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International Symposium on Computational Intelligence and Design, ISCID

作者： Jianguang Yuan Guangyuan Liu Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781509035595

Until recently, claims for the psychological benefits of physical exercise have tended to precede supportive evidence. Actually, emotional effects of exercise remain confusing, both positive and negative effects being reported. Meanwhile, several studies have shown that the aerobic exercise has impact on human's mood through making subjective evaluation of the Profile of Mood States (POMS) before and after exercise. However, the conclusion via comparing with the Profile of Mood States (POMS) scores was lack of the objective basis. In order to explain the result that exercise has effects of adjustment to fear emotion from the objective point of view, therefore, this study researched exercise which can improve mood based on the subjects' physiological signal, and analyzed data using the affective computing. Issues involved in this study are discussed.

关键词： Mood Physiology Time series analysis Affective computing Fatigue Computational intelligence nonlinear circuits

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TS-CAN+: A Improved TS-CAN Architecture for Non-Contact Heart Rate Measurement

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Li, Yihang Huang, Junjian Zhao, Jichao Wu, Dayu Zheng, Mao Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China University of Auckland Auckland New Zealand

Heart rate is one of the most crucial physiological indicators of the human body, which can reflect the health status of the cardiovascular and cerebrovascular. Therefore, heart rate monitoring in real-time is essential for the prevention and treatment of cardiovascular and cerebrovascular diseases. Remote Photoplethysmography (rPPG) is a non-contact heart rate measurement method, which addresses the limitation of traditional heart rate measurement methods that need to contact with the subjects, and has a broad application prospect in ward vital signs monitoring and remote physiological health monitoring. How to use the rPPG method to measure heart rate more accurately is still a challenging problem. In this paper, we propose the TS-CAN+ method, which is based on TS-CAN and further incorporates convolutional block attention modules and replaces the ordinary convolution of the appearance branch with the depthwise separable convolution to improve the network accuracy. To validate the performance of our model, we conduct extensive experiments on the public PURE, UBFC-rPPG, and MMPD datasets. The experimental results indicate that compared with TS-CAN, the mean absolute error of TS-CAN+ is reduced by 37.21% in the cross-dataset test on UBFC-rPPG dataset and reduced by 12.18% on MMPD dataset. IEEE

关键词： Convolution

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

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3rd International Conference on Bioinformatics and intelligent Computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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A Distributed Dual Proximal Algorithm for Non-Smooth Composite Constrained Optimization and Its Application

A Distributed Dual Proximal Algorithm for Non-Smooth Composi...

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第40届中国控制会议

作者： Liang Ran Jinhui Hu Hongli Liu Huaqing Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest University

Distributed optimization is a promising prospect that shows high potential in various applications, such as machine learning, power systems and others. This paper studies a class of constrained composite optimization problems over a network of agents, in which the local cost functions that are privately maintained by agents can be split into a strongly convex term and a convex（or possibly non-smooth） one. To tackle this problem in a distribute manner,a class of synchronous distributed algorithms based on dual proximal methods（Syn Dis-Du Pro） is presented, and its asynchronous version（Asyn Dis-Du Pro） without the global clock is developed as well. Under the proposed schemes, each agent in the network not only possesses the individual step-sizes, but also conducts local computation and communication without leaking its private information. In addition, the practicability and effectiveness of the proposed algorithms are demonstrated by the simulations on distributed energy resources coordination（DERC） based on power systems.

关键词： Convex optimization multi-agent systems distributed algorithms non-smooth constrained optimization problems

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Joint Global and Local Feature Learning Based on Facial StO2 for Stress Recognition

Joint Global and Local Feature Learning Based on Facial StO2...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Dong Chen Xinyu Liu Tong Chen Dairong Peng Jiaxiu Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University

Stress is an emotional state that is inevitable in social life, it is of great importance to accurately identify different types of stress. In this paper, we use human facial tissue oxygen saturation (StO2) to identify four states of an individual’s baseline, emotional stress, high-intensity physical stress and low-intensity physical stress. For the stress classification, we proposed a network called GLNet that combines global and local StO2 features. Specifically, GLNet learns local features from facial regions that provide the effective stress information and global features from the whole face. Then, the two features are fused at decision-level and classified. In addition, a new data augmentation method was proposed to address the data imbalance, which generates new data by constructing a mixed network called MixNet that fuses the depth features of multiple data. Experimental results show that MixNet can effectively alleviate the problem of data imbalance and GLNet combined with the data expanded by MixNet achieved the best performance compared to previous methods of StO2 stress recognition.

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Kernel least mean square algorithm with mixed kernel

Kernel least mean square algorithm with mixed kernel

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Qitang Sun Lujuan Dang Wanli Wang Shiyuan Wang College of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

This paper presents a novel kernel least square algorithm with mixed kernel (KLMS-MK) to improve the filtering performance of kernel least mean square (KLMS). By applying the convex combination method to the kernel function in KLMS, KLMS-MK bears the advantages of both the Gaussian kernel and the Laplace kernel. In KLMS-MK, the mixed parameter for the convex combination is updated with the stochastic gradient descent. Therefore, the steady-state mean square error (MSE) and the convergence rate are improved by KLMS-MK, simultaneously. Simulation results on chaotic time series prediction and nonlinear regression validate the excellent performance of KLMS-MK from the aspects of the convergence rate and estimation accuracy.

