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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Gao, Tianqi Tian, Yuanxu Tao, Hongfeng Ni, Yuanzhi School of Internet of Things Engineering Jiangnan University Wuxi214122 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789811939976

Vehicle platooning is an emerging application in transportation system, boosting safe and efficient traffic environment. Based on the vehicular wireless communication technology, the driving states of the neighboring vehicles can be obtained for accurate vehicle control. In this work, the vehicle platoon system is modeled considering random characteristics of vehicular wireless communications. The consensus-based platoon control strategy and the autoregressive filtering algorithm are designed for efficient and effective control under topology of predecessor following (PF) and predecessor leading following (PLF), respectively. The platoon control model and algorithms are implemented by Simulink, where the results verify the effectiveness of the proposed solutions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series analysis

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image 16

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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16th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Xie, Shuo Ma, Yingjie Xu, Wei Qiu, Song Sun, Yifei East China Normal University School of Communication and Electronic Engineering China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798350330755

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets. © 2023 IEEE.

关键词： Image enhancement

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Weed detection based on improved Yolov5 8

Weed detection based on improved Yolov5

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8th International Conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Yu, ZiZheng Nie, ZiJun Hu, Yong Qi Yan, HuangBin Lou, LiJun Yin, Zhijian Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350302455

The scale of grain production affects human life and development. With the continuous expansion of cultivated land, the reproductive ability of weeds to mutiply gradually increases, which affects the growth of crops. If weeds are not treated properly managed, they can also allow spread indiscriminately in the field and reduce crop yields. The growth ability of weeds is stronger than that of crops, and there are many types of weeds with a wide of species diversity. To solve the problem that the existing weed detection methods cannot detect and classify weeds accurately and quickly, a deep learning method based on improved Yolov5 was designed for weed detection. By replacing the 3 × 3 convolution with multi-head self-attention (MHSA) in the Yolov5's backbone, the accuracy of weed detection is improved. The experimental results show that the improved Yolov5 weed detection algorithm achieves 51.4% accuracy. Compared with the original Yolov5 model, the calculation amount is 3 points less than the original, and total time required is slightly shorter, which improves the accuracy of weed classification and positioning. © 2023 IEEE.

关键词： Crops

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Augmented Reality Enabled Telepresence Framework for Intuitive Robotic Teleoperation

Augmented Reality Enabled Telepresence Framework for Intuiti...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Xu, Zheng Lin, Zecai Liu, Huanghua Ren, Qingsong Gao, Anzhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Institute of Medical Robotics and Department of Automation Shanghai200240 China

ISBN: (纸本)9798350325706

In terms of telepresence for robotic teleoperation, the existing remote surgery provides the patient's side real-time videos to remote operators. Its effectiveness is limited due to the lack of depth information, occlusion, and restricted viewing angles. To address it, this paper develops a robotic telepresence framework integrating augmented reality (AR) and 3D reconstruction technology, providing a 3D virtual patient's side environment for remote operators. The real scene of the patient's side is reconstructed by a monocular camera with a manipulator before the operation, and the manipulator is teleoperated by the operator at the remote's side. The manipulator's pose is fused into the classic SVO+REMODE algorithm to improve the reconstructed accuracy. On the remote's side, an AR head-mounted display (HMD) is deployed to provide telepresence for operators under a similar environment, and the reconstructed scene of the patient's side is registered to the remote room, from which the operator can see the whole scene of the patient body and surrounding objects. An ArUco cube is used to evaluate the superimposed error between virtual and real displays, and the effectiveness is validated in an experimental platform for remote surgery. © 2023 IEEE.

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Smoke Detection In Complex Scenery using Improved YOLOV5 8

Smoke Detection In Complex Scenery using Improved YOLOV5

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8th International Conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Nie, ZiJun Hu, YongQi Yu, ZiZheng Li, RuJia Yin, ZhiJian Zhan, HuaLin Yang, Zhen Jiangxi Science and Technology Normal University School of Communications and Electronics Nanchang China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9798350302455

Considering smoke detection in a complex scene, high smoke object error rate and low detection efficiency, an improved YOLOV5 for smoke detection is proposed in this paper. To improve the effectiveness of smoke detection algorithm for smoke targets in complex scenes. First, a new lightweight convolution technique GSConv is introduced to replace the Conv layer in the neck layer for feature extraction, and the original C3 module is replaced by Cross Stage Partial Network (GSCSP) module VoVGSCSP. This improvement reduces the computation complexity of the algorithm while maintaining the detection accuracy. Second, CIoU is replaced by SIoU as the regression function of the prediction box to improve the prediction accuracy of the prediction box and reduce the rate of missed detection of the target. Finally, to improve the performance of the deep network, Dynamic ReLU is introduced as an activation function (dynamically adjusting the ReLU parameters depending on the input data). Experiments on the SM-dataset (smoke detection dataset) show that the improved model improves the accuracy and precision of target smoke detection compared to the YOLOV5 model. Its precision is increased by 1.8%, mAP@0.5 is increased by 1.0%, the complexity of the model is reduced by 23.8%. The improved algorithm proposed in this paper can effectively extract smoke features and more suitable for smoke detection tasks in complex scenes. © 2023 IEEE.

