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8th CAA International Conference on Vehicular control and Intelligence, CVCI 2024

作者： Jing, Penghao Zhu, Yongxin Li, Yongfu Huang, Xin School of Automation Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing Chongqing400065 China

ISBN: (纸本)9798331504892

This paper aims to assess the impact of denial-of-service (DoS) attacks on the traffic flow of connected and autono-mous vehicles (CAVs). With this aim, a novel car-following model is presented to depict the dynamics of CAVs to support a compre-hensive analysis of the CAV traffic performance. Particularly, the model uniquely integrates the information delay, the optimal ve-locity changes with memory and the malicious effect of manipu-lated information. Then, the linear stability of the proposed model is derived using the perturbation method. By introducing small perturbations into the system, the stability conditions for a platoon of CAVs are determined. Finally, extensive simulations are con-ducted to explore the impact of DoS attack on CAV traffic. Our experimental results demonstrate that the model accurately captures real traffic flow, particularly the behavior of a CAV platoon under a DoS network attack. © 2024 IEEE.

关键词： Autonomous vehicles

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Enhanced Potato Detection Using an Improved YOLOv8 Algorithm 24

Enhanced Potato Detection Using an Improved YOLOv8 Algorithm

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9th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2024

作者： Wang, Zihan Zhang, Yunxi Ma, Yuqi Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798400716911

This paper proposes a novel potato detection algorithm YOLOv8-P, based upon the improved YOLOv8 framework to achieve enhanced detection accuracy. The algorithm incorporates the BiFormer attention mechanism and introduces an additional detection layer specifically designed for large targets, thereby significantly improving the model's performance in identifying larger potatoes. Additionally, a comprehensive dataset named potato-2024 was developed to address the limitations of existing public datasets in potato detection. To validate the effectiveness of YOLOv8-P, comparative experiments were conducted with YOLOv5 and YOLOv8 on the potato-2024 dataset. The results demonstrate that, under identical experimental conditions and parameters, YOLOv8-P exhibits superior recognition accuracy in potato identification, thus establishing a new benchmark in potato detection technology. © 2024 Copyright held by the owner/author(s).

关键词： YOLOv8

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Collaborative SLAM System Based on Shared Priori Maps

Collaborative SLAM System Based on Shared Priori Maps

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wu, Chunxu Shou, Yicheng Li, Zhen Liu, Xiangdong School of Automation and Key Laboratory for Intelligent Control and Decision on Complex Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811966125

Multi-robot system outperforms in efficiency for cooperative task, where the challenging burden of information exchange for surrounding environment is facilitated by collaborative simultaneous localization and mapping (SLAM). However, the utilization of environmental information, i.e., mapping, should be specifically put into efficient use and merged from the viewpoint of computation saving and localization accuracy improvement. Towards these issues, this paper proposes the Collaborative SLAM system based on shared priori maps which includes multi-agent initialization and a new map sharing and reuse mechanism among various active agents in an incremental way. Finally, based on the data set recorded in the actual laboratory environment, the proposed Collaborative SLAM system is evaluated from the perspectives of localization accuracy and computational power consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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AF-YOLO:A Road Scene Object Detection Algorithm Fused with Mixed-domain Attention

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第2期8卷 347-363页

作者： Liao, Lyuchao Chen, Ruixiang Shi, Jinjin Zheng, Qi Xiong, Rong Zeng, Jiemao Fujian Provincial Universities Key Laboratory of Industrial Control and Data Analysis Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology Fujian University of Technology Fuzhou350118 China Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology Fujian University of Technology Fuzhou350118 China

Object identification is a significant computer vision issue with a growing number of applications in the area of autonomous driving. In order to solve the problem of difficult detection of small objects and dense objects in the target detection algorithm in the autonomous driving scenarios,we propose an object detection method with a hybrid domain attention mechanism. In the model, the Neck network is integrated with a parallel mixed-domain attention mechanism to increase the usable features in the feature map from both channel and spatial aspects. Then, we improve the Spatial Pyramid Pooling (SPP) module with dilated convolutions to limit the loss of important information in the feature map after max pooling, which further optimizes the detector performance. Finally, a fusion approach is proposed to improve the detection layer and enhance the capability of detecting tiny objects. Extensive experiments on the BDD100k dataset show that the proposed model outperforms the state-of-the-art YOLOv5s model with an average accuracy increase of 6.22%(mAP @0.5). © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolution

