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arXiv 2022年

作者： Cheng, Bin Lv, Yuezu Li, Zhongkui Duan, Zhisheng Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200120 China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314000 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper studies the consensus control problem faced with three essential demands, namely, discrete control updating for each agent, discrete-time communications among neighboring agents, and the fully distributed fashion of the controller implementation without requiring any global information of the whole network topology. Noting that the existing related results only meeting one or two demands at most are essentially not applicable, in this paper we establish a novel framework to solve the problem of fully distributed consensus with discrete communication and control. The first key point in this framework is the design of controllers that are only updated at discrete event instants and do not depend on global information by introducing time-varying gains inspired by the adaptive control technique. Another key point is the invention of novel dynamic triggering functions that are independent of relative information among neighboring agents. Under the established framework, we propose fully distributed state-feedback event-triggered protocols for undirected graphs and also further study the more complexed cases of output-feedback control and directed graphs. Finally, numerical examples are provided to verify the effectiveness of the proposed event-triggered protocols. Copyright © 2022, The Authors. All rights reserved.

关键词： Directed graphs

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Motion Semantic Enhancement and Autonomous Information Mining for Static-Dynamic Visual Emotion Recognition in Human–Robot Interaction

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IEEE Transactions on Instrumentation and measurement 2025年 74卷

作者： Cheng-Shan Jiang Zhen-Tao Liu Edwardo F. Fukushima Jinhua She School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education and Engineering Research Center of Intelligent Technology for Geo-Exploration China University of Geosciences Wuhan China School of Engineering Tokyo University of Technology Hachioji Japan

The advancement of static-dynamic visual emotion recognition (DVER) plays a pivotal role in the evolution of intelligent and empathetic machines. Currently, the progress of static visual emotion recognition (SVER) faces challenges, primarily the need to balance model efficiency with robust recognition performance. Moreover, obstacles such as noisy data and limited labeled datasets restrict models from effectively learning the appearance features and dynamic dependencies intrinsic to DVER. In the realm of SVER, facial pixel, and semantic representation are derived using a lightweight surface and landmark features embedding network, followed by neuron-energy-based feature fusion-filtering to enhance the fusion of semantic representations positively. For DVER, a Global Anchor-Dependent Noisy Emotional Data Filtering (GADNEF) method is employed for noisy label learning (NLL), facilitating clip-wise filtering of ambiguous data via iterative computations of frame-wise attention statistics across batches. Furthermore, a self-supervised learning (SSL) paradigm based on a unified appearance and motion-guided masked autoencoder (UAMGMAE) is implemented, enabling large-scale knowledge transfer tailored for video-based facial expression analysis. Our approach has achieved accuracies of 97.07%, 98.90%, 61.39%, and 92.28% on SVER datasets including KDEF, RaFD, SFEW, and RAF-DB, respectively, while the weighted average recall (WAR) and unweighted average recall (UAR) on DVER datasets such as DFEW and MAFW were 75.12%, 64.37%, 55.26%, and 42.53%, respectively. A preliminary application experiment has also been conducted to validate the practical applicability of our method within human–robot interaction (HRI) scenarios.

关键词： Noise measurement Visualization Emotion recognition Semantics Human-robot interaction Filtering Feature extraction Training Neurons Transformers

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Stabilization of discrete time stochastic linear systems with time delay via event-triggered control

Stabilization of discrete time stochastic linear systems wit...

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Chinese Automation Congress (CAC)

作者： Jian Dong Xiaofeng Zong School of Automation( China University of Geosciences) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan People’s Republic of China

ISBN: (纸本)9781665426480

This paper presents the stabilization problem of a class of discrete-time stochastic linear systems with time delay using an event-triggered control(ETC) strategy. For the general linear systems, we propose a new event triggering mechanism and deduce sufficient conditions for mean square exponentially stabilizability(MSES) and almost sure exponentially stabilizability(ASES) via ETC. The results show that the stability of the system depends on the system parameters (time delay, noise, event trigger threshold parameter, and unstable characteristic value of the original system). When the feedback control is disturbed by low-intensity noise, the unstable system can be stable in the mean square. Finally, the validity of the theoretical results is illustrated through a simulation example.

