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2nd International Symposium on Sensor Technology and Control, ISSTC 2023

作者： Wang, Zhiyun Chu, Mengyang Su, Yahui Guo, Xiaohui Liu, Rui Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Electronic and Information Engineering Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Integrated Circuits Hefei China Anhui Xinhuai Electronics Co. Ltd Huaibei China

ISBN: (纸本)9798350314465

Flexible capacitive pressure sensors are optimal for wearable electronic equipment owing to their advantages of simplicity in architecture and low power consumption. However, fabricating a bionic flexible capacitive pressure sensor with high sensitivity and a wide dynamic range of detection is still a great challenge. In this article, we propose a flexible pressure sensor based on femtosecond laser ablation with a treefrog suckers bionic microstructure. The treefrog suckers capacitive pressure sensor (TSCPS) has high sensitivity (0-10 kPa, 0.0762 kPa-1), wide detection range (500 kPa), high repeatability (>12000 s at 10 kPa), and fast response time (62.5 ms). For practical applications, the TSCPS was positioned on the wrist, palm, and sole of the hand to detect the movements of each part accordingly. The experimental findings indicate that the TSCPS has broad promising applications in bionic wearable products such as human locomotion monitoring. © 2023 IEEE.

关键词： Pressure sensors

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Remote Detection and Classification of Human Stress Using a Depth Sensing Technique

Remote Detection and Classification of Human Stress Using a ...

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Asian Conference on Affective Computing and intelligent Interaction (ACII Asia)

作者： Yuhao Shan Tong Chen Liansheng Yao Zhan Wu Wanhui Wen Guangyuan Liu Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781538653128

Stress plays an important role in our daily life. Long-term's psychological stress will lead to serious health as well as social problems, it is important to detect and monitor the psychological stress in its early stage. Most existing stress detection equipment are contact-type, such as wrist strap. However, in a real application, such as a working environment, a contact-free stress detection system will bring greater convenience. In this paper, we proposed a novel framework for detecting and classifying human stress based on respiratory signals measured remotely by using a Kinect sensor with a detection range of 3 meters. We test the framework on respiratory signals data set from 20 individuals under 3 different tasks (listen relax music, do exercise and do Stroop Color-word test), corresponding to relaxation, physical stress and psychological stress state. Experimental results suggest that the proposed method is a promising way for monitoring human stress and even discriminating psychological stress from the physical stress.

关键词： human computer interaction medical signal processing patient monitoring psychology medical signal processing computer human interaction Psychological Stress Patient Monitoring Psychology Stress (mechanics) physical stress strapping of wrist Stressing telemetry Sensing Techniques Social Problems

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Distributed Model Predictive Control Framework for Consensus of Multi-Agent System

Distributed Model Predictive Control Framework for Consensus...

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IEEE International Conference on Mechatronics and Automation

作者： Huaqing Li Lifeng Zheng Huiwei Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University No. 2 Tiansheng Road Beibei District Chongqing China

Considering the widespread applications of the model predictive control (MPC), such as industrial process control and robotic operations, this paper proposes an MPC-based asynchronous distributed algorithm to solve the consensus problem of multi-agent system (MAS) by using the operator splitting method. The proposed algorithm is asynchronous, which means that all agents do not require to update and communicate simultaneously. When the positive uncoordinated step sizes satisfy the given condition, the optimal solution can be worked out by using the proposed algorithm. Moreover, all agents are allowed to set the step sizes locally based on local information. Numerical simulation validates the feasibility and effectiveness of the proposed algorithm.

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Reliability Analysis of Optical Neural Networks with Non-Ideal Signal Transmission

SSRN

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SSRN 2024年

作者： Fu, Pengju Xie, Yiyuan Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China

Machine learning plays a significant role in various fields. As a fundamental part of machine learning, matrix operations are one of the most important computational parts of neural networks. However, the advancement of Moore’s law is hindered by limitations in the number and size of transistors on electronic chips. Therefore, the implementation of optical neural network (ONN) using photonic devices to accomplish matrix-vector multiplication (MVM) has attracted significant attention. Compared with traditional electrical on neural networks, ONN exhibits faster computational speed, higher parallel input capability and greater energy efficiency. Nevertheless, crosstalk and signal loss are inevitable characteristics in optical signal transmission, which can degrade the performance of ONN. In this paper, we present analysis models and simulations to assess the effects of crosstalk and loss on optical computing systems. We have observed that the accuracy of ONN degrades when crosstalk and loss are present. In severe cases, ONN is unable to complete the classification task. To mitigate the interference of crosstalk, we have designed a novel double-layer structure to reduce the number of optical devices required for signal transmission and optimize the impact of crosstalk on ONN performance. For the identical classification task, the ONN based on a double-layer structure exhibits better interference immunity than the generic ONN, with a 10% performance improvement. In particular, performance is improved by 50% when only crosstalk is considered. © 2024, The Authors. All rights reserved.

关键词： Crosstalk

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Reliability Analysis of Optical Neural Networks with Non-Ideal Signal Transmission

SSRN

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SSRN 2024年

作者： Xie, Yiyuan Fu, Pengju Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China

关键词： Crosstalk

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A recurrent neural network for optimal energy management considering the battery cycle-life in smart grid

A recurrent neural network for optimal energy management con...

