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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Jiaxiu Liu, Xinyu Jia, Yingjuan Chen, Tong Huang, Jingru Zhang, Zheyuan Southwest University College of Electronic and Information Engineering Chongqing400715 China Southwest University Institute of Legal Psychology and Intelligent Computing Chongqing400715 China Southwest University Chongqing Key Laboratory of Non-Linear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9798350337488

Stress is an integral state that affects physical and mental health of individuals. Tissue Oxygen Saturation (StO2) is an emerging physiological signal and can reflect different stress states. Previous studies have simply extracted features of StO2 from specific regions of the face without exploring potential associations between them. In this paper, we analyze the facial Region of Interest (ROI) based on StO2, so as to explore the deep relationship between ROIs. Building upon this, we construct, for the first time, a StO2-based facial graph structure for stress recognition by using ROIs as nodes. In addition, we propose a new graph pooling method called FTPool which allows to measure node significance in terms of both node characteristics and graph topology. Finally, we further propose a dual-stream network (GCNet) combining GNN and CNN for the baseline-independent stress recognition. The experimental results show that the proposed GCNet can achieve state-of-the-art (SOTA) results with an accuracy of 78.57% on the original unbalanced database. © 2023 IEEE.

关键词： Graph pooling Graph structure Regions of Interest Stress recognition Tissue oxygen saturation

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A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification 6

A Fusion Shallow and Deep Features Network for Facial StO2 S...

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6th IEEE information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Xia, Haolin Sun, Sirui Liu, Xinyu Zhou, Ju Wang, Hanpu Chen, Tong School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

ISBN: (纸本)9781665460033

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data. © 2023 IEEE.

关键词： Feature extraction

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Recurrent Neural Network for nonconvex Economic Emission Dispatch

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 46-55页

作者： Jiayu Wang Xing He Junjian Huang Guo Chen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China Key Laboratory of Machine Perception and Children's Intelligence Development Chongqing University of EducationChongqing 400067China School of Electrical Engineering and Computer Science University of NewcastleNewcastleNSW 2308Australia

In this paper, an economic emission dispatch(EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network(RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization(PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.

关键词： Recurrent neural network(RNN) nonconvex economic emission dispatch optimization problem

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Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for linear and Set Constrained Convex Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1485-1498页

作者： You Zhao Xing He Mingliang Zhou Tingwen Huang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China School of Computer Science Chongqing UniversityChongqing 400044China Department of Mathematics Texas A&M University at QatarDoha 23874Qatar IEEE

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence *** previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization *** this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual *** satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence *** only requires assuming convexity of the objective *** validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.

关键词： Accelerated projection neurodynamic approach lin-ear and set constraints projection operators smooth and nonsmooth convex optimization time scaling.

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A brief survey on dynamical systems with saturated impulses

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Journal of the Franklin Institute 2025年第10期362卷

作者： Hongjuan Wu Chuandong Li Zhilong He School of Computer Science and Engineering Chongqing Three Gorges University Chongqing 404120 PR China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing 400715 PR China College of Mathematics and System Science Xinjiang University Urumqi 830017 PR China

In this paper, the research results on dynamical systems with saturated impulses (DSSI) are reviewed. We emphasize on the fundamental results, and the recent progress of the saturated impulse theories and the typical applications. Four aspects of saturated impulse are surveyed. That is, the common DSSI models with the important definitions and lemmas, the basic ideas for dealing with the saturated impulses, the estimation of the domain of attraction (DOA) for DSSI, and the typical applications of DSSI. The typical applications contain the design of hybrid controller for dynamical systems, the consensus of multi-agent systems (MASs), and the stability analysis of neural networks (NNs) and other networked systems. Finally, the conclusions and some future research directions are given.

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Row-Stochastic Matrices Based Distributed Optimization Algorithm with Uncoordinated Step-Sizes 6

Row-Stochastic Matrices Based Distributed Optimization Algor...

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6th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Li, Huaqing Wang, Jinmeng Wang, Zheng Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781728158570

This paper investigates the distributed optimization problem over multi-agent networks, in which the target of agents is to collaboratively optimize the sum of all local objective functions. Each local objective function is uniquely known by a single agent. We concentrate on the scenario where communication among agents is portrayed as directed graphs. Based on the exact first order method, a fully distributed optimization algorithm is proposed to deal with the optimization problem. The proposed algorithm utilizes row-stochastic matrices and uncoordinated step-sizes, which exactly drives all agents to converge to the global optimization solution. Under the assumptions that the global objective function is strong convex and the local objective functions have Lipschitz continuous gradient, we show that the proposed algorithm linearly converges to the global optimization solution as long as the maximum step-size of agents does not exceed an explicitly characterized upper bound. Finally, numerical experiments are presented to demonstrate the correctness of theoretical analysis. © 2019 IEEE.

关键词： Directed graphs

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Joint Global and Local Feature Learning Based on Facial StO2 for Stress Recognition 19

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

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

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

ISBN: (纸本)9798350346558

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. © 2022 IEEE.

关键词： Face recognition

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Stability of Inertial Delayed Neural Networks Via Event-Triggered Impulsive Control

Stability of Inertial Delayed Neural Networks Via Event-Trig...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Hong, Nan Zhang, Wei Zhang, Tianguang Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Department of Electronics and Information Engineering Chongqing China

ISBN: (纸本)9781665412872

This paper studies the stability of inertial delayed neural networks via event-triggered impulsive control (ETIC), where impulsive instants are determined by an effective Lyapunov-based impulsive event triggered mechanism (IETM). Based on the IETM, the Zeno behavior is excluded and ensures the globally exponential stability and h-stability of the neural networks by constructing Lyapunov function and some different restrictive conditions. In addition, the delay term in inertial neural networks is well dealt with the two lemmas given in this paper. It is shown that inertial delayed neural networks can be stabilized through this event-triggered impulsive control. Finally, two numerical examples and their simulations are carried out to verify the effectiveness of this method. © 2021 IEEE.

关键词： Time delay

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An adaptive malicious punishment over secure distributed estimation under attacks 4

An adaptive malicious punishment over secure distributed est...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Hua, Yi Hu, Limei Chen, Feng Department of Electronic and Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781538683392

In this paper, we propose an adaptive malicious punishment DLMS algorithm to mitigate the adversarial attack. Two types of attacks are proposed in the WSNs. The adaptive threshold is designed to detect attacks. Then corresponding weight of malicious nodes can be reduced, due to the punishing factor. The simulations demonstrate that the proposed algorithm is robust to the adversarial attacks. © 2018 IEEE.

关键词： Component Detecting malicious nodes DLMS The adaptive threshold The malicious attack The punishing weight

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A Distributed Dynamic Resource Allocation Strategy in A Large-scale Micro-grids Group 6

A Distributed Dynamic Resource Allocation Strategy in A Larg...

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6th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2019

作者： Fu, Zao He, Xing Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781728158570

In this paper, a dynamic economic dispatch problem (DEDP) with renewable energy generators is studied. With the purpose of minimizing the cost of DEDP and optimally allocate the load demand among the interconnected generators considering ramp-rate limits and generation limits, an effective continuous-time distributed algorithm based on interior point methods and primal-dual methods is proposed. In order to overcome the drawbacks of the renewable energy, a parallel system composed with flywheel energy storage systems and renewable energy generators is proposed to work in a constant power output mode. In addition, the using of switched systems makes the optimal solution more accurate. The excellent performance of the proposed algorithm is verified by IEEE 30-bus system with the REG based simulation. © 2019 IEEE.

关键词： Continuous time systems

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