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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yi, Xin Yi, Jing-Wen Chai, Li School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331527471

It is well known that adding the momentum term can accelerate the convergence rate of optimization algorithms. In this paper, the momentum term is exploited to double accelerate the distributed Nesterov gradient descent algorithm without increasing the communication burden, and a novel momentum-based distributed Nesterov gradient descent (DNGD-M) algorithm is proposed. Using the method of inequality transformation, the convergence problem of the DNGD-M algorithm is converted into the stability problem of the extended system. Then, by analyzing the state transition matrix of the extended system, the convergence conditions of the proposed algorithm and the corresponding ranges of parameter values are obtained. Finally, numerical experiments are provided to verify the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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Influence of Physical Parameters on Echo Spectrum of Underwater Target

Influence of Physical Parameters on Echo Spectrum of Underwa...

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2024 OES China Ocean Acoustics, COA 2024

作者： Chen, Yunfei Yu, Dapeng Jia, Bing Wang, Zhenshan Yong, Jintao Wang, Yanjie School of Information and Communication Engineering Dalian University of Technology Dalian China Science and Technology on Underwater Test and Control Laboratory Dalian China

ISBN: (纸本)9798350340297

The elastic scattering features of the underwater target scattering wave depend on the physical parameters of the target, such as the material and structure. Intuitively evaluating the influence of the elastic scattering wave on the characteristics of the whole scattering wave signal is difficult due to the coupling and superposition of the elastic scattering wave and the geometric scattering wave of the target. Based on the different modulation characteristics of broadband echo spectrum produced by target geometry scattering and elastic scattering wave, this paper characterizes and studies the modulation features of echo spectrum and tests the influence of physical parameters on the echo spectrum of underwater target using several simple cylindrical targets with different physical parameters. Moreover, the geometric scattering wave and the elastic scattering wave of the simple target echo are extracted using the fractional Fourier transform (FrFT) domain filtering method, and the influence of physical parameters on the echo spectrum is quantitatively analyzed. © 2024 IEEE.

关键词： Elastic scattering

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Convolutional Neural Network Based Defect Detection in Small Diameter Pipe Welds 9

Convolutional Neural Network Based Defect Detection in Small...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Guo, Yu Gao, Weixin Wang, Zihan School of Electronic Engineering Xi'an Shiyou University Xi'an China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (纸本)9798350376548

Aiming at the current situation of low accuracy of X-ray weld image detection rate of small diameter pipe, the defect recognition algorithm of small diameter pipe weld image based on image segmentation is proposed by analysing the image and combining with convolutional neural network. Firstly, the ROI region is obtained by two-step image segmentation, then the SDR image is obtained by grey density clustering, and finally the SDR image is classified and identified on the basis of analysing and determining the structure of convolutional neural network. The experimental results show that the accuracy of defect detection reaches 95%, the false alarm rate is only 5%, and the recognition speed is fast, which achieves the effective recognition of small diameter pipe X-ray weld images. © 2024 IEEE.

关键词： Convolutional neural networks

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Windings Reconfigured Fault-Tolerant control for Five-Phase PMSMs with Reduced Torque Ripple

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IEEE Transactions on Energy Conversion 2025年第1期40卷 665-676页

作者： Ji, Jinghua Li, Zongwang Zhao, Xiaopeng Yu, Yanjun Zhao, Wenxiang AVIC Xi'an Flight Automatic Control Research Institute Xi'an710065 China Nanjing Institute of Technology School of Electric Power Engineering Nanjing211167 China Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China

This paper proposes a winding reconfigured fault-tolerant control strategy for five-phase permanent magnet synchronous motors with an open circuit fault. First, a new decoupling matrix is derived from the referenced fault-tolerant currents, which effectively decouples the phase currents into the fundamental subspace and dual-zero-sequence subspace. In order to mitigate the harmonic current, a novel winding connection topology is proposed in which the harmonic plane current can be eliminated without closed-loop control. Thereby, the pulsating term in the torque equation can be eliminated. Moreover, the post-fault four-phase motor can operate in the two-phase mode. As a result, only the currents of two phases are required for coordinate transformation, and the coordinate transformation process can be greatly simplified. The analysis of voltage utilization shows that the elimination of the harmonic subspace can improve the voltage utilization rate. Experimental results verify that the proposed strategy can achieve lower torque ripples, lower system loss and less computation time compared with the existing fault-tolerant strategy. © 1986-2012 IEEE.

关键词： Permanent magnets

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A Study on the Thermal Deformation Prediction Model of a Motorized Spindle Based on Swarm Intelligence Optimization Algorithms 7

A Study on the Thermal Deformation Prediction Model of a Mot...

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7th IEEE International Conference on information Systems and Computer Aided Education, ICISCAE 2024

作者： Wei, Wei Wang, Ruizhe Department of Information and Control Engineering Shenyang Institute of Science and Technology Shenyang China School of Electrical Engineering Suihua University Suihua China

ISBN: (纸本)9798350350760

Thermal deformation of the motorized spindle significantly impacts the machining accuracy of CNC machine tools. The thermal expansion coefficient, a critical parameter affecting thermal deformation, is highly nonlinear, making it challenging to determine an accurate coefficient that enhances machining precision. This study proposes a thermal deformation model for the motorized spindle based on an improved Grasshopper Optimization Algorithm, aimed at optimizing the thermal expansion coefficient. Experimental studies were conducted on the motorized spindle at various rotational speeds. The results demonstrate that the thermal expansion coefficient obtained using the improved Grasshopper Optimization Algorithm yields more accurate predictions of thermal deformation, with smaller errors compared to actual measurements, than those obtained using an empirical thermal expansion coefficient. ©2024 IEEE.