关键词： Kernel Testing Signal processing algorithms Gaussian noise Convergence Approximation algorithms Time series analysis

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Centroid-Based Security Monitoring for Indoor Elderly Care with Millimeter Wave Radar

Centroid-Based Security Monitoring for Indoor Elderly Care w...

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第43届中国控制会议

作者： Chicheng Huang Chuandong Li Zelin Gao Zhenyu Wu Yawei Shi Huaqing Li College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The increasing use of millimeter wave radar for smart elderly care is due to its precision,privacy,and *** technology enables real-time monitoring of unexpected incidents,particularly falls among the elderly *** examining the relationship between cloud points and human centroids,this paper exploits the unique features of millimeter wave radar for personnel detection and human activity *** improved Kalman filter algorithm for centroid tracking has been *** effectively monitors the three-dimensional trajectory and velocity changes associated with various human ***,machine learning algorithms and neural networks are employed to classify these behaviors,including walking,sitting,and *** to conventional Kalman filtering,this approach effectively handles dispersion characteristics within point clouds,resulting in a smoother trajectory and more consistent *** method enables the recognition of abnormal activities of people indoors with a maximum classification accuracy of 93.02%.

关键词： Millimeter Wave Radar Point Clouds Centroid Tracking Human Activity Recognition

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A Multiband Frequency Reconfigurable Antenna for Wireless Communication

A Multiband Frequency Reconfigurable Antenna for Wireless Co...

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第43届中国控制会议

作者： Rongkai Liang Chuandong Li Youxun Su Weiwei Liang Menggang Chen Zhen Luo College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In order to enhance the adaptability of wireless devices across diverse applications,it becomes imperative for an antenna to embody a multitude of functionalities,including the capacity to dynamically reconfigure the operating frequency,polarization mode,and radiation *** paper introduces a frequency-reconfigurable antenna that incorporates an L-U shaped slot band with a Split-Ring Resonator(SRR).Fabricated on a 1.2 mm low-cost FR4 substrate using a partially grounded design approach,which measures 40 x 35 *** manipulating the bias states of the two PIN diodes,the radiation element current and field distributions can be altered,resulting in reconfigurable bands spanning 2.45 GHz,3.3 GHz,4.5 GHz,7.4GHz,and 9.4 GHz,with an expansive Ultra-Wide Bandwidth(UWB) of 4230 *** antenna design caters to a range of wireless applications,including WLAN(Wireless Local Area Network),WiMAX(Worldwide Interoperability for Microwave Access),Remote Radio,and X-band Satellite *** meticulous simulation optimization,four reconfiguration scenarios are thoroughly examined by presenting and analyzing the simulation results.

关键词： Frequency Bandwidth Reconfigurable Multi-Band Antenna Ultra Wide Band Antenna PIN Diode

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Model-Free Reinforcement Learning Economic Dispatch Algorithms for Price-Based Residential Demand Response Management System

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 123-135页

作者： Li, Jun Li, Huaqing Huang, Tingwen Zheng, Lifeng Ji, Lianghao Yin, Shen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Texas A&M University at Qatar Doha Qatar Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Mechanical and Industrial Engineering Faculty of Engineering Norwegian University of Science and Technology Trondheim7033 Norway

It is projected that plug-in electric vehicles (PEVs) would steadily increase as household appliances. However, PEVs’ high power consumption, stochastic usage patterns, and storage capacity will surely result in a rise in the elasticity of demand response and pose significant difficulties for price-based residential demand response management (PRDRM). This artcle aims to optimize a two-tier globally shared nonconvex PRDRM problem with local constraints and PEVs, known as social welfare: maximizing retailer profits and minimizing the combined residential costs. This is done by balancing residential electricity use with retail electricity prices in an unknown market environment. The proposed online/offline model-free reinforcement learning-based economic dispatch (MFRL-ED) methods can adaptively decide on the ideal retail price sequence by integrating the daily residential-retailer behavior model with the agent-environment interaction method, providing a basic MFRL-ED solution for PRDRM without a system identification step and an accurate load-retail model. Experiments show that MFRL-ED methods provide an effective class of PRDRM solutions. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Reinforcement learning

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Event-Triggered Adaptive Finite-Time Control for Switched Cyberphysical Systems With Uncertain Deception Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 136-146页

作者： Wang, Xin Zhou, Yuhao Luo, Biao Jiang, Yuming Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Automation Central South University Changsha410083 China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7491 Norway T&M University at Qatar Doha23874 Qatar

This article is concerned with the event-triggered adaptive finite-time control problem for a class of switched cyber-physical systems (CPSs) subject to uncertain deception attacks. We deploy the finite time command filter to alleviate the possible explosion of complexity rendered by the virtual control signal’s duplicative differentiation. To stabilize the target system with a guaranteed disturbance rejection performance index, a disturbance observer is constructed by using fuzzy logic systems (FLS) with the hope of estimating the unknown external disturbance. Furthermore, the Nussbaum gain is utilized to resist the unknown control efficiency generated by uncertain deception attacks. Based on the common Lyapunov function (CLF) method, sufficient conditions are derived to ensure that all signals in the closed-loop system are uniformly bounded at a finite time. Finally, the simulation example shows the validity and efficacy of the developed control method. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Closed loop systems

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