Search Results (735)

Addressing the challenge of radar detection of small targets under sea clutter, target detection methods based on a three-dimensional feature space have shown effectiveness. However, their application has revealed several problems, including high dependency on linear relationships between features for dimensionality reduction, unclear reduction objectives, and spatial divergence of target samples, which limit detection performance. To mitigate these challenges, we constructed a feature density distance metric employing copula functions to quantitatively describe the classification capability of multidimensional features to distinguish targets from sea clutter. On the basis of this, a lightweight nonlinear dimensionality reduction network utilizing a self-attention mechanism was developed, optimally re-expressing multidimensional features into a three-dimensional feature space. Additionally, a concave hull classifier using feature sample distance was proposed to mitigate the negative impact of target sample divergence in the feature space. Furthermore, multivariate autoregressive prediction was used to optimize features, reducing erroneous decisions caused by anomalous feature samples. Experimental results using the measured data from the SDRDSP public dataset demonstrated that the proposed detection method achieved a detection probability more than 4% higher than comparative methods under Sea State 5, was less affected by false alarm rates, and exhibited superior detection performance under different false alarm probabilities from 10⁻³ to 10⁻¹. Full article

The study presents a comparative analysis of climate data under two scenarios: a Gaussian copula marginal regression model for count time series data and a copula-based bivariate count time series model. These models, built after comprehensive simulations, offer adaptable autocorrelation structures considering the daily average temperature and humidity data observed at a regional airport in Mobile, AL. Full article

This research envisages a system composed of three subsystems connected in series. Each subsystem comprises three units connected in parallel. For the system to function, at least one unit per subsystem must remain operational. Unit failure is governed by an exponential distribution, while unit repair is governed by either a general distribution or a Gumbel–Hougaard family copula distribution. The primary goal of this research is to compare the overall performance of our system under these two different regimes for performing repairs. Laplace transforms and supplementary variable methods are employed in solving the system. Our metrics for evaluating system performance are the availability, reliability, mean time to failure, and cost. The second goal of this research is to showcase a strategy for reduction that enhances the overall efficiency and availability of our system. Full article

Fatigue load modeling is crucial for optimizing and assessing the lifespan of floating wind turbines. This study addresses the complex characteristics of fatigue loads on floating wind turbines under the combined effects of wind and waves. We propose a fatigue load modeling approach based on Vine copula theory and machine learning. Firstly, we establish an optimal joint probability distribution model using Vine copula theory for the four-dimensional random variables (wind speed, wave height, wave period, and wind direction), with model fit assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE). Secondly, representative wind and wave load conditions are determined using Monte Carlo sampling based on the established joint probability distribution model. Thirdly, fatigue load simulations are performed using the high-fidelity simulator OpenFAST to compute Damage Equivalent Load (DEL) values for critical components (blade root and tower base). Finally, utilizing measured wind and wave data from the Lianyungang Ocean Observatory in the East China Sea, simulation tests are conducted. We apply five commonly used machine learning models (Kriging, MLP, SVR, BNN, and RF) to develop DEL models for blade root and tower base. The results indicate that the RF model exhibits the smallest prediction error, not exceeding 3.9%, and demonstrates high accuracy, particularly in predicting flapwise fatigue loads at the blade root, achieving prediction accuracies of up to 99.97%. These findings underscore the effectiveness of our approach in accurately predicting fatigue loads under real-world conditions, which is essential for enhancing the reliability and efficiency of floating wind turbines. Full article

Distributed photovoltaic (PV) output exhibits strong stochasticity and weak adjustability. After being integrated with the network, its interaction with stochastic loads increases the difficulty of assessing the distribution network’s static voltage stability (SVS). In response to this issue, this article presents a probabilistic assessment method for SVS in a distribution network with distributed PV that considers the bilateral uncertainties and correlations on the source and load sides. The probabilistic models for the uncertain variables are established, with the correlation between stochastic variables described using the Copula function. The three-point estimate method (3PEM) based on the Nataf transformation is used to generate correlated samples. Continuous power flow (CPF) calculations are then performed on these samples to obtain the system’s critical voltage stability state. The distribution curves of critical voltage and load margin index (LMI) are fitted using Cornish-Fisher series. Finally, the utility function is introduced to establish the degree of risk of voltage instability under different scenarios, and the SVS assessment of the distribution network is completed. The IEEE 33-node distribution system is utilized to test the method presented, and the results across various scenarios highlight the method’s effectiveness. Full article