关键词： Swarm intelligence

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Effect of industrial alkene ozonolysis on atmospheric H_(2)SO_(4)formation

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Journal of Environmental Sciences 2025年第4期150卷 466-476页

作者： Xing Wang Xiaowen Shi Youling Jiang Yan Ma Jun Zheng School of Environmental Science and Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China Jiangsu Provincial Academy of Environmental Sciences Nanjing 210036China

This study has employed the master chemical mechanism(MCM)to investigate the influence of the ozone oxidation pathways in the atmospheric formation of H_(2)SO_(4)from shortchain olefins in industrialized ***-situ H_(2)SO_(4)formation data were obtained using a high-resolution chemical ionization time-of-flight mass spectrometer,and the simulated H_(2)SO_(4)concentrations calculated using updated parameters for the MCM model exhibited good agreementwith *** the simulation analysis of different reaction pathways involved in H_(2)SO_(4)formation,hydroxyl radicals were found to dominate H_(2)SO_(4)production during the daytime,while olefin ozone oxidation contributed up to 65%of total H_(2)SO_(4)production during the night-time.A sensitivity analysis of the H_(2)SO_(4)production parameters has revealed a high sensitivity to changes in sulfur dioxide,and a relatively high sensitivity to olefins with fast ozonolysis reaction rates and bimolecular reaction rates of resulting stabilized Criegee Intermediates.A high relative humidity promotes daytime H_(2)SO_(4)formation,but has an inhibiting effect during the night-time due to the different dominant reaction pathways.

关键词： Gaseous sulfuric acid Alkene ozonation reaction MCM model Sensitivity analysis

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Linear Regression-Based Autonomous Intelligent Optimization for Constrained Multiobjective Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4620-4634页

作者： Wang, Yan Sun, Xiaoyan Zhang, Yong Gong, Dunwei Hu, Hejuan Zuo, Mingcheng China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Shandong Qingdao266061 China China University of Mining and Technology Artificial Intelligence Research Institute Jiangsu Xuzhou221116 China

It is very challenging to autonomously generate algorithms suitable for constrained multiobjective optimization problems due to the diverse performance of existing algorithms. In this article, we propose a linear regression (LR)-based autonomous intelligent optimization method. It first extracts typical features of a constrained multiobjective optimization problem by focused sampling to form a feature vector. Then, a LR model is designed to learn the relationship between optimization problems and intelligent optimization algorithms (IOAs). Finally, the trained model autonomously generates a suitable IOA by inputting the feature vector. The proposed method is applied to six constrained multiobjective benchmark test sets with various characteristics and compared with seven popular optimization algorithms. The experimental results verify the effectiveness of the proposed method. In addition, the proposed method is used to solve the operation optimization problems of an integrated coal mine energy system, and the experimental results show its practicability. © 2020 IEEE.

关键词： Feature extraction

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Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

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Chinese Physics B 2023年第10期32卷 273-282页

作者：马璐陈梅辉何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health *** physiological complexity of the cardiac system is related to age,disease,*** the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time *** propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological *** obtained results suggest that these two network measures show distinct changes between different *** is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac ***,the complexity of the cardiac system is *** that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed *** of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two ***,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

关键词： electrocardiogram signals multivariate recurrence networks cross-clustering coefficient entropy multiscale analysis

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Digital Twin and Cloud Based Remote control of Vehicles 2

Digital Twin and Cloud Based Remote Control of Vehicles

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2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Rovid, Andras Tihanyi, Viktor Cserni, Marton Csontho, Mihaly Domina, Adam Remeli, Viktor Vincze, Zsolt Szanto, Matyas Szalai, Matyas Nagy, Szabolcs Szalay, Zsolt Budapest University of Technology and Economics Faculty of Transportation Engineering and Vehicle Engineering Department of Automotive Technologies Hungary Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Control Engineering and Information Technology Hungary

ISBN: (纸本)9798350307733

Cloud-based, centralized control system architectures have been published in recent years, examining the applications, structure and validity of the idea of controlling a vehicle, or multiple vehicles directly from the cloud. The literature presents possible advantages and disadvantages of control as a Service (CaaS), as well as multiple viable applications, however no real-car real-world implementation exists to the best of our knowledge. In our experiments, we demonstrate a real-life and real-time vehicle control method that runs in the cloud and reacts adaptively to the vehicle's surroundings. In the presented tests, acquisition of the vehicle and environment state is performed from a collective perception-based digital twin that is also produced in our cloud-based Central System using data streams from our distributed sensor network. We conduct real-world physical vehicle tests, and establish that cloud-based centralized control is a viable solution, and an interesting research direction. We test our vehicle control in one mixed reality, and two real-world scenarios on the ZalaZONE proving ground in Hungary, Zalaegerszeg. Our experiments prove that cloud-based low-level vehicle control is feasible. © 2024 IEEE.

关键词： Sensor networks

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Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

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Chinese Physics B 2023年第11期32卷 401-407页

作者：马璐任彦霖何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free ***, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

关键词： epilepsy EEG signal horizontal visibility graph complex network

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