Search Results (148)

The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery. Full article

Various metaheuristic algorithms inspired by nature have been designed to deal with a variety of practical optimization problems. As an excellent metaheuristic algorithm, the improved particle swarm optimization algorithm based on grouping (IPSO) has strong global search capabilities. However, it lacks a strong local search ability and the ability to solve constrained discrete optimization problems. This paper focuses on improving these two aspects of the IPSO algorithm. Based on IPSO, we propose an improved particle swarm optimization algorithm based on variable neighborhood search (VN-IPSO) and design a 0-1 integer programming solution with constraints. In the experiment, the performance of the VN-IPSO algorithm is fully tested and analyzed using 23 classic benchmark functions (continuous optimization), 6 knapsack problems (discrete optimization), and 10 CEC2017 composite functions (complex functions). The results show that the VN-IPSO algorithm wins 18 first places in the classic benchmark function test set, including 6 first places in the solutions for seven unimodal test functions, indicating a good local search ability. In solving the six knapsack problems, it wins four first places, demonstrating the effectiveness of the 0-1 integer programming constraint solution and the excellent solution ability of VN-IPSO in discrete optimization problems. In the test of 10 composite functions, VN-IPSO wins first place four times and ranks the first in the comprehensive ranking, demonstrating its excellent solving ability for complex functions. Full article

Unmanned aerial vehicles (UAVs) have recently been applied in several contexts due to their flexibility, mobility, and fast deployment. One of the essential aspects of multi-UAV systems is path planning, which autonomously determines paths for drones from starting points to destination points. However, UAVs face many obstacles in their routes, potentially causing loss or damage. Several heuristic approaches have been investigated to address collision avoidance. These approaches are generally applied in static environments where the environment is known in advance and paths are generated offline, making them unsuitable for unknown or dynamic environments. Additionally, limited flight times due to battery constraints pose another challenge in multi-UAV path planning. Reinforcement learning (RL) emerges as a promising candidate to generate collision-free paths for drones in dynamic environments due to its adaptability and generalization capabilities. In this study, we propose a framework to provide a novel solution for multi-UAV path planning in a 3D dynamic environment. The improved particle swarm optimization with reinforcement learning (IPSO-RL) framework is designed to tackle the multi-UAV path planning problem in a fully distributed and reactive manner. The framework integrates IPSO with deep RL to provide the drone with additional feedback and guidance to operate more sustainably. This integration incorporates a unique reward system that can adapt to various environments. Simulations demonstrate the effectiveness of the IPSO-RL approach, showing superior results in terms of collision avoidance, path length, and energy efficiency compared to other benchmarks. The results also illustrate that the proposed IPSO-RL framework can acquire a feasible and effective route successfully with minimum energy consumption in complicated environments. Full article

In urban building management, accurate prediction of building energy consumption is significant in realizing energy conservation and improving energy efficiency. Due to the complexity and variability of energy consumption data, existing prediction models face the challenge of difficult parameter selection, which directly affects their accuracy and application. To solve this problem, this study proposes an improved particle swarm algorithm (IPSO) for optimizing the parameters of the least squares support vector machine (LSSVM) and constructing an energy consumption prediction model based on IPSO-LSSVM. The model fully combines the advantages of LSSVM in terms of nonlinear fitting and generalization ability and uses the IPSO algorithm to adjust the parameters precisely. By analyzing the sample data characteristics and validating them on two different types of building energy consumption datasets, the results of the study show that, compared with traditional baseline models such as back-propagation neural networks (BP) and support vector regression (SVR), the model proposed in this study is more accurate and efficient in parameter selection and significantly reduces the prediction error rate. This improved approach not only improves the accuracy of building energy consumption prediction but also enhances the robustness and adaptability of the model, which provides reliable methodological support for the development of more effective energy-saving strategies and optimization of energy use to achieve the goal of energy-saving and consumption reduction and provides a new solution for the future management of building energy consumption. Full article

The operation and maintenance of futuristic electric vehicles need accurate estimation of the state of health (SOH) of lithium-ion batteries (LIBs). To address this issue, a robust neural network framework is proposed to estimate the SOH. This article developed a novel approach that combines improved particle swarm optimization (IPSO) with bidirectional long short-term memory (Bi-LSTM) to effectively address the issue of precisely estimating SOH. The proposed IPSO-Bi-LSTM model is more effective than the other models for SOH estimation. This is because Bi-LSTM can capture both past and future appropriate information, making it more suitable for modeling complicated temporal sequences. The IPSO main objective is to optimize the model hyperparameters. To increase the model’s accuracy, the IPSO improves the parameters. The PSO-Bi-LSTM model performed better than the other approaches, according to experimental findings based on the NASA-PCOE battery dataset, and all of the SOH estimated outcomes, such as root mean square errors, were less than 0.50%. This result suggests that the proposed PSO-Bi-LSTM model has the ability to robustly estimate the SOH with a high accuracy. Full article