关键词： Linear systems Sufficient conditions Automation Delay effects Stability analysis Feedback control

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Smart Infrared Temperature measurement System Based On Elastic Compute Service Platform

Smart Infrared Temperature Measurement System Based On Elast...

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Chinese Automation Congress (CAC)

作者： Yongbo Li Li Yuan School of Automation( China University of Geosciences) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan People’s Republic of China

ISBN: (纸本)9781665426480

In the fight against the novel coronavirus, this paper designs a smart infrared temperature measurement system based on the Elastic Compute Service platform. In the perceptual layer part of the Internet of Things(IoT), the system uses infrared temperature sensors to quickly collect the temperature of the forehead or arm of the human body and Uses the MCU to automatically transmits the measured data to the Elastic Compute Service through the WiFi module. In the network layer part of the Internet of Things,the data written into the database of the Elastic Compute Service through the program deployed on the Elastic Compute Service. In the application layer of the Internet of Things, the remote management terminal monitors the collected body temperature data in real-time and provides quick warnings. Finally, the User can log in to the applet on his mobile phone to quickly and easily obtain personal information.

关键词： Temperature measurement Temperature sensors Temperature distribution Forehead Databases Real-time systems Mobile handsets

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Spin–Orbit Torque Induced Magnetization Switching of Heusler Alloy Co2fesi for Neuromorphic Computing

SSRN

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SSRN 2022年

作者： Zhao, Jiale Zhang, Shuai Li, Wendi Dong, Kaifeng Wang, Ke Guo, Zhe School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical and Electronic Engineering East China University of Technology Nanchang330013 China

Spintronic devices have the advantages of high endurance and energy efficiency for nonvolatile memory and emerging neuromorphic computing applications, especially with the spin-orbit torque (SOT) driven magnetization switching manner. The Co-based Heusler alloy is potentially an ideal material for constructing spintronic devices due to its high spin polarization, high saturation magnetization, high Curie temperature and low damping. Here, we investigate the current induced magnetization switching of Heusler alloy Co 2 FeSi with good perpendicular magnetic anisotropy through SOT. Inspired by the gradual switching process, we further study the memristive behaviors of the device, which can be used as artificial synapse for neuromorphic computing. Both the pulse amplitudes and pulse numbers are exploited to modulate the resistance states. Based on the extracted synaptic characteristics, an artificial neural network is simulated, causing a high accuracy of 91.52%. Our work paves a way for the application of Heusler alloy in magnetic random-access memory and neuromorphic computing. © 2022, The Authors. All rights reserved.

关键词： Neural networks

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Distributed Leader-Following Impulsive Consensus of Nonlinear Multi-Agent systems with Partial Input Saturation

Distributed Leader-Following Impulsive Consensus of Nonlinea...

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Chinese Automation Congress (CAC)

作者： Jiejie Lan Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan P. R. China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing P. R. China School of Electrical Engineering and Automation Hubei Normal University Huangshi P. R. China

In this letter, leader-following consensus of a nonlinear MASs satisfying Lipschitz conditions with partial actuator saturation constraints is investigated First of all, an impulse control protocol which only requires local information of agents and their neighbors is proposed. Then, using Lyapunov stability theory, impulse control theory, and algebraic graph theory, sufficient conditions for the consensus of multi-agent systems are provided. Ultimately, numerical simulations are employed to show that the research findings are accurate.