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

作者： Miao Sun Xing He School of Microelectronics Fudan University Shanghai China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronics and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

This paper proposes a combined battery energy management (CBEM) model against the background of smart grid, which focus on the absorptive ability of Battery Energy Storage System (BESS) distinguishing to the traditional energy management. To solve the CBEM model, an one-layer recurrent neural network (RNN) is recommended. Furthermore, by considering the peak and valley periods division prices, the generators absorption and the distribution network bought from power grid are optimized. Meanwhile, the emission cost is considered in total cost and there is a corresponding constraint to control the emission and the outputs of generators. Finally, the effectiveness of the CBEM model is verified on Matlab. With the fluctuation of electrical price, the charge model and discharge model of BESS are showed. Results indicate that this model performs well in cutting the load in peak time and storage energy in valley time.

关键词： BESS cycle-life cost emission cost control recurrent neural network energy management recurrent neural nets Energy management Smart grids Power grids Battery Cycle Life energy storage battery energy storage system

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Identifying Strong Stress and Weak Stress Through Blood Volume Pulse

Identifying Strong Stress and Weak Stress Through Blood Volu...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Jialan Xie Wanhui Wen Guangyuan Liu Chuanwu Chen Jie Zhang Hong Liu School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Ciruits and Intelligent Processing

This paper explored the difference between weak stress(WS) and strong stress(SS) on the relative ejection period(REP) of the blood volume pulse(BVP). Features were extracted from the REP series to recognize WS and SS. The blood volume pulse data were acquired during the defense of 31 graduate students for their master's degree under the real stress environment. The result shows that, in the real stress situation, the features of REP have obtained a correct rate of 91.93% in distinguishing strong stress state and weak stress state.

关键词： blood volume pulse ejection period real situation strong stress weak stress

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Stress Recognition Based on the Triple-axis Acceleration

Stress Recognition Based on the Triple-axis Acceleration

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Hong Liu Wanhui Wen Guangyuan Liu Jie Zhang Jialan Xie School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Ciruits and Intelligent Processing

Strong and weak stress were elicited by real-scene thesis defense and pre-defending presentation of the thesis work, and tri-axial accelerometer data were acquired and analyzed for their ability to recognize strong and weak stress. Twenty-six subjects(7 females and 19 males) participated in the data acquisition experiment. A support vector machine classifier obtained a correct rate of 92.31% in the binary classification of the 26 subjects' tri-axial accelerometer data acquired during strong stress and weak stress.

关键词： emotion recognition tri-axial acceleration stress support vector machine

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Distributed Optimization for Energy Allocation in Mictrogrid Power System

Distributed Optimization for Energy Allocation in Mictrogrid...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent Systems

作者： Tiancai Wang Xing He Junjian Huang Miao Sun Yachen He Qin Huang Dongyang Feng Wenming Zhang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China Key laboratory of Machine Perception and Children's Intelligence Development Chongqing University of Education Chongqing China

ISBN: (纸本)9781538604915

In this paper, a continuous-time distributed algorithms is proposed to solve the energy allocation problem in microgrid power system. Projected output feedback method is employed in the approach to deal with the local capacity constraints. Each agent in the multi-agent network has its own local objective function, local bound constraint and can access the information from the agent nearby. The agents determine the optimal power generation in the responsible area to minimize the sum of local generation cost function while reaching the global supply and demand balance. Also, the distributed method is applied to test on a six-agent system and the simulation result demonstrates that the algorithm proposed can successfully obtain the optimal power generation and the auxiliary variables of each generator agent can always reach consensus.

关键词： Generators Optimization Resource management Distributed algorithms Power generation Microgrids Smart grids

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Secrecy Performance Analysis with Optimal DF Relay Selection of Underlay CR Networks over Nakagami-m Fading Channels

Secrecy Performance Analysis with Optimal DF Relay Selection...

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IEEE International Conference on Communications

作者： Huan Zhang Hongjiang Lei Imran Shafique Ansari Gaofeng Pan Zhi Ren Khalid A. Qaraqe Chongqing University of Posts and Telecommunications Chongqing Chongqing CN Department of Electrical and Computer Engineering Texas A&M University at Qatar Education City Doha Qatar Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China

ISBN: (纸本)9781467390002

In this paper, the secrecy outage performance of an underlay cognitive decode-and-forward (DF) relay network over independent and non-identical distributed (i.n.i.d) Nakagami-m fading channels is investigated, in which the secondary user transmitter communicates with the secondary destination via relays, and an eavesdropper attempts to overhear the information. We assume that the channel state information (CSI) of all links is available. The exact and asymptotic closed-form expressions for the secrecy outage probability with optimal relay selection are derived, and verified by Monte-Carlo simulations. Through asymptotic analysis, we find that the secrecy diversity order is determined by the number of relays and the fading parameters between relay and destination.

关键词： Relays Fading channels Cognitive radio Wireless sensor networks Communication system security Decoding

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