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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 922-932页

作者： Cheng, Yuhu Chen, Yang Kong, Yi Philip Chen, C.L. Wang, Xuesong China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationSchool of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

An end-to-end unsupervised domain adaptation method for hyperspectral image (HSI) classification based on a graph dual adversarial network is proposed in this article. First, in order to extract the domain-invariant features of the source and target domains, the rich spectral information and spatial position of HSI are used to construct a spectral-spatial nearest neighbor graph, which is input into the graph convolutional network. Then, a prototype adversarial strategy is proposed, which uses the labeled data of the source domain to reliably calculate the feature prototypes of different classes. Through the prototype adversarial strategy, the distances between different prototypes are appropriately extended, so that the clusters of different classes are far away from their respective decision boundaries, and the discriminability of features are also enhanced. The dual adversarial strategy is composed of the prototype adversarial strategy and the domain adversarial strategy. It is worth noting that the dual adversarial strategy does not require feature extractor and discriminator to work in turn, which can be implemented through a gradient reversal layer. Finally, on the basis of adapting the overall features of both the domains via the domain adversarial strategy, the source- and target-domain features are further adapted by minimizing the correlation alignment loss of each class of samples. Experimental results on two real HSI datasets of Botswana and Kennedy Space Center show the effectiveness of our proposed method. © 2020 IEEE.

关键词： Spectroscopy

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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A Physical-Data Fusion Framework for Lithium-ion Battery SOC Estimation∗ 49

A Physical-Data Fusion Framework for Lithium-ion Battery SOC...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Fu, Bingzhe Li, Yihuan Liu, Kailong Lin, Wei School of Control and Computer Engineering North China Electric Power University Beijing China School of Control Science and Engineering Shandong University Shandong China

ISBN: (纸本)9798350331820

Lithium-ion batteries have increasingly become a primary energy source in Electric Vehicles (EVs), power grid energy storage, aerospace, and other fields. Accurate State of Charge (SOC) estimation is crucial for the safe and efficient operation of lithium batteries. This paper proposes a physical-data fusion framework for accurate SOC estimation of Lithium-ion batteries. First, a fractional-order model (FOM) of lithium-ion batteries is built to describe the electrochemical reactions inside the battery, and a fractional-order Extended Kalman Filter (FOEKF) algorithm is used to achieve preliminary SOC estimation. Second, the FOM is combined with an error model established by Long Short-Term Memory (LSTM) to improve the accuracy of the FOEK-based SOC estimation by compensating for the estimation error. Finally, the proposed framework is verified on Maryland dataset, and the experimental results demonstrate that the fusion framework exhibits superior performance in improving the accuracy of SOC estimation, with at least 75% and 50% reduction in MSE and max error, respectively. © 2023 IEEE.

关键词： Long short-term memory

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Fourier Series Fitting-Based Intrusion Detection for Real-Time Industrial control Systems

Fourier Series Fitting-Based Intrusion Detection for Real-Ti...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhang, Heng Wang, Yinchu Xue, Yawen Deng, Ruilong Li, Hongran Zhang, Jian School of Computer Engineering Jiangsu Ocean University Lianyungang China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331507886

Recent incidents have shown the attacks against Industrial control System (ICS), which may result in failure or even catastrophe and prompt the studies of defense. Existing security systems are mostly based on network perimeter defenses which lacking effective countermeasures in the event that such defenses are bypassed and attackers gain access to the system. In this paper, we propose a Fourier series fitting based intrusion detection method, which enables a detection and an alarm when ICS is under attack. Specifically, we first divide the data into three parts based on time with a sliding window, namely real-Time data, sliding window data to be predicted, and historical data. Then we fit the data within the prediction window to the data around the window, together with the root mean squared error (RMSE) calculated for the data within the window and the historical data. The intrusion detection system (IDS) will alarm when the difference between the above RMSE and the reference one is greater than a threshold value. We test two types of data injection attacks, extensive simulations illustrate the effectiveness of our method in detecting both of these attacks. © 2024 IEEE.

关键词： Network intrusion

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Character recognition competition for street view shop signs

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National Science Review 2023年第6期10卷 27-31页

作者： Jingqun Tang Weidong Du Bin Wang Wenyang Zhou Shuqi Mei Tao Xue Xing Xu Hai Zhang Shanghai Byte Dance Technology Co. Ltd School of Control Science and Engineering Zhejiang University School of Computer Science and Technology University of Science and Technology of China School of Computer Science and Technology Tsinghua Univeristy Shenzhen Tencent Technology Co. Ltd School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Store signboards provide important information in street view images,andcharacter recognition in natural scenes is an important research direction in computer *** view store signboard character recognitio...

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Improving Spiking Neural Network Performance Using Astrocyte Feedback for Farsi Digit Recognition 31

Improving Spiking Neural Network Performance Using Astrocyte...