Directional sensor networks are a widely used architecture in the sensing layer of the Internet of Things (IoT), which has excellent data collection and transmission capabilities. The coverage hole caused by random deployment of sensors is the main factor restricting the quality of data collection in the IoT sensing layer. Determining how to enhance coverage performance by repairing coverage holes is a very challenging task. To this end, we propose a node deployment optimization method to enhance the coverage performance of the IoT sensing layer. Firstly, with the goal of maximizing the effective coverage area, an improved particle swarm optimization (IPSO) algorithm is used to solve and obtain the optimal set of sensing directions. Secondly, we propose a repair path search method based on the improved sparrow search algorithm (ISSA), using the minimum exposure path (MEP) found as the repair path. Finally, a node scheduling algorithm is designed based on MEP to determine the optimal deployment location of mobile nodes and achieve coverage enhancement. The simulation results show that compared with existing algorithms, the proposed node deployment optimization method can significantly improve the coverage rate of the IoT sensing layer and reduce energy consumption during the redeployment process. Full article

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery. Full article

To overcome the limitations of single-type intelligent optimization algorithms prone to becoming stuck in local optima for complex problems, a hybrid intelligent optimization algorithm named SDIQ is proposed. This algorithm combines simulated annealing (SA), differential evolution (DE), quantum-behaved particle swarm optimization (QPSO), and improved particle swarm optimization (IPSO) into a unified framework. Initially, SA is used to explore the solution space and guide individuals toward preliminary optimization. The individuals are then ranked by fitness and divided into three subpopulations, each optimized by DE, QPSO, and IPSO, respectively. After each iteration, probabilistic learning based on fitness logarithms facilitates mutual learning among subpopulations, enabling global information sharing and improvement. The experimental results demonstrate that SDIQ exhibits strong global search capability and stability in solving both standard test functions and real-world engineering problems. Compared to traditional algorithms, SDIQ enhances global convergence and solution efficiency by integrating multiple optimization strategies and leveraging inter-individual learning, providing an effective solution for complex optimization problems. Full article

Accurate runoff prediction is crucial for watershed water resource management, flood prevention, and hydropower station scheduling. Data-driven models have been increasingly applied to runoff prediction tasks and have achieved impressive results. However, existing data-driven methods may produce unreasonable predictions due to the lack of prior knowledge guidance. This study proposes a multivariate runoff prediction model that couples knowledge embedding with data-driven approaches, integrating information contained in runoff probability distributions as constraints into the data-driven model and optimizing the existing loss function with prior probability density functions (PDFs). Using the main stream in the Yellow River Basin with nine hydrological stations as an example, we selected runoff feature factors using the transfer entropy method, chose a temporal convolutional network (TCN) as the data-driven model, and optimized model parameters with the IPSO algorithm, studying univariate input models (TCN-UID), multivariable input models (TCN-MID), and the coupling model. The results indicate the following: (1) Among numerous influencing factors, precipitation, sunshine duration, and relative humidity are the key feature factors driving runoff occurrence; (2) the coupling model can effectively fit the extremes of runoff sequences, improving prediction accuracy in the training set by 6.9% and 4.7% compared to TCN-UID and TCN-MID, respectively, and by 5.7% and 2.8% in the test set. The coupling model established through knowledge embedding not only retains the advantages of data-driven models but also effectively addresses the poor prediction performance of data-driven models at extremes, thereby enhancing the accuracy of runoff predictions. Full article

A model framework for the prediction of defects in strip steel is proposed with the objective of enhancing the accuracy of defect detection. Initially, the data are balanced through the utilisation of the Improved Synthetic Minority Oversampling Technique (ISmote), which is based on clustering techniques. Subsequently, further enhancements are made to the inertia weights and learning factors of the immune particle swarm optimisation (IPSO), with additional optimisations in speed updates and population diversity. These enhancements are designed to address the issue of premature convergence at the early stages of the process and local optima at the later stages. Finally, a prediction model is then constructed based on stacking, with its hyperparameters optimised through the improved immune particle swarm optimisation (IIPSO). The results of the experimental trials demonstrate that the IIPSO-ISmote-Stacking model framework exhibits superior prediction performance when compared to other models. The Macro_Precision, Macro_Recall, and Macro_F1 values for this framework are 93.3%, 93.6%, and 92.2%, respectively. Full article

