are provided to demonstrate the effectiveness of the method. Full article
(This article belongs to the Section Mathematics and Computer Science)
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Figure 1

Figure 1
<p>LIDAR perception. (<b>a</b>) Under ideal conditions, LIDAR pulse do not experience scattering and intensity attenuation upon reaching the target. (<b>b</b>) When there are scattering media in the air, the LIDAR pulse is scattered, resulting in a attenuation in the target’s reflection intensity. It may even incorrectly identify scattering particles as targets.</p>
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<p>In the presence of scattering particles in the air, a fraction of the emitted pulse will be scattered, with some of the scattered pulses diverging away from the detector, some converging towards the detector, and a fraction of the emitted pulse penetrating through the scattering particles.</p>
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<p>The RGB image above represents the scene from the perspective of the camera. The lower left corner shows the original point cloud for this scene, while the lower right corner displays the simulated snowfall point cloud for the same scene.</p>
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<p>LIDAR sensor schematic with dual-beam configuration.</p>
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<p>Comparison of LIDAR simulations under different dusty weather conditions. For all dusty weather conditions, the half-power pulse width and beam divergence angle are set to 10 ns and 0.003 radians, respectively. All point cloud colors are encoded according to the Jet colormap rule, where blue represents high values and red represents low values. In the point cloud under clear weather conditions, we provide 3D bounding boxes of real objects as a reference.</p>
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<p>Comparison of different half-power pulse widths based on our blowing sand simulation with <math display="inline"><semantics> <mi>α</mi> </semantics></math> set to 0.01. The beam divergence angle is kept constant at 0.003 radians. All point cloud colors are still encoded according to the Jet colormap rules, and we provide 3D bounding boxes of real objects under clear weather conditions.</p>
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<p>Comparison of different beam divergence angles based on our blowing sand simulation with <math display="inline"><semantics> <mi>α</mi> </semantics></math> set to 0.01. The half-power pulse width is kept constant at 10ns. All point cloud colors are still encoded according to the Jet colormap rules, and we provide 3D bounding boxes of real objects under clear weather conditions.</p>
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<p>Comparison of different simulation methods. LISA [<a href="#B21-mathematics-12-00141" class="html-bibr">21</a>] adheres to all its default parameters, while Hahner’s simulation [<a href="#B19-mathematics-12-00141" class="html-bibr">19</a>] substitutes particle distribution with our blowing sand distribution, maintaining the rest according to the default parameters. Our results were based on blowing sand simulation with <math display="inline"><semantics> <mi>α</mi> </semantics></math>, half-power pulse width, and beam divergence angle set to 0.01, 10 ns and 0.003 radians, respectively. All point cloud colors are still encoded according to the Jet colormap rules, and we provide 3D bounding boxes of real objects under clear weather conditions.</p>
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25 pages, 10746 KiB  
Article
University Campus as a Complex Pedestrian Dynamic Network: A Case Study of Walkability Patterns at Texas Tech University
by Gisou Salkhi Khasraghi, Dimitri Volchenkov, Ali Nejat and Rodolfo Hernandez
Mathematics 2024, 12(1), 140; https://doi.org/10.3390/math12010140 - 31 Dec 2023
Cited by 1 | Viewed by 2051
Abstract
Statistical mechanics of walks defined on the spatial graphs of the city of Lubbock (10,421 nodes) and the Texas Tech University (TTU) campus pedestrian network (1466 nodes) are used for evaluating structural isolation and the integration of graph nodes, assessing their accessibility and [...] Read more.
