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Recurrent Neural Network for Nonconvex Economic Emission Dispatch

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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Memristor-based hyper-chaotic circuit for image encryption

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Chinese Physics B 2020年第11期29卷 259-270页

作者： Jiao-Jiao Chen Deng-Wei Yan Shu-Kai Duan Li-Dan Wang College of Electronic and Information Engineering Southwest UniversityChongqing 400715China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest UniversityChongqing 400715China Intelligent Transmission and Control Technology Joint Engineering Laboratory Chongqing 400715China Brain-inspired Computing and Intelligent Control Chongqing Key Laboratory Chongqing 400715China Chongqing Collaborative Innovation Center for Brain Science Chongqing 400715China School of Artificial Intelligence Southwest UniversityChongqing 400715China

The memristor is a kind of non-linear element with memory function,which can be applied to chaotic systems to increase signal randomness and *** this paper,a new four-dimensional hyper-chaotic system is designed based on a flux controlled memristor model,which can generate complex chaotic *** basic dynamic theory analysis and numerical simulations of the system,such as the stability of equilibrium points,the Lyapunov exponents and dimension,Poincare maps,the power spectrum,and the waveform graph prove that it has rich dynamic ***,the circuit implementation of this system is *** consistency of simulation program with integrated circuit emphasis(SPICE)simulation and numerical analysis proves the ability to generate ***,a new image encryption scheme is designed by using the memristor-based hyper-chaotic system proposed in this *** scheme involves a total of two *** using different security analysis factors,the proposed algorithm is compared with other image encryption schemes,including correlation analysis,information entropy,*** results show that the proposed image encryption scheme has a large key space and presents a better encryption effect.

关键词： memristor SPICE simulation hyper-chaotic system image encryption

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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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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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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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Evolution of facial tissue oxygen saturation and detection of human physical stress

Evolution of facial tissue oxygen saturation and detection o...

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2020 Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2020

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

ISBN: (纸本)9781728160665

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. © 2020 IEEE.

关键词： Hyperspectral imaging

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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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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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Effects of acute moderate intensity exercise on emotion based on beta power in EEG 3

Effects of acute moderate intensity exercise on emotion base...

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3rd International Conference on Computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th International Conference on information and Multimedia Technology, ICIMT 2019

作者： Gao, Pengfei Long, Zhengji Xiao, Zhangyan Yang, Fu Zhang, Jihai Liu, Guangyuan School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Processing Chongqing China

ISBN: (纸本)9781450376273

According to previous studies, high anxiety leads to an increase in beta power, while pleasure and relaxation of emotion reduces beta power. In this experiment, the International Standard Picture Stimulation Library(IAPS) was used to induce positive and negative emotions of subjects, and EEG signals were collected when subjects were looking at pictures. Comparing the beta power of positive and negative EEG induced by before and after exercise, we found that the power of beta stimulated by positive and negative pictures before exercise is generally greater than that after exercise. It indicates that subjects were happier when facing positive stimuli than before exercise, and less negative emotions were produced when facing negative stimuli than before exercise. In short, acute moderate intensity exercise can improve people's negative emotion in the face of negative stimuli. The purpose of this study is to explore the mechanism of the effect of acute moderate intensity exercise on emotion by analyzing beta power in late positive potential (LPP). © 2019 Association for Computing Machinery.

关键词： Facings

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Random Fourier Features Based Kernel Risk Sensitive Loss Algorithm with Adaptive Moment Estimation Optimization Technology 22

Random Fourier Features Based Kernel Risk Sensitive Loss Alg...

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6th International Conference on Digital Signal processing, ICDSP 2022

作者： Wang, Daili Zheng, Yunfei Cai, Peng Liu, Qiang Shen, Minglin Wang, Shiyuan Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University China

ISBN: (纸本)9781450395809

Random Fourier features based kernel risk sensitive loss (RFFKRSL) is a popular nonlinear adaptive filtering algorithm developed in the random Fourier features space. The most attractive feature of such algorithm is that it would not cause the issue of linearly increasing network structure like the well-known kernel adaptive filtering algorithms, while having the ability to curb the negative influence induced by non-Gaussian noises. The stochastic gradient descent (SGD) method, however, is a default choice to determine the filtering coefficients of RFFKRSL, which can result in an undesirable convergence performance of the algorithm in many cases. To address this issue, two alternative optimization technologies, including adaptive moment estimation (Adam) and its extended version, i. e., Nesterov-accelerated Adam (Nadam), have been adopted to re-derive RFFKRSL. For simplicity, the proposed two algorithms are named as RFFKRSL with Adam (AdamRFFKRSL) and RFFKRSL with Nadam (NadamRFFKRSL), respectively. Although Adam and Nadam are common to deep neural network based learning methods, their applications to adaptive filtering are seldom to be seen, and the combination of them with RFFKRSL may open a new way to design nonlinear adaptive filtering algorithms that have been built with SGD method. Simulations on two time series prediction tasks are reported to demonstrate the desirable performance of the proposed algorithms. © 2022 ACM.

关键词： Stochastic systems

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