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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhu, Yiran Xu, Xing Ji, Yanli Shen, Fumin Shen, Heng Tao Lu, Huimin Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Mechanical and Control Engineering Kyushu Institute of Technology Japan

ISBN: (纸本)9781665438643

Graph convolution networks (GCNs) have become the mainstream framework for the skeleton-based action recognition task, since the skeleton representation of human action can be naturally modeled by the graph structure. Generally, most of the existing GCN based models extract and aggregate skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale information such as part and body features in the skeleton. To address this issue, we propose a novel Graph Convolutional Hourglass Network (GCHN) model, which is scalable by stacking several basic modules of Graph Convolutional Hourglass Block (GCHB). Each GCHB module consists of the sequential operations of graph convolution, graph pooling and graph unpooling, which can promote the interaction of multi-scale information in the skeleton and effectively improve the recognition performance. Extensive experiments on the challenging NTU-RGB+D and Kinetics-Skeleton datasets demonstrate that the proposed GCHN model achieves state-of-the-art performance. © 2021 IEEE

关键词： Convolution

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Attack Stealthiness and Detection of Multiagent Systems: A Zero-Sum Formulation

Attack Stealthiness and Detection of Multiagent Systems: A Z...

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IEEE Conference on Decision and control

作者： Shiyong Zhu Miaomiao Wang Jie Chen Anders Rantzer Department of Electrical Engineering City University of Hong Kong Hong Kong China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Department of Automatic Control Lund University Lund Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Cybersecurity has in recent years emerged as a paramount concern in the design and operation of industrial systems and civil infrastructures, due mainly to their susceptibility to malicious cyber attacks which take advantage of the vulnerability of communication networks and IT devices. In this paper, we investigate such an attack and counter attack scenario by considering multiagent systems, a somewhat basic prototype of cyberphysical systems. We study false data injection attacks launched on the agent sensors, and possible defense of such attacks at the agent actuators. The primary issue under consideration is the stealthiness of the attacks, while steering a multiagent system away from its consensual state. We propose a metric to quantify the stealthiness, and formulate the stealthiness problem as one of zero-sum games. We solve the problem explicitly, which gives rise to a fundamental bound on the stealthiness achievable, and as well optimal attack and defense strategies that achieve the optimal stealthiness, both of which can be obtained in terms of certain augmented controllability Gramians associated with the agents. The stealthiness bound is seen to depend on agent dynamics and network characteristics including a measure of connectivity.

关键词： Actuators Protocols Prototypes Games Sensor phenomena and characterization controllability Sensor systems Game theory Observability Multi-agent systems

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无人飞行器在不确定扰动下的动态目标跟踪

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engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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Automotive Seat Surface Defect Detection Method Based on Improved YOLOv5 Algorithm

Automotive Seat Surface Defect Detection Method Based on Imp...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Juchao Zhang Changlin Yu Gang Wang Chunling Xu Zaixiang Pang Zhongbo Sun Department of Control Science and Engineering Changchun University of Technology Changchun China Department of Mechanical and Electrical Engineering Changchun University of Technology Changchun China Department of Public and Basic Computer Education Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

A method for detecting surface defects of car seats based on improved YOLOv5 is proposed to address the current issues of low efficiency, high missed detection rate, and difficult cost control in manual inspection of surface defects of car seats. In addition, in order to solve the imbalance problem of various defect images collected by industrial cameras, the methods of translation, cropping and adding noise are used to pre-enhance the images. Train the manually annotated dataset using an improved Yolov5 model, which uses K-means++ clustering anchor boxes to enhance adaptability to the dataset in this paper. At the same time, add a BiFormer module to the network to reduce the loss of small target defects and enhance the network’s feature fusion and extraction capabilities. The experimental results show that the mAP of the whole is increased from 67.1% to 78.2%, and the model can meet the requirements of the identification of car seat surface defects.

关键词： YOLO Deep learning Training Machine vision Noise Inspection Feature extraction

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A Sandwich control System with Dual Stochastic Impulses

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 741-744页

作者： Chuanlin Liao Dan Tu Yuming Feng Wei Zhang Zitao Wang B.O.Onasanya the School of Computer Science and Engineering Chongqing Three Gorges University the School of Physical Education and Health Chongqing Three Gorges University the School of Three Gorges Artificial Intelligence Chongqing Three Gorges University the Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University the Department of Mathematics University of Ibadan

Dear editor,Along with the progress of science and technology and the development of social civilization,control system brings an increasingly significant function in daily *** application field of control system is very wide,for instance,in mobile technology[1],artificial earth satellite[2],pest control[3],***[4]first put forward the concept of random pulse in *** present,impulsive control is used in networked control[5],secure communication[6],*** the 21st century,the impulsive control has been used in synchronization of coupled system,intelligent fault identification,image encryption.

关键词： system impulsive instance

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent Systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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第35届中国控制与决策会议

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer ***,the majority of the existing research assumes that the dynamics is known *** remove this condition,the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is ***,a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external ***,the unknown regulator equations are solved using the iterative recurrent neural network ***,the state-based data-driven distributed control law is synthesized to solve the *** optimal gains are derived by solving convex optimization problems using input and state ***,a numerical example is presented to verify the feasibility of the proposed framework.

关键词： Linear systems multi-agent systems cooperative output regulation data-driven control optimal control

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Design and Analysis of Weather Parameters Using MEMS Technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring

Design and Analysis of Weather Parameters Using MEMS Technol...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Abdurrashid S. Hassan Vimala A. Juliet G. Pius Agbulu G. Joselin Retna Kumar Raghavendra Reddy N V S Gunasekar Department of Electronics and Control/Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechatronic Engineering School of Engineering Sciences & Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0 . It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar.

关键词： Temperature sensors Temperature Platinum Terrestrial atmosphere Humidity Stability analysis Finite element analysis

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Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Prisilla, Ardha Ardea Pusparani, Yori Chang, Wen-Thong Liau, Ben-Yi Jan, Yih-Kuen Ardhianto, Peter Lin, Chih-Yang Lung, Chi-Wen Asia University Taichung Department of Digital Media Design Taiwan LaSalle College Jakarta Department of Fashion Design Jakarta Indonesia Budi Luhur University Department of Visual Communication Design Jakarta Indonesia Department of Computer Science and Information Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Asia University Department of Creative Product Design Taichung Taiwan Soegijapranata Catholic University Department of Visual Communication Design Semarang Indonesia National Central University Department of Mechanical Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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