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检索条件"机构=Chongqing Key Laboratory of Non-Linear Circuits and Intelligent Information Processing"

共 177 条记录，以下是101-110 订阅

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Robust Multi-kernel Generalized Maximum Correntropy Filters

Robust Multi-kernel Generalized Maximum Correntropy Filters

引用

IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Qiaona Sun Tao Zhang Xuewei Huang Shiyuan Wang College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

The traditional multi-kernel filtering algorithms are proposed by combining multiple kernel with the second similarity measure. Nevertheless, these multi-kernel filters suffer from both heavy computation burden and divergence for impulsive noise environments. To deal with these challenges for online applications, the multi-kernel generalized maximum correntropy (MKGMC) algorithm is developed for improving both precision and stability under the generalized maximum correntropy criterion. To alleviate the complex network structure of MKGMC, using the vector quantization strategy in MKGMC develops quantized MKGMC (QMKGMC) to reduce the computational and spatial complexity. From the viewpoints of precision and stability, the proposed algorithms outperform other typical compared algorithms on time series prediction.

关键词： Vector quantization Stability criteria Time series analysis Filtering algorithms Predictive models Prediction algorithms Robustness

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Row-Stochastic Matrices Based Distributed Optimization Algorithm With Uncoordinated Step-Sizes

Row-Stochastic Matrices Based Distributed Optimization Algor...

引用

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

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

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

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.

关键词： Distributed optimization directed graphs uncoordinated step-sizes row-stochastic matrices directed graphs Objective function Optimization algorithms Agents Global optimization Dyscoordination NETWORKING AGENT Distribute matrices

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Recognition of Social Anxiety through the Analysis of BVP Volume Series

Recognition of Social Anxiety through the Analysis of BVP Vo...

引用

International Symposium on Computational Intelligence and Design, ISCID

作者： Guohong Zheng Guangyuan Liu Hui Hu Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781509035595

Social anxiety (SA) is highly prevalent in the general population, usually causing the impairment of social function and bringing mental and physical tortures to people. Accurate recognition of SA is significant to help people alleviate their anxiety through appropriate ways of emotion regulation. This paper presents the blood volume pulse (BVP) volume series for the recognition of SA. 30 subjects' high anxiety state and low anxiety state are respectively induced by impromptu speaking with 21 audience and without audience. The mean and the weighted Krawtchouk moments are extracted as the features. Two sample t-test shows that the higher degree of anxiety, the smaller mean of the BVP volume series. Support vector machines and leave-one-subject-out cross validation are used for the recognition of SA. The result shows that the recognition accuracy of high anxiety state and low anxiety state are 90% and 86.67%, respectively.

关键词： Physiology Public speaking Feature extraction Emotion recognition Support vector machines Sociology Statistics

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A Novel Distributed Algorithm to Seek GNE for Aggregative Games via Primal-Dual Proximal Gradient

A Novel Distributed Algorithm to Seek GNE for Aggregative Ga...

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International Conference on Control, Automation and Robotics (ICCAR)

作者： Zhe Li Huaqing Li Liang Ran Songyang Li Lvming Fan Lifeng Zheng Jun Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

In this paper, the generalized Nash equilibria (GNE) problems over the aggregative game is concerned inspired by the widely application of non-cooperative game in many fields. In the game, each agent aims to minimize its own objective function that depends on both its private decision and the average decision information of all agents’. Furthermore, decision variables are within feasible decision sets and shares a global coupling inequality constraint. To seek the GNE, a novel distributed primal-dual proximal algorithm is proposed. Meanwhile, the proposed algorithm adopts edge-based communication model and takes uncoordinated step-sizes that avoid the conservativeness. By the theories of monotone inclusion and averaged operators, convergence of the algorithm is proved. Finally, numerical studies on electric vehicles path and destination strategy problem are conducted to clarify the efficiency of the proposed algorithm.

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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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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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Facial Expression Recognition of Public Speaking Anxiety

Facial Expression Recognition of Public Speaking Anxiety

引用

International Symposium on Computational Intelligence and Design, ISCID

作者： Fuli Huang Wanhui Wen Guangyuan Liu Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (纸本)9781509035595

Public Speaking Anxiety (PSA) is one of the most universal subtypes of social anxiety, and the facial expression recognition of PSA is an immediate area of research focus. The experiment obtained the facial expression data of 18 postgraduates in their thesis defense for the master's degree. Then, valid data were selected by using self-evaluation of the subject and the average evaluation of 5 audience. Next, the paper used Active Shape Model (ASM) algorithm for feature extraction of 68 feature points in 5 facial zones. Moreover, the paper applied support vector machine algorithm to recognize the social anxiety state according to the displacements of the feature points, and backward selection (BS) algorithm was used to find out the most important features in recognition. The result illustrated the validity of the method and obtained the best correct rate of 99% in the recognition of social anxiety state. Meanwhile, the maximum displacement zone in social anxiety state is jaw, and followed by mouth, eyebrow, eyes and nose. Obvious individual differences were found in the formation of anxious facial expression.

关键词： Mouth Feature extraction Public speaking Face Speech Nose Face recognition

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Evolution of Facial Tissue Oxygen Saturation and Detection of Human Physical Stress

Evolution of Facial Tissue Oxygen Saturation and Detection o...

引用

Image processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Xinyu Liu Xiao Xiao Ranlei Cao Tong Chen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

Physical stress has a direct impact on human health, which makes the detection of physical stress necessary in daily life. In this paper, we extracted tissue oxygen saturation (StO2) by hyperspectral imaging to detect and distinguish physical stress from relaxation state remotely. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased and decreased comparing to the baseline, the StO2 after two minutes recovered towards the baseline. This changes of StO2 is reported for the first time. Based on the findings, we also located some useful ROIs on face for the detection of human physical stress.

关键词： physical stress stress detection tissue oxygen saturation hyperspectral imaging physical stress hyperspectral imagery human health Oximetry Face

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Community Energy Management System Based on Deep Learning and Lagrangian Programming Neural Network

Community Energy Management System Based on Deep Learning an...

引用

Neuromorphic Computing (ICNC), International Conference on

作者： Shuying Xu Xing He Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

In this paper, a innovative approach is proposed for solving the energy scheduling problem (ESP) in community energy management system (CEMS), combining deep learning with lagrangian programming neural network (LPNN). First, The LPNN is employed to address the ESP, and its effectiveness is thereafter validated. Subsequently, a deep learning-based LPNN (DLPNN) model accompanied by a specially designed loss function is introduced. This enables the trained DLPNN to solve the ESP in CEMS without the need for repeated steps, which is a limitation of using LPNN alone. Additionally, the dynamic variables in the ESP are incorporated as input variables for training the DLPNN, allowing the model to determine optimal solutions for varying values of these variables. Ultimately, the effectiveness of the introduced approach is confirmed through simulation experiments.

关键词： Deep learning Training Processor scheduling Computational modeling Neural networks Urban areas Programming Dynamic scheduling Dynamic programming Energy management systems

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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...

引用

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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