Environmental uncertainties present a significant challenge in the design of onboard photovoltaic hybrid power systems (PV-HPS), a pivotal decarbonization technology garnering widespread attention in the shipping industry. Neglecting environmental uncertainties associated with photovoltaic (PV) output and hull resistance can lead to suboptimal solutions. To address this issue, this paper proposes a stochastic optimization method for PV-HPS, aiming to minimize greenhouse gas (GHG) emissions and lifecycle costs. Copula functions are employed to establish joint distributions of uncertainties in solar irradiance, ambient temperature, significant wave height, and wave period. Monte Carlo simulation, the bi-bin method, and the multi-objective particle swarm optimization (MOPSO) algorithm are utilized for scenario generation, scenario reduction, and design space exploration. The efficacy of the proposed method is demonstrated through a case study involving an unmanned ship. Additionally, deterministic optimization and two partial stochastic optimizations are conducted to underscore the importance of simultaneously considering environmental uncertainties related to power sources and hull resistance. The results affirm the proposed approach’s capability to reduce GHG emissions and lifecycle costs. A sensitivity analysis of bin number is performed to investigate the tradeoff between optimality and computation time. Full article

China is the largest soybean importer and consumer in the world. Soybean oil is the most-consumed vegetable oil in China, while soybean meal is the most important protein feed raw material in China, which affects the costs of animal husbandry. Volatility in the international soybean market would generate risk spillovers to China’s soybean industrial chain. This paper analyzed the channel of risk spillover from the international soybean market to China’s soybean industry chain and the asymmetry of the risk spillover. The degree of risk spillover from the international soybean market to the Chinese soybean industry chain was measured by the Copula–CoVaR model. The moderating role of inventory and demand in asymmetric risk spillovers was analyzed by quantile regression. We draw the following conclusions: First, the international soybean market impacts China’s soybean industry chain through soybeans rather than soybean meal and oil. The price fluctuation of China soybean market is obviously lower than that of the international soybean market. Second, there are apparent asymmetric risk spillovers from the international soybean market to China’s soybean industry chain, especially the soybean meal market. Third, increasing the Chinese soybean inventory and growing demand could effectively prevent the downside risk spillover from international markets to China’s soybean market. This also explains the asymmetry of risk spillovers. The research enriches the research perspective on food security, and the analysis of risk spillover mechanisms provides a scientific basis for relevant companies to develop risk-management strategies. Full article

This paper presents new classes of strong fuzzy negations, fuzzy implications and Copulas. It begins by presenting two theorems with function classes involving the construction of strong fuzzy negations. These classes are based on a well-known equilibrium point theorem. After that, a construction of fuzzy implication is presented, which is not based on any negation. Finally, moving on to the area concerning copulas, we present proof about the third property of copulas. To conclude, we will present two original constructions of copulas. All the above constructions are motivated by a specific formula. For some specific conditions of the variables x, y and other conditions for the function f(x), the formula presented produces strict and strong fuzzy negations, fuzzy implications and copulas. Full article

For multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence. When preliminary data and likelihood analysis suggest asymmetric tail dependence, a method is proposed to improve extreme value inferences based on the joint lower and upper tails. A prior that uses previous information on tail dependence can be used in combination with the likelihood. With the combination of the prior and the likelihood (which in practice has some degree of misspecification) to obtain a tilted log-likelihood, inferences with suitably transformed parameters can be based on Bayesian computing methods or with numerical optimization of the tilted log-likelihood to obtain the posterior mode and Hessian at this mode. Full article

