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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

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Computer Modeling in engineering & sciences 2023年第11期137卷 1677-1694页

作者： Muhammad Tayyeb Muhammad Umer Khaled Alnowaiser Saima Sadiq Ala’Abdulmajid Eshmawi Rizwan Majeed Abdullah Mohamed Houbing Song Imran Ashraf Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar KhanPakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computer Science and Information Technology Universiti Tun Husein OnnMalaysia(UTHM)Bahru80536Malaysia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information Systems University of MarylandBaltimore CountyBaltimoreMD 21250USA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed ***,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is *** addition,the diagnosis requires experienced medical experts and is ***,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning *** study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational *** dataset containing averaged signals with window size 10 is used as an *** competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the *** outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy *** findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

关键词： Cardiovascular disease prediction electrocardiograms deep learning multilayer perceptron

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Parameter Estimation and Tracking Control of MIMO Linear systems Without Prior Knowledge of Control Signs and Parameter Bounds

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IEEE Transactions on Automatic Control 2024年第6期70卷 3695-3710页

作者： Xu, Yuchun Zhang, Yanjun Zhang, Ji-Feng Chinese Academy of Sciences Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Beijing100190 China Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology State Key Lab of Automation Intelligent Unmanned Systems Beijing100081 China University of Chinese Academy of Sciences School of Mathematics Sciences Beijing100149 China Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China Chinese Academy of Sciences Academy of Mathematics and Systems Science Beijing100190 China

Dealing with the uncertain high-frequency gain matrix, denoted as Kp, is a fundamental problem in multivariable adaptive control systems. In this paper, we propose a new solution for parameter estimation and adaptive control for a general class of multi-input multi-output discrete-time linear time-invariant systems. The proposed scheme does not require any prior knowledge of the sign or bound information of Kp, and thus, significantly relaxes the design conditions in traditional multivariable adaptive control systems. Compared with the commonly used Nussbaum gain or multi-model techniques for addressing the unknown signs of Kp, the proposed scheme does not rely on any additional design conditions or any switching mechanism, while still ensuring closed-loop stability and asymptotic output tracking. Specifically, an output feedback adaptive control law is developed based on a matrix decomposition technique, which leads to derivation of a modified estimation error model. Subsequently, a gradient-based parameter update law is formulated only relying on the non-zero condition of the leading principle minors of Kp. Through designing gain functions and stable filters, the controller is always non-singular and does not involve any causal contradiction problem. Simulation study showcases the design process and demonstrates the effectiveness of the proposed scheme. © 1963-2012 IEEE.

关键词： MIMO systems

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LEO Satellite Oriented Wildfire Detection Model Using Deep Neural Networks: A Transfer Learning Based Approach 6

LEO Satellite Oriented Wildfire Detection Model Using Deep N...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Gain, Mrityunjoy Raha, Avi Deb Biswas, Bristy Bairagi, Anupam Kumar Adhikary, Apurba Debnath, Rameswar Khulna University Computer Science and Engineering Discipline Khulna Bangladesh Ai Innovation Lab Khulna Bangladesh Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350385779

Wildfires are a recurring concern in several regions around the globe, often leading to catastrophic outcomes. The increasing accessibility of small satellites operating in Low Earth Orbit (LEO) has led to the potential for enhancing the speed of wildfire detection b y utilizing constellations of m any miniature satellites in the areas of interest. This research presents a system model for detecting and reporting wildfires to fire stations using LEO satellites. In this paper, we propose a transfer learning-based lightweight deep learning wildfire detection model at the base station to analyze pictures captured by LEO satellites and make predictions on the occurrence of wildfires. We use MobileNetV2 as our backbone model. MobileNetV2 is a lightweight model with low parameters. Lightweight models for image categorization offer faster inference, lower power consumption, efficient memory u sage, a nd ease of deployment, making them well-suited for real-time applications. However, a highly parameterized model can perform better than a poorly parameterized model. We apply transfer learning to ensure high performance in a low-parameterized model. The server must continuously check the images of all forest regions within a limited interval period to detect wildfires quickly. The lightweight model is very suitable for continuous image checking. Our experimental setup uses four baselines: VGG16, ResNet152, DenseNet161, and MobileNetv2. This study shows the efficacy of models in acquiring information from many sources within a given topic, as opposed to starting with no prior knowledge. We can also find from t he experimental results that our proposed low-parameterized transfer learning-based mobileNetV2 model achieves near 100 % accuracy, outperforming the other high-parameterized baselines. © 2024 IEEE.