Statistical mechanics of walks defined on the spatial graphs of the city of Lubbock (10,421 nodes) and the Texas Tech University (TTU) campus pedestrian network (1466 nodes) are used for evaluating structural isolation and the integration of graph nodes, assessing their accessibility and navigability in the graph, and predicting possible graph structural modifications driving the campus evolution. We present the betweenness and closeness maps of the campus, the first passage times to the different campus areas by isotropic and anisotropic random walks, as well as the first passage times under the conditions of traffic noise. We further show the isolation and integration indices of all areas on the campus, as well as their navigability and strive scores, and energy and fugacity scores. The TTU university campus, a large pedestrian zone located close to the historical city center of Lubbock, mediates between the historical city going downhill and its runaway sprawling body. Full article
(This article belongs to the Special Issue Dynamic Complex Networks: Models, Algorithms, and Applications)
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Figure 1

Figure 1
<p>First-attaining times to the nodes in a connected undirected graph corresponds to the stereographic projection of the vector <math display="inline"><semantics> <mrow> <msub> <mi>ϕ</mi> <mi>s</mi> </msub> <mo>∈</mo> <msubsup> <mi>S</mi> <mn>1</mn> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mi>N</mi> </mrow> </semantics></math> from the polar point <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mn>1</mn> </msub> </semantics></math> on a <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>N</mi> <mo>−</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>-dimensional unit sphere <math display="inline"><semantics> <msubsup> <mi>S</mi> <mn>1</mn> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </semantics></math> onto a projective space <math display="inline"><semantics> <mrow> <mi>P</mi> <msup> <mi mathvariant="double-struck">R</mi> <mrow> <mo>(</mo> <mi>N</mi> <mo>−</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </mrow> </semantics></math> scaled by the factor <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <msqrt> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <msub> <mi>λ</mi> <mi>s</mi> </msub> <mo>)</mo> </mrow> </msqrt> </mrow> </semantics></math>. Under the conditions of traffic noise and fluctuating transportation capacity of routes, the stationary distribution of random walks in a spatial graph, <math display="inline"><semantics> <mrow> <msub> <mi>π</mi> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mi>ϕ</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>i</mi> </mrow> <mn>2</mn> </msubsup> <mo>,</mo> </mrow> </semantics></math> corresponding to the polar point <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mn>1</mn> </msub> </semantics></math> is replaced by the nodes’ fugacity distribution <math display="inline"><semantics> <mi mathvariant="script">P</mi> </semantics></math> (<a href="#FD20-mathematics-12-00140" class="html-disp-formula">20</a>), corresponding to the point <math display="inline"><semantics> <msqrt> <mi mathvariant="script">P</mi> </msqrt> </semantics></math> that serves as a new point of stereographic projection.</p>
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<p>(<b>a</b>) The city is running away from Loop 289 bounding Lubbock before the mid-1960s. The location of the TTU campus on the city map is marked by the sign. (<b>b</b>) The graph nodes, representing spaces of movements found in the <span class="html-italic">OpenStreetMap</span> service for the city of Lubbock, are highlighted according to their components in Fiedler’s eigenvector, belonging to the <span class="html-italic">second</span> largest eigenvalue of the <math display="inline"><semantics> <mrow> <mi>Force</mi> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </semantics></math>-matrix defined in (<a href="#FD3-mathematics-12-00140" class="html-disp-formula">3</a>). The Fiedler eigenvector indicates the direction of the steepest descent of the entropic force <math display="inline"><semantics> <mrow> <mi>Force</mi> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </semantics></math> over the city spatial graph of Lubbock.</p>
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<p>The isolation and integration scores in the spatial graph of the city of Lubbock, TX. The location of the TTU campus is marked by the sign. (<b>a</b>) The isolation index (<a href="#FD7-mathematics-12-00140" class="html-disp-formula">7</a>) of nodes in the spatial network of Lubbock, TX, was obtained from the <span class="html-italic">OpenStreetMap</span> service. Red-hued neighborhoods are those that are isolated, and the colored bar shows the isolation index value on a decibel scale. Geographic coordinates are shown by the axis labels. (<b>b</b>) Nodes in Lubbock’s spatial graph are represented by their integration index (<a href="#FD8-mathematics-12-00140" class="html-disp-formula">8</a>). The well-integrated neighborhoods are shown in red on the colored bar, which represents the integration index value on a decibel scale.</p>