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31st International Conference on Electrical engineering, ICEE 2023

作者： Nazari, Malihe Bahrami, Fariba Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Advanced Control Systems Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Advanced Control Systems Lab Tehran Iran

ISBN: (纸本)9798350312560

The third generation of neural networks is called spiking neural networks. Spiking neural networks can not only answer all the problems that can be solved by common neural networks, they can also be computationally more powerful than them. Also, these networks are closer to the biological reality of neurons. According to these reasons, spiking neural networks have received much attention in recent years. In addition to neurons, other elements are also used in spiking neural networks, whose connection with neurons has been proven biologically. These elements are called astrocytes. In this study, we have used astrocyte local feedback in a spiking neural network to improve the accuracy of the network in a digit recognition process. To reach this goal, we use the network proposed by Diehl et al in 2015, then we added astrocyte local feedback to this network. We run the network in both states. The classification accuracy of the network in the absence of astrocytes was 88.48% on Farsi handwritten digits. After adding astrocyte local feedback, it is observed that astrocytes control the activity of neurons and modified the weight of connections based on neuron activity. In this state classification accuracy reached 90.26%. The idea was to create a neural network for learning, where, inspired by the physiological role of astrocytes at the tripartite synapse improves the performance of the neural network and we demonstrate that systems that include neurons and astrocytes perform better than systems incorporating only neurons. © 2023 IEEE.

关键词： Neurons

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A novel fractional physics-informed neural networks method for solving the time-fractional Huxley equation

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Neural Computing and Applications 2024年第30期36卷 19097-19119页

作者： Shi, Jieyu Yang, Xiaozhong Liu, Xinlong School of Control and Computer Engineering North China Electric Power University Beijing102206 China Institute of Information and Computing School of Mathematics and Physics North China Electric Power University Beijing102206 China

The neural network methods in solving differential equations have significant research importance and promising application prospects. Aimed at the time-fractional Huxley (TFH) equation, we propose a novel fractional physics-informed neural networks (fPINNs) method. By integrating the physical information of the TFH equation into neural networks, the fPINNs are trained as a precise approximation model for solving the TFH equation. The fPINNs method involves calculating the Caputo fractional derivative using the L1 formula and estimating the integer-order derivative through the chain rule. The Adam algorithm is employed to optimize two kinds of loss functions constructed by hard and soft constraints, respectively. Through an analysis of the impact of fPINNs parameters on training results, we identify the optimal parameter configuration for solving both one-dimensional and two-dimensional (2D) TFH equations. Numerical examples validate the efficiency and robustness of the fPINNs method in solving TFH equations. Furthermore, numerical simulation of the 2D TFH problem demonstrates the method’s practical applicability to engineering problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Numerical methods

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Practical Realization of Only Active Floating Lossless Inductance Simulator 10

Practical Realization of Only Active Floating Lossless Induc...

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10th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2024

作者： Roongmuanpha, Natchanai Unhavanich, Sumalee Pukkalanun, Tattaya Tangsrirat, Worapong School Of Engineering Department Of Computer Engineering Bangkok10520 Thailand Faculty Of Digital Technology Bangkok10300 Thailand School Of Engineering Department Of Instrumentation And Control Engineering Bangkok10520 Thailand

ISBN: (纸本)9798350385946

This paper describes an electronically controllable floating lossless inductance simulator circuit implemented with only off-the-shelf available integrated circuits, namely LT1228 and operational amplifier (OA). The proposed design consists solely of two LT1228s and a single OA. By using an active element named LT1228, the equivalent inductance can be tuned electronically with depended on the externally supplied current applied to LT1228. The proposed simulator imposed just one component matching restriction that required the equality of two transconductances. It is easily attainable with a simple current mirror. The worthiness of the proposed floating inductance simulator has been proven and applied to the construction of the 2nd-order RLC highpass/bandpass filter as an application. PSPICE simulations are used to investigate the characteristics of the proposed simulator and its filter realization. © 2024 IEEE.

关键词： Operational amplifiers

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GrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch

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IEEE Internet of Things Journal 2025年第13期12卷 25240-25253页

作者： Ding, Lei Shi, Jiale Wu, Yuan Hu, Xinrong Bi, Hongliang Chen, Yanjiao Wuhan Textile University School of Computer Science and Artificial Intelligence Engineering Research Center of Hubei Province for Clothing Information 430200 China China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang University College of Electrical Engineering Hangzhou310027 China

In the domain of smart devices, biometric identity authentication has become a leading and crucial technology, mainly because of its improved security and user convenience. Traditional methods frequently depend on complex activities, facial recognition, or passwords, which can be cumbersome and error-prone. This paper presents GrasOpen, an innovative biometric authentication system tailored for door-opening scenarios, utilizing the natural reach-and-grasp motion linked to door handles. The system employs smartwatches with accelerometers and gyroscopes to track and analyze arm movements, ensuring a seamless and intuitive user experience. GrasOpen tackles key challenges by leveraging the unique characteristics of the reach-and-grasp motion, eliminating the necessity for users to remember complex motions, and avoiding redundant actions like waiting for facial recognition. Specifically, GrasOpen initially proposes a lightweight model to discern door-opening actions from daily activities. Then, to realize the robust authentication system, GrasOpen integrates a complementary filter (CF) method to capture diversity-dependent features in door-opening actions. These features are subsequently processed through a modified ConvBoost model for precise user authentication. Experimental results reveal an impressive accuracy of 98.78% for activity recognition and 98.65% for identity authentication, highlighting GrasOpen’s excellence in function and performance. The system’s security and robustness are validated across diverse authentication environments. © 2014 IEEE.

关键词： Biometrics

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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