Understanding the propagation characteristics and driving factors from meteorological drought to hydrological drought is essential for alleviating drought and for early warning systems regarding drought. This study focused on the Weihe River basin (WRB) and its two subregions (the Jinghe River (JRB) and the middle reaches of the Weihe River (MWRB)), utilizing the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) to characterize meteorological and hydrological drought, respectively. Based on Copula theory and conditional probability, a quantification model for the propagation time (PT) of meteorological–hydrological drought was constructed. The dynamic characteristics of PT on annual and seasonal scales were explored. Additionally, the influences of different seasonal meteorological factors and underlying surface factors on the dynamic changes in PT were analyzed. The main conclusions were as follows: (1) The PT of meteorological–hydrological drought was characterized by faster propagation during the hot months (June–September) and slower propagation during the cold months (December to March of the following year); (2) Under the same level of hydrological drought, as the level of meteorological drought increases, the PT of the drought shortens. The propagation thresholds of meteorological to hydrological drought in the WRB, the JRB, and the MWRB are −0.69, −0.81, and −0.78, respectively. (3) In the dynamic changes in PT, the WRB showed a non-significant decrease; however, both the JRB and the MWRB exhibited a significant increase in PT across different drought levels. (4) The influence of the water and heat status during spring, summer, and winter on PT was more pronounced, while in autumn, the impact of the basin’s water storage and discharge status was more significant in the JRB and the MWRB. Full article

The intermittent nature of solar energy poses significant challenges to the integration of photovoltaic (PV) power generation into the electrical grid. Consequently, the precise forecasting of PV power output becomes essential for efficient real-time power system dispatch. To meet this demand, this paper proposes a deep learning model, the CA-Transformer, specifically designed for PV power output prediction. To overcome the shortcomings of traditional correlation coefficient methods in dealing with nonlinear relationships, this study utilizes the Copula function. This approach allows for a more flexible and accurate determination of correlations within time series data, enabling the selection of features that exhibit a high degree of correlation with PV power output. Given the unique data characteristics of PV power output, the proposed model employs a 1D-CNN model to identify local patterns and trends within the time series data. Simultaneously, it implements a cosine similarity attention mechanism to detect long-range dependencies within the time series. It then leverages a parallel structure of a 1D-CNN and a cosine similarity attention mechanism to capture patterns across varying time scales and integrate them. In order to show the effectiveness of the model proposed in this study, its prediction results were compared with those of other models (LSTM and Transformer). The experimental results demonstrate that our model outperforms in terms of PV power output prediction, thereby offering a robust tool for the intelligent management of PV power generation. Full article

We propose the “Marshall–Olkin Bivariate Weibull Model with Modified Singularity MOBW-$\mu$”, which focuses on bivariate distributions essential for reliability and survival analyses. Distributions such as the Marshall–Olkin bivariate exponential (MOBE) and the Marshall–Olkin bivariate Weibull (MOBW) are discussed. The MOBW-$\mu$ model is introduced, which incorporates a lag parameter $\mu$ in the singular part, and probabilistic properties such as the joint survival function, marginal density functions, and the bivariate hazard rate function are explored. In addition, aspects such as the correlation structure and survival copulation are addressed and we show that the correlation of the MOBW-$\mu$ is always lower than that of its copula, regardless of the parameters. The latter result implies that the MOBW-$\mu$ does not have the Lancaster’s phenomenon that explains that any nonlinear transformation of variables decreases the correlation in absolute value. The article concludes by presenting a robust theoretical framework applicable to various disciplines. Full article

The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models’ robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks—generator and discriminator—to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN’s sensitivity to training parameters and TVAE’s robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data. Full article

Background/Objectives: The forced social isolation implemented to prevent the spread of the COVID-19 virus was accompanied by a worsening of mental health, an increase in insomnia symptoms, and the emergence of ‘skin hunger’—an increased longing for personal touch. This study aimed to enhance our understanding of the interconnection between sleep, mental health, and the need for physical (NPC) and real-life social contact (NRL-SC). Methods: A total of 2827 adults participated in an online survey during the second COVID-19 lockdown. A Bayesian Gaussian copula graphical model (BGCGM) and a Bayesian-directed acyclic graph (DAG) were estimated, and mixed ANOVAs were carried out. Results: NPC with non-family members (t(2091) = 12.55, p < 0.001, d = 0.27) and relational lifestyle satisfaction (t(2089) = 13.62, p < 0.001, d = 0.30) were lower during the second lockdown than before the pandemic. In our BGCGM, there were weak positive edges between the need for PC and RL-SC on one hand and sleep and mental health on the other. Conclusions: During the second lockdown, people craved less physical contact with non-family members and were less satisfied with their relational lifestyle than before the pandemic. Individuals with a greater need for PC and RL-SC reported poorer mental health (i.e., worry, depression, and mental fatigue). Full article

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. Full article