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Comprehensive Evaluation Method for Traffic Flow Data Quality Based on Grey Correlation Analysis and Particle Swarm Optimization

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Journal of Systems Science and Systems engineering 2024年第1期33卷 106-128页

作者： Wei Ba Baojun Chen Qi Li School of Automation and electrical engineering Dalian Jiaotong UniversityDalian116028China faculty of Electronic information and electrical engineering Dalian University of TechnologyDalian116023China

Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or ***,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation *** order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this *** first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is ***,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function ***,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of *** proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.

关键词： Data quality comprehensive evaluation particle swarm optimization grey correlation analysis traffic flow data

Evaluating transient behaviour of large-scale photovoltaic systems during lightning events using enhanced finite difference time domain method with variable cell size approach

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High Voltage 2024年第3期9卷 636-647页

作者： Ibrahim Hetita Diaa-Eldin A.Mansour Yang Han Ping Yang Congling Wang Mohamed M.F.Darwish Matti Lehtonen Amr S.Zalhaf School of Mechanical and electrical engineering University of Electronic Science and Technology of China(UESTC)ChengduChina electrical Power and Machines engineering Department Faculty of EngineeringHelwan UniversityCairoEgypt electrical Power and Machines engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Department of electrical Power engineering Faculty of EngineeringEgypt-Japan University of Science and TechnologyAlexandriaEgypt Department of electrical engineering Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of electrical engineering and Automation School of Electrical EngineeringAalto UniversityEspooFinland

Photovoltaic(PV)arrays are usually installed in open areas;hence,they are vulnerable to lightning strikes that can result in cell degradation,complete damage,service disruption,and increased maintenance *** a result,it is imperative to develop an effective and efficient lightning protection system by evaluating the transient behaviour of PV arrays during lightning *** aim is to evaluate the transient analysis of large-scale PV systems when subjected to lightning strikes using the finite difference time domain(FDTD)*** overvoltages are calculated at various points within the mounting *** optimise the FDTD method's execution time and make it more suitable for less powerful hardware,a variable cell size approach is ***,larger cell dimensions are used in the earthing system and smaller cell dimensions are used in the mounting *** FDTD method is utilised to calculate the temporal variation of transient overvoltages for large-scale PV systems under different scenarios,including variations in the striking point,soil resistivity,and the presence of a metal *** results indicate that the highest transient overvoltages occur at the striking point,and these values increase with the presence of a PV metal frame as well as with higher soil ***,a comparison is performed between the overvoltage results obtained from the FDTD approach and the partial element equivalent circuit(PEEC)method at the four corner points of the mounting systems to demonstrate the superior accuracy of the FDTD ***,a laboratory experiment is conducted on a small-scale PV system to validate the simulation *** calculated overvoltages obtained from the FDTD and PEEC methods are compared with the measured values,yielding a mean absolute error of 5%and 11%for the FDTD and PEEC methods,respectively,thereby confirming the accuracy of the FDTD simulation model.

关键词： transient behaviour finite

Optimization for sound insulation of a sandwich plate with a corrugation and auxetic honeycomb hybrid core

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1595-1612页

作者： Fenglian LI Yiping WANG Yuxing ZOU School of Mechanical and electrical engineering Beijing Information Science and Technology UniversityBeijing 100192China

A sandwich plate with a corrugation and auxetic honeycomb hybrid core is constructed,and its sound insulation and optimization are ***,the motion governing equation of the sandwich plate is established by the third-order shear deformation theory(TSDT),and then combined with the fluid-structure coupling conditions,and the sound insulation is *** theoretical results are validated by COMSOL simulation results,and the effects of the structural parameter on the sound insulation are ***,the standard genetic algorithm is adopted to optimize the sound insulation of the sandwich plate.

关键词： optimization sound insulation hybrid core layer genetic algorithm

Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet

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Computers, Materials & Continua 2024年第8期80卷 1967-1983页

作者： Qiuyan Wang Haibing Dong Yongfei Huang Zenglei Liu Yundong Gou School of electrical and information engineering Hunan Institute of TechnologyHengyang421002China

Sharing data while protecting privacy in the industrial Internet is a significant *** machine learning methods require a combination of all data for training;however,this approach can be limited by data availability and privacy *** learning(FL)has gained considerable attention because it allows for decentralized training on multiple local ***,the training data collected by data providers are often non-independent and identically distributed(non-IID),resulting in poor FL *** paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain *** overcome the problem of non-IID data leading to poor training accuracy,we propose dynamically updating the local model based on the divergence of the global and local *** approach can significantly improve the accuracy of FL training when there is relatively large *** addition,we design a dynamic gradient clipping algorithm to alleviate the influence of noise on the model accuracy to reduce potential privacy leakage caused by sharing model ***,we evaluate the performance of the proposed scheme using commonly used open-source image *** simulation results demonstrate that our method can significantly enhance the accuracy while protecting privacy and maintaining efficiency,thereby providing a new solution to data-sharing and privacy-protection challenges in the industrial Internet.