关键词： Orbits

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Modeling and Measurement of 3D Solenoid Inductor Based on Through-Silicon Vias

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Chinese Journal of Electronics 2023年第2期32卷 365-374页

作者： YIN Xiangkun WANG Fengjuan ZHU Zhangming Vasilis F.Pavlidis LIU Xiaoxian LU Qijun LIU Yang YANG Yintang Shaanxi Key Lab.of Integrated Circuits and Systems School of Microelectronics Xidian University School of Automation and Information Engineering Xi'an University of Technique Department of Computer Science University of Manchester

Through-silicon via(TSV) provides vertical interconnectivity among the stacked dies in three-dimensional integrated circuits(3D ICs) and is a promising option to minimize 3D solenoid inductors for on-chip radio-frequency applications. In this paper, a rigorous analytical inductance model of 3D solenoid inductor is proposed based on the concept of loop and partial inductance. And a series of 3D samples are fabricated on 12-in high-resistivity silicon wafer using low-cost standard CMOS-compatible process. The results of the proposed model match very well with those obtained by simulation and measurement. With this model, the inductance can be estimated accurately and efficiently over a wide range of inductor windings, TSV height, space, and pitch.

关键词： Semiconductor device modeling Inductance Analytical models Solid modeling Three-dimensional displays Integrated circuit modeling Inductors

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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A multi-layer metallic structure based on phase cancellation for wideband RCS reduction 2

A multi-layer metallic structure based on phase cancellation...

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2nd International Workshop on engineering Physics, IWEP 2024

作者： Duan, Y. Xiao, S. School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Systems Science and Engineering Sun Yat-sen University Guangzhou China

In this article, a multi-layer metallic structure based on phase cancellation for wideband radar cross-section (RCS) reduction is proposed. Firstly, the process of perfect electric conductors (PECs) positioned at different heights to generate reflected waves with different phases, ultimately achieving phase cancellation and RCS reduction, is investigated. Secondly, the accumulative nature of phase cancellation is studied, which advances understanding on phase cancellation and provides new a way for wideband RCS reduction. The proposed multi-layer metallic structure is simulated, which offers at least a fractional bandwidth of 122.2% (2.1 GHz-8.7 GHz) with 10 dB RCS reduction. A good agreement between the calculated and simulated results validates the proposed design. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Radar cross section

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Target Grouping and Formation Recognition Based on Movement Trend Features 10th

Target Grouping and Formation Recognition Based on Movement ...

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10th China Conference on Command and Control, C2 2022

作者： Meng, Xiangxia Li, Tingting Rao, Jiaren Science and Technology on Information Systems Engineering Laboratory Nanjing210007 China

ISBN: (纸本)9789811960512

In order to improve the accuracy and efficiency of target grouping and formation recognition in complex air combat scenarios, an automatic algorithm based on movement trend features is established. Firstly, a trajectory segmentation algorithm based on the minimum description length is established to extract the trajectory segments that can express the current movement trend features of the targets. Then, a trajectory similarity measurement model is used to group targets. A shape feature matching algorithm is constructed to identify the formations of the groups. The algorithm can realize the automatic aggregation of single targets to group, and can automatically identify the formation. © 2022, Chinese Institute of Command and Control.

关键词： Trajectories

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New Interpretation of Battlefield Situation Awareness in the Era of Artificial Intelligence 10th

New Interpretation of Battlefield Situation Awareness in the...

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10th China Conference on Command and Control, C2 2022

作者： Wang, Heng Li, Tingting Rao, Jiaren Science and Technology on Information Systems Engineering Laboratory Nanjing210007 China

ISBN: (纸本)9789811960512

The intelligent warfare is coming with the development of new technologies, promoting the battlefield situation awareness with new features. In order to support the developing of artificial intelligent situation awareness systems, the connotation of battlefield situation awareness is discussed from the perspective of Chinese military theory and commander’s cognitive process. Then, the cognitive domain operations, the acquisition of cognitive advantages and the development trend of battlefield situation awareness system in the era of artificial intelligence are discussed. With the above theories, a new application mode of artificial intelligent battlefield situation awareness system is proposed. © 2022, Chinese Institute of Command and Control.

关键词： Cognitive systems

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