关键词： Smoke

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control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

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Journal of systems Science & Complexity 2024年第1期37卷 114-135页

作者： QIN Yahang ZHANG Chengye CHEN Ci XIE Shengli LEWIS Frank L School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China. Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China. UTA Research Institute The University of Texas at ArlingtonFort WorthTX 76019USA

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou *** navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou ***,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback *** proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle *** is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.

关键词： BeiDou navigation multipath effect prescribed convergence rate reinforcement learning urban localization.

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The Deployment of Microservice at Edge based on MQTT for Low Latency 22

The Deployment of Microservice at Edge based on MQTT for Low...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Han, Mengqi Yang, Bo Liu, Yuxiang Liu, Sicheng Liu, Qi Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798331527471

With the rapid development of smart manufacturing, existing microservice deployment strategies based on the TCP protocol tend to encounter problems such as network latency, excessive header length, and port exhaustion in high concurrency scenarios. MQTT Broker can relay data between microservices, effectively alleviating these issues. However, the communication overhead of the Broker is significantly related to its deployment and the dependency of microservices. An appropriate deployment solution can significantly improve service quality. To address this issue, this paper models the deployment of MQTT Broker and microservices as a graph with cycles. By introducing auxiliary variables, the NP-hard problem is transformed into a mixed-integer quadratic programming problem, improving problem-solving efficiency and optimizing the microservices deployment strategy to reduce communication overhead. Meanwhile, a real-world heterogeneous cluster was constructed to experiment on fault detection applications, elaborating on the superiority of the microservice deployment strategy designed in this paper. Experimental results demonstrate that our approach reduces communication overhead by 20% in comparison to both the greedy strategy and the K8s strategy. This suggests that achieving an optimal balance between the number of Brokers and microservices is vital for ensuring efficient resource utilization and maintaining low communication latency, particularly as the scale of services expands. © 2024 IEEE.

关键词： Smart manufacturing

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Distributed MPC for Reconfigurable Architecture systems via Alternating Direction Method of Multipliers

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1336-1344页

作者： Ting Bai Shaoyuan Li Yuanyuan Zou the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China

This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different *** equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is *** this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration ***,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control ***,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.

关键词： Alternating direction method of multipliers(ADMM)algorithm distributed control model predictive control(MPC) reconfigurable architecture systems.

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Weighting Strategy of Multi-camera system for Pose Measurement

Weighting Strategy of Multi-camera System for Pose Measure...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Li, Yuran Zhou, Tongyuehao Yu, Wenbin Li, Yichen Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789811966125

In a multi-camera system, based on Binocular vision, choosing the best camera combination is one of the effective ways to improve measurement accuracy. When multi-cameras layout and intrinsic parameters are fixed, the angle and the distance from cameras to object are two main factors influencing the pose measurement result. From the perspective of object’s observability and image resolution, the mathematic relation between measurement error and the angle, distance from cameras to object is derived which forms the weighting formula whose high value corresponds to the best camera combination. Comprehensive simulation is conducted, proving that the proposed method can decrease the measurement error by 55% compared with former methods, and even by 94% in some occasions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cameras

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De-Redundant Biomedical Feature Extraction Based On Improved MultiSURF

De-Redundant Biomedical Feature Extraction Based On Improved...

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2023 China Automation Congress, CAC 2023

作者： Ma, Ruixiang Zhang, Bin Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350303759

Biomedical data contain valuable information which contributes to the prediction of clinical disease risk. However, the presence of redundant and irrelevant features will diminish prediction accuracy and augment model complexity. According to our research, MultiSURF is a famous feature selection method but falls short in eliminating redundant features. To address this constraint, this paper introduced an improved MultiSURF method known as EN-MultiSURF. The proposed method integrated the strengths of Elastic Net and MultiSURF, which is capable of detecting similarity between features as well as eliminating redundant and irrelevant features. The proposed method was compared with Relief, MultiSURF, Elastic Net, Random Forest and Decision Tree on one binary along with four multiclass numerical datasets. The experimental results substantiated that our method surpassed other methods and exhibited a higher degree of consistency with medical prior knowledge, which established itself as a valuable tool for biomedical data mining. © 2023 IEEE.

关键词： Feature Selection

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