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Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

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arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

Deception in Differential Games: Information Limiting Strategy to Induce Dilemma

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arXiv 2024年

作者： Shishika, Daigo Moll, Alexander Von Maity, Dipankar Dorothy, Michael Department of Mechanical Engineering George Mason University FairfaxVA22030 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States Department of Electrical and Computer Engineering University of North Carolina CharlotteNC28223 United States Army Research Directorate DEVCOM Army Research Laboratory APG MD20783 United States

Can deception exist in differential games? We provide a case study for a Turret-Attacker differential game, where two Attackers seek to score points by reaching a target region while a Turret tries to minimize the score by aligning itself with the Attackers before they reach the target. In contrast to the original problem solved with complete information, we assume that the Turret only has partial information about the maximum speed of the Attackers. We investigate whether there is any incentive for the Attackers to move slower than their maximum speed in order to "deceive" the Turret into taking suboptimal actions. We first describe the existence of a dilemma that the Turret may face. Then we derive a set of initial conditions from which the Attackers can force the Turret into a situation where it must take a guess. © 2024, CC BY.

关键词： Game theory

Optimal Evasion from a Sensing-Limited Pursuer

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arXiv 2024年

作者： Maity, Dipankar Von Moll, Alexander Shishika, Daigo Dorothy, Michael The Department of Electrical and Computer Engineering University of North Carolina at Charlotte NC28223 United States The Control Science Center Air Force Research Laboratory WPAFB OH45433 United States The Department of Mechanical Engineering George Mason University FairfaxVA22030 United States The Army Research Directorate DEVCOM Army Research Laboratory APG MD20783 United States

This paper investigates a partial-information pursuit evasion game in which the Pursuer has a limited-range sensor to detect the Evader. Given a fixed final time, we derive the optimal evasion strategy for the Evader to maximize its distance from the pursuer at the end. Our analysis reveals that in certain parametric regimes, the optimal Evasion strategy involves a ‘risky’ maneuver, where the Evader’s trajectory comes extremely close to the pursuer’s sensing boundary before moving behind the Pursuer. Additionally, we explore a special case in which the Pursuer can choose the final time. In this scenario, we determine a (Nash) equilibrium pair for both the final time and the evasion strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

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arXiv 2023年

作者： Wang, Qi Chen, Li Zhan, Zhiyuan Zhang, Jianhua Yin, Zhong The Department of Control Science and Engineering the School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The OsloMet Artificial Intelligence Lab Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway The Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website: https://***/7ohnKeats/SCVCNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

Communication-Efficient Personalized Federated Learning for Green Communications in IoMT

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Communication-Efficient Personalized Federated Learning for ...

IEEE International Conference on Communications (ICC)

作者： Ziqi Chen Jun Du Chunxiao Jiang Yunlong Lu Zhu Han Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. Simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

关键词： Training Adaptation models Data privacy Costs Accuracy Federated learning Computational modeling

A Method for Recognition of Mixed Gas Composition Based on PCA and KNN 19

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A Method for Recognition of Mixed Gas Composition Based on P...

19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Xia, Wanyu Song, Tingting Yan, Zhanwei Song, Kai Chen, Deyun Chen, Yinsheng National Experimental Teaching Demonstration Center for Measurement and Control Technology and Instrumentation Harbin University of Science and Technology Harbin150080 China Postdoctoral Research Station of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9781665424462

In the mining process of coal, oil, and natural gas, there are often a large number of toxic, hazardous and explosive gases, which pose a greater safety hazard. These gases usually exist in the form of mixtures. Accurately identifying the composition of the mixed gas is of great significance to the prevention of safety accidents. In order to improve the accuracy of the component recognition of the mixed gas, this paper proposes a mixed gas component recognition algorithm based on the combination of principal component analysis (PCA) and k-nearest neighbor algorithm (KNN). PCA is used to extract the characteristics of the sensor array signal to obtain the characteristic value of the gas, and then KNN is used to realize the recognition of the gas type. The results show that the recognition rate of the feature quantity after dimensionality reduction as input is higher than that before dimensionality reduction. Finally, PCA and KNN algorithm and PCA and support vector machine (SVM) algorithm are compared for the recognition rate of mixed gas. Experimental results show that the proposed method has a recognition rate of 96.88% for mixed gas components. © 2021 IEEE.