关键词： Federated learning data sharing non-IID data differential privacy blockchain

Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

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Journal of Systems Science & Complexity 2024年第3期37卷 965-984页

作者： NIU Ben WANG Xiaoan WANG Xiaomei WANG Xinjun LI Tao faculty of Electronic information and electrical engineering Dalian University of TechnologyDalian 116024China School of information Science and engineering Shandong Normal UniversityJinan 250014China School of Mathematics Southeast UniversityNanjing 211189China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state *** to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered *** to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be ***,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear *** presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to ***,it is shown that the constraint requirement on the system will not be violated during the ***,two simulation examples are provided to show the effectiveness of the proposed control scheme.

关键词： Asymptotic tracking control barrier Lyapunov functions full state constraints nonlinear pure-feedback systems

Power optimization of photovoltaic modules under varying environmental conditions based on current equalization collaborating with constant voltage control

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Clean Energy 2024年第4期8卷 169-182页

作者： DaiBin Tang Fei Lu Siaw Tzer Hwai Gilbert Thio School of electrical engineering Anhui Technical College of Mechanical and Electrical EngineeringWuhu 241002China Centre for Sustainability in Advanced electrical and Electronics Systems(CSAEES) Faculty of EngineeringBuilt Environment and Information TechnologySEGi University47810 Petaling JayaSelangorMalaysia

The performance of photovoltaic(PV)modules is affected by environmental factors such as irradiance and temperature,which can lead to a decrease in output performance or even *** study proposes an improved formula for calculating the real maximum power of PV modules by analysing the influence of irradiance and temperature.A simulation model is developed using PLECS software to simulate the global maximum power of PV modules under different environmental conditions and the results are compared with the calculated real maximum power.A power optimization scheme for PV modules is then proposed based on current equalization and constant voltage *** scheme employs a single-switch multi-winding forward-flyback converter to equalize the mismatched currents between cell strings,thereby enhancing the output *** proportional-integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of PV *** models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb-and-observe(TPO)algorithm with current equalization,a segment perturb-and-observe algorithm without current equalization,and the proposed power optimization *** simulation results demonstrate that the proposed constant voltage control has greater efficiency than the TPO *** proposed scheme achieves a significant improvement in efficiency,with a 27.87%increase compared with the segment perturb-and-observe algorithm without current equalization.

关键词： current equalization constant voltage control forward-flyback converter PV modules perturb-and-observe(P&O)algorithm

An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of electrical and information engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of electrical engineering and Automation Henan Institute of Technology School of Electronics and information Zhongyuan University of Technology KINDI Center for Computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

H_(∞)/Passive Synchronization of Semi-Markov Jump Neural Networks Subject to Hybrid Attacks via an Activation Function Division Approach

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Journal of Systems Science & Complexity 2024年第3期37卷 1023-1036页

作者： ZHANG Ziwei SHEN Hao SU Lei School of electrical and information engineering Anhui University of TechnologyMa'anshan 243002China Anhui Province Key Laboratory of Special Heavy Load Robot and School of electrical and information engineering Anhui University of TechnologyMa'anshan243002China

In this work,an H_(∞)/passive-based secure synchronization control problem is investigated for continuous-time semi-Markov neural networks subject to hybrid attacks,in which hybrid attacks are the combinations of denial-of-service attacks and deception attacks,and they are described by two groups of independent Bernoulli *** this foundation,via the Lyapunov stability theory and linear matrix inequality technology,the H_(∞)/passive-based performance criteria for semi-Markov jump neural networks are ***,an activation function division approach for neural networks is adopted to further reduce the conservatism of the ***,a simulation example is provided to verify the validity and feasibility of the proposed method.

关键词： Activation function division approach deception attacks denial-of-service attacks H_(∞)/passive synchronization semi-Markov jump neural networks

Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of electrical and information engineering Zhengzhou UniversityZhengzhou 450001 School of electrical engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of electrical and information engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of electrical and information engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.