It is a common view that Popper’s defense of the open society has been a defense of Western, liberal democracies. This seems to imply that by fostering democratic institutions we are ipso facto fostering open societies. I criticize this view by arguing that in-built incentives in democratic mechanisms move us away from (or hamper) the open society. Democracy promotes voters’ ignorance, indulges voters’ irrationality, and allows voters to externalize costs. This is contrary to well-informed, rational decisions and personal responsibility that lie at the fundamentals of the open society. I suggest that it has been free-market capitalism, or free-market societies, which has moved us closer to the ideal of the open society and which best realizes open society’s values. Full article

Bromhexine and ambroxol are among the mucolytic drugs most widely used to treat acute and chronic respiratory diseases. Entering the municipal wastewater and undergoing transformations during disinfection with active chlorine, these compounds can produce nitrogen- and bromine-containing disinfection by-products (DBPs) that are dangerous for aquatic ecosystems. In the present study, primary and deep degradation products of ambroxol and bromhexine obtained in model aquatic chlorination experiments were studied via the combination of high-performance liquid and gas chromatography with high-resolution mass spectrometry. It was shown that at the initial stages, the reactions of cyclization, hydroxylation, chlorination, electrophilic ipso-substitution of bromine atoms with chlorine, and oxidative N-dealkylation occur. Along with known metabolites, a number of novel primary DBPs were tentatively identified based on their elemental compositions and tandem mass spectra. Deep degradation of bromhexine and ambroxol gives twenty-four identified volatile and semi-volatile compounds of six classes, among which trihalomethanes account for more than 50%. The specific class of bromhexine- and ambroxol-related DBPs are bromine-containing haloanilines. Seven of them, including methoxy derivatives, were first discovered in the present study. One more novel class of DBPs associated with bromhexine and ambroxol is represented by halogenated indazoles formed through dealkylation of the primary transformation products containing pyrazoline or tetrahydropyrimidine cycle in their structure. Full article

Due to the increasing integration of photovoltaic (PV) solar power into power systems, the prediction of PV solar power output plays an important role in power system planning and management. This study combines an optimized data clustering method with a serially integrated AdaLSTM network to improve the accuracy and robustness of PV solar power prediction. During the data clustering process, the Euclidean distance-based clustering centroids are optimized by an improved particle swarm optimization (iPSO) algorithm. For each obtained data cluster, the AdaLSTM network is utilized for model training, in which multiple LSTMs are serially combined together through the AdaBoost algorithm. For PV power prediction tasks, the inputs of the testing set are classified into the nearest data cluster by the K-nearest neighbor (KNN) method, and then the corresponding AdaLSTM network of this cluster is used to perform the prediction. Case studies from two real PV stations are used for prediction performance evaluation. Results based on three prediction horizons (10, 30 and 60 min) demonstrate that the proposed model combining the optimized data clustering and AdaLSTM has higher prediction accuracy and robustness than other comparison models. The root mean square error (RMSE) of the proposed model is reduced, respectively, by 75.22%, 73.80%, 67.60%, 66.30%, and 64.85% compared with persistence, BPNN, CNN, LSTM, and AdaLSTM without clustering (Case A, 30 min prediction). Even compared with the model combining the K-means clustering and AdaLSTM, the RMSE can be reduced by 10.75%. Full article

The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD’s path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO’s superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC. Full article

Two ferrocene derivatives, namely, 1,2-(tetramethylene)-ferrocene and 1,2,1′,2′-bis(tetramethylene)-ferrocene, were synthesized in a four-step reaction sequence starting from ferrocene. Friedel–Crafts acylation of ferrocene using succinic anhydride gave mono- or bis(3-carboxypropinoyl)-ferrocene depending on the stoichiometry of succinic anhydride. The reduction of the keto groups to methylene followed by ring-closing using trifluoroacetic anhydride gave 1,2-(α-ketotetramethylene)-ferrocene or 1,2,1′,2′-bis(α-ketotetramethylene)-ferrocene. The diastereomeric mixture of the latter diketones was separated using column chromatography, characterized via single-crystal X-ray analysis, and assigned its stereochemistry. Reduction of the keto groups to methylene under Clemmensen conditions gave homoannular mono- or bis(tetramethylene)-ferrocene derivatives. The molecular structure of 1,2-(tetramethylene)-ferrocene revealed that the ipso carbon atoms of the cyclopentadienyl group are 0.023(3) Å farther away from Fe(II) compared to the remaining three carbon atoms. Both complexes exhibit lower half-wave oxidation potentials than ferrocene, possibly due to the electron-releasing effects of the tetramethylene bridges. Full article