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<p>(<b>a</b>) The spatial morphology of the campus is described as a network of areas encompassing various pedestrian pathways, including sidewalks and the footprints of buildings. The location sign marks Memorial Circle, the most connected place on the TTU campus. (<b>b</b>) A tree representation of the obtained spatial graph rooted at Memorial Circle.</p>
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<p>The shortest-path centrality scores in TTU CPN. (<b>a</b>) Closeness centrality of nodes, i.e., the total geodesic distance from a given vertex to all other vertices in the network. (<b>b</b>) Betweenness centrality assesses the role of particular graph nodes in the global cohesiveness of the network.</p>
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<p>(<b>a</b>) The nodes of the TTU CPN spatial graph are colored according to the (logarithm) FPT values (<a href="#FD13-mathematics-12-00140" class="html-disp-formula">13</a>), calculated for the isotropic random walks <math display="inline"><semantics> <msup> <mi>W</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </semantics></math>. (<b>b</b>) The nodes of the TTU CPN spatial graph are colored according to the (logarithm) FPT values (<a href="#FD13-mathematics-12-00140" class="html-disp-formula">13</a>), calculated for the anisotropic random walks <math display="inline"><semantics> <msup> <mi>W</mi> <mrow> <mo>(</mo> <mo>∞</mo> <mo>)</mo> </mrow> </msup> </semantics></math>. (<b>c</b>) The nodes of the TTU CPN spatial graph are colored according to the (logarithm) FPT values (<a href="#FD22-mathematics-12-00140" class="html-disp-formula">22</a>), calculated for the isotropic random walks <math display="inline"><semantics> <msup> <mi>W</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </semantics></math> under the conditions of random traffic congestion in the network.</p>
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<p>The MDS representations for the CMT-distances calculated for the isotropic random walks under (<b>a</b>) the normal traffic operation (<a href="#FD14-mathematics-12-00140" class="html-disp-formula">14</a>) and (<b>b</b>) fluctuating transportation capacity (<a href="#FD23-mathematics-12-00140" class="html-disp-formula">23</a>).</p>
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<p>(<b>a</b>) The isolation index (<a href="#FD7-mathematics-12-00140" class="html-disp-formula">7</a>) calculated for the different places of movement in the TTU CPN. (<b>b</b>) The integration index (<a href="#FD8-mathematics-12-00140" class="html-disp-formula">8</a>) in the TTU CPN.</p>
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<p>The scatter plot shows the relationships between the isolation and integration indexes for the walking places of TTU CPN.</p>
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<p>(<b>a</b>) The navigability potential calculated for the isotropic random walks <math display="inline"><semantics> <msubsup> <mi>W</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> </semantics></math> defined in the TTU CPN graph. (<b>b</b>) The strive potential calculated for the isotropic random walks <math display="inline"><semantics> <msubsup> <mi>W</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> </semantics></math> defined in the TTU CPN graph.</p>
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<p>The relation between the amount of predictable and unpredictable information about the current position of random walkers with respect to (<b>a</b>) isotropic random walks defined by the transition matrix <math display="inline"><semantics> <msup> <mi>W</mi> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </semantics></math>; (<b>b</b>) anisotropic random walks defined by the transition matrix <math display="inline"><semantics> <msup> <mi>W</mi> <mrow> <mo>(</mo> <mo>∞</mo> <mo>)</mo> </mrow> </msup> </semantics></math>.</p>
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<p>(<b>a</b>) Energy scores of walking places in the TTU CPN. (<b>b</b>) Logarithm of fugacity scores of walking places in the TTU CPN.</p>
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19 pages, 346 KiB  
Article
On Extended Lr-Norm-Based Derivatives to Intuitionistic Fuzzy Sets
by A. S. Wungreiphi, Fokrul Alom Mazarbhuiya and Mohamed Shenify
Mathematics 2024, 12(1), 139; https://doi.org/10.3390/math12010139 - 31 Dec 2023
Viewed by 743
Abstract
The study of differential equation theory has come a long way, with applications in various fields. In 1961, Zygmund and Calderón introduced the notion of derivatives to metric Lr, which proved to be better in applications than approximate derivatives. However, most [...] Read more.
The study of differential equation theory has come a long way, with applications in various fields. In 1961, Zygmund and Calderón introduced the notion of derivatives to metric Lr, which proved to be better in applications than approximate derivatives. However, most of the studies available are on Fuzzy Set Theory. In view of this, intuitionistic fuzzy Lr-norm-based derivatives deserve study. In this study, the Lr-norm-based derivative for intuitionistic fuzzy number valued functions is introduced. Some of its basic properties are also discussed, along with numerical examples. The results obtained show that the proposed derivative is not dependent on the existence of the Hukuhara difference. Lastly, the Cauchy problem for the intuitionistic fuzzy differential equation is discussed. Full article
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