关键词： Actuators Sufficient conditions Energy consumption Protocols Energy conservation Numerical simulation control systems

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Zero-Shot Object Goal Visual Navigation

arXiv

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arXiv 2022年

作者： Zhao, Qianfan Zhang, Lu He, Bin Qiao, Hong Liu, Zhiyong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China The Nanjing Artificial Intelligence Research of IA Jiangning District Jiangsu Nanjing211100 China The College of Electronic and Information Engineering Tongji University China

Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes predefined in the training stage. However, in real households, there may exist numerous target classes that the robot needs to deal with, and it is hard for all of these classes to be contained in the training stage. To address this challenge, we study the zero-shot object goal visual navigation task, which aims at guiding robots to find targets belonging to novel classes without any training samples. To this end, we also propose a novel zero-shot object navigation framework called semantic similarity network (SSNet). Our framework use the detection results and the cosine similarity between semantic word embeddings as input. Such type of input data has a weak correlation with classes and thus our framework has the ability to generalize the policy to novel classes. Extensive experiments on the AI2-THOR platform show that our model outperforms the baseline models in the zero-shot object navigation task, which proves the generalization ability of our model. Our code is available at: https://***/pioneer-innovation/Zero-Shot-Object-Navigation. © 2022, CC BY.

关键词： Robots

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TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving

arXiv

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arXiv 2022年

作者： Zhang, Qichao Gao, Yinfeng Zhang, Yikang Guo, Youtian Ding, Dawei Wang, Yunpeng Sun, Peng Zhao, Dongbin The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Baidu Inc. Beijing100085 China

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying on expensive and time-consuming real-world testing. Existing simulators rely on heuristic-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios. To bridge the gap between simulation and the real world, we propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic behaviors directly from human demonstration. In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage. In the first stage, we propose a novel auxiliary RouteLoss for the trajectory prediction model to generate multi-modal diverse trajectories in the drivable area. In the second stage, reinforcement learning is used to track the predicted trajectories while avoiding collisions, which can improve the feasibility of generated trajectories. In addition, we develop a data-driven simulator I-Sim that can be used to train reinforcement learning models in parallel based on naturalistic driving data. The vehicle model in I-Sim can guarantee that the generated trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give comprehensive metrics to evaluate generated trajectories for simulation scenarios, which shows that TrajGen outperforms either trajectory prediction or inverse reinforcement learning in terms of fidelity, reactivity, feasibility, and diversity. Copyright © 2022, The Authors. All rights reserved.

关键词： Trajectories

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A novel dual-sine memristive conservative chaotic system: analysis of rich dynamical behaviors and its application in color medical image encryption

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Physica Scripta 2025年第7期100卷 075242-075242页

作者： Mu Li Yizhe Li School of Information and Electrical Engineering Hunan University of Science and Technology Hunan Province People’s Republic of China Hunan Provincial Key Laboratory of Intelligent Control and Maintenance of Complex Systems Hunan Province People’s Republic of China

A novel dual-sine memristive conservative chaotic system with sinusoidal attractor oscillatory variations is proposed, which exhibits rich dynamic behaviors. Study of the equilibrium point, divergence, Lyapunov exponent, and Kaplan-Yorke dimension confirms the system's conservatism and chaos. As the system parameters a and b are varied, the attractors exhibit two different sinusoidal oscillatory variations: one with wave peaks extending to the left and right, and the other extending simultaneously up and down and to the left and right. The system appears to be in a very large range of chaotic state. Furthermore, the multistability of the system is studied, and three different states of coexisting attractors are found. Also, two different types of transient transfer behaviors are observed in the system with time. In addition, the digital hardware implementation of the system is completed using FPGA, which proves the feasibility of the system implementation. In the end, we combine the conservative chaotic system proposed in this paper with the traditional DNA color image encryption algorithm and apply it to medical image encryption, which shows good encryption effect through the analysis of histograms and other tests such as noise resistance, which reflects the potential of this system in the field of cryptography from the side.

关键词：

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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Chinese Automation Congress (CAC)

作者： Weixin Hong Huaiyu Wu Zhen Shen Gang Xiong Bin Hu Xisong Dong Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available.

关键词： Automation Reinforcement learning Predictive models Prediction algorithms Planning Task analysis

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