关键词： Support vector machines

Bis-benzylisoquinoline alkaloids inhibit flavivirus entry and replication by compromising endolysosomal trafficking and autophagy

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Virologica Sinica 2024年第6期39卷 892-908页

作者： Lihong Huang Lele Liu Junhai Zhu Nanjun Chen Jie Chen Chuen-Fuk Chan Fei Gao Youqin Yin Jiufeng Sun Rongxin Zhang Kehui Zhang Wenbao Qi Jianbo Yue State Key Laboratory for Animal Disease Control and Prevention South China Agricultural UniversityGuangzhou510642China Key Laboratory of Zoonoses Ministry of Agriculture and Rural AffairsSouth China Agricultural UniversityGuangzhou510642China National and Regional Joint Engineering Laboratory for Medicament of Zoonoses Prevention and Control Guangzhou510642China Key Laboratory of Zoonoses Prevention and Control of Guangdong Province GuangzhouChina Department of Computer Science City University of Hong KongHong Kong999077China Department of Biomedical Sciences City University of Hong KongHong Kong999077China Guangdong Workstation for Emerging Infectious Disease Control and Prevention Guangdong Provincial Key Laboratory of Pathogen Detection for Emerging Infectious Disease ResponseGuangdong Provincial Center for Disease Control and PreventionGuangzhou511430China Laboratory of Immunology and Inflammation Institute of Basic Medical Sciences and Department of BiotechnologySchool of Life Sciences and BiopharmaceuticsGuangdong Pharmaceutical UniversityGuangzhou510006China State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia MedicaChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100730China Beijing Key Laboratory of Active Substances Discovery and Druggability Evaluation Institute of Materia MedicaChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100730China City University of Hong Kong Shenzhen Research Institute Shenzhen518057China Division of Natural and Applied Sciences Synear Molecular Biology LabGlobal Health Research CenterDuke Kunshan UniversityKunshan215316China College of Life Sciences Wuhan UniversityWuhan430072China

Flaviviruses,such as dengue virus(DENV),Zika virus(ZIKV),and Japanese encephalitis virus(JEV),represent a substantial public health challenge as there are currently no approved treatments ***,we investigated the antiviral effects of bis-benzylisoquinoline alkaloids(BBAs)on flavivirus *** evaluated five specific BBAs—berbamine,tetrandrine,iso-tetrandrine,fangchinoline,and cepharanthine—and found that they effectively inhibited infections by ZIKV,DENV,or JEV by blocking virus entry and genome replication stages in the flavivirus life ***,we synthesized a fluorophore-conjugated BBA and showed that BBAs targeted endolysosomes,causing lysosomal pH *** studies on inhibiting ZIKV infection by BBAs revealed that these compounds blocked TRPML channels,leading to lysosomal dysfunction and reducing the expression of NCAM1,a key receptor for the entry of ZIKV into cells,thereby decreasing cells susceptibility to ZIKV ***,BBAs inhibited the fusion of autophagosomes and lysosomes,significantly reducing viral RNA ***,our results suggest that BBAs inhibit flavivirus entry and replication by compromising endolysosomal trafficking and autophagy,respectively,underscoring the potential of BBAs as therapeutic agents against flavivirus infections.

关键词： Bis-benzylisoquinoline alkaloids(BBAs) Flavivirus Zika virus(ZIKV) Endolysosomes TRPML channels Autophagy

A Gas Recognition Method Based on PCA and PSO-LSSVM 19

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A Gas Recognition Method Based on PCA and PSO-LSSVM

19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Song, Tingting Xia, Wanyu Yan, Zhanwei Song, Kai Chen, Deyun Chen, Yinsheng National Experimental Teaching Demonstration Center for Measurement and Control Technology and Instrumentation Harbin University of Science and Technology Harbin150080 China Postdoctoral Research Station of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9781665424462

Gases in the real environment always exist in the form of mixtures, and effective identification of gas types to reduce the occurrence of safety incidents has become an important direction in the field of gas analysis research. This paper proposes a mixed gas identification method based on PCA and PSO-LSSVM. This method uses principal component analysis (PCA) to reduce the dimensionality of the sensor array output signal, and uses the Relief algorithm to select data. The particle swarm algorithm is used to iteratively optimize the relevant parameters in the least squares support vector machine model, and the PSO-LSSVM model is constructed to qualitatively analyze the composition of the mixed gas. This article uses a public data set of mixed gases of ethylene, methane and carbon monoxide to conduct experiments. The experimental results show that the method used in this paper can effectively identify the type of mixed gas, and the recognition accuracy rate reaches 91.67%. The method proposed in this paper can provide a research foundation for the follow-up analysis of the mixed gas concentration. © 2021 IEEE.

关键词： Particle swarm optimization (PSO)