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Fall Detection System Based on YOLO Algorithm and MobileNetV2 Model 10

Fall Detection System Based on YOLO Algorithm and MobileNetV...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Han, Chunxiao Zhang, Jingyan Wu, Han Tianjin Key Laboratory of Information Sensing & Intelligent Control China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9798331510138

Accidental falls are the major causes of injury among elderly people living alone. However, traditional fall detection algorithms usually have missed or false detections problems. In this paper, we design a fall detection system based on YOLO algorithm and MobileNetV2 model. First, detection frame coordinates and joint coordinates of the human body are fused as feature information, and background information is filtered out. A lightweight deep learning model, MobileNetV2, is then used for classification. Compared with commonly used lightweight classification models such as ShuffleNet and EfficientNet, the MobileNetV2 model performs better in recognizing fall behavior. Our system can effectively distinguish fall behavior from daily activities, meeting the requirements for fall detection. © 2024 IEEE.

关键词： Fall detection

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Stabilization of Perturbed Continuous-Time Systems Using Event-Triggered Model Predictive control

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IEEE Transactions on Cybernetics 2022年第5期52卷 4039-4051页

作者： Wang, Mengzhi Sun, Jian Chen, Jie Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex System Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

In this article, event-triggered model predictive control (EMPC) of continuous-time nonlinear systems with bounded disturbances is studied. Two novel event-triggered control schemes are proposed. In the first strategy, an event-triggering condition, designed based on the state error between the actual system state and the optimal one, with an absolute threshold is considered. In the second strategy, an event-triggering condition with a mixed threshold is designed to further save the computational resources. The minimal interevent times of both event-triggered control schemes are obtained to avoid the Zeno behavior. Sufficient conditions of recursive feasibility for these two triggering strategies, which refer to the prediction horizon, the triggering level, and the disturbance bound, are obtained, respectively. Input-to-state practical stability (ISpS) of both event-triggered control systems is established without requiring the system state entering the terminal set in finite time, respectively. Finally, the numerical simulation shows the effectiveness of the proposed methods. © 2013 IEEE.

关键词： Model predictive control

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Automatic testing of computerized interlocks based on recognition of the interlocking host computer interface

Automatic testing of computerized interlocks based on recogn...

引用

2023 International Conference on Communication Network and Machine Learning, CNML 2023

作者： Wang, Juanning Li, Qiang Key Laboratory of Optoelectronic Technology and Intelligent Control Ministry of Education Gansu Province Lanzhou China Lanzhou Jiaotong University Institute of Automatic Control Gansu Province Lanzhou China Lanzhou Jiaotong University Research Institute Gansu Province Lanzhou China

ISBN: (纸本)9798400716683

Contending with the current status quo of computerized interlock automatic test, in the test result determination module of computerized interlock automatic test system, OpenCV computer vision library is introduced to realize the test result determination based on interlock interface recognition. In the automatic test system, the interlocking software under test receives the simulated push-button operation commands and the simulated station signaling equipment status, and outputs the test results after the interlocking operation. OpenCV recognizes the signaling status changes of signaling machines, turnouts, track sections and other equipment in the test results, and then compares the results with the expected results to determine whether the test is passed or not. The results show that the automatic test result determination based on image recognition is more versatile, more visualized, and significantly more efficient. © 2023 ACM.

关键词： Image processing

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Chip Architecture Design Management Based on Low-Power Multitasking Simulation 9

Chip Architecture Design Management Based on Low-Power Multi...

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9th International Conference on Advances in Machinery, Materials Science and Engineering Applications, MMSE 2023

作者： Zhang, Ying Sun, Kai National Key Laboratory of Science and Technology on Aerospace Intelligent Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781643684383

From the aspects of low-power DSP design, low-power SOC design, and low-power power switch design, this paper proposes a chip architecture 'high-low matching' scheme for low-power multitasking requirements. The low-power DSP design starts with operating voltage and operating frequency control, and uses three low-power modes, PD1, PD2, and PD3;the low-power SOC design carries out low-power mode management from the clock control of APB and AHB bus;In terms of low-power power switch design, no-load low-power control, and input under-voltage protection, output over-voltage protection, over-temperature protection, and output over-current protection are implemented. The paper proposes different low-power control modes. After the evaluation of the built test platform, it is proved that the design of the scheme is reasonable, and it can realize the engineering application of low-power multi-task management. © 2023 The Authors.

关键词： Electric power utilization

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