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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350316339

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. © 2024 IEEE.

关键词： Stochastic systems

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A Timeslot Clustering-Based Hybrid Traffic Scheduling in Time-Sensitive Networking 22

A Timeslot Clustering-Based Hybrid Traffic Scheduling in Tim...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yang, Zhihao Wang, Shouliang Xu, Qimin Li, Xin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9798331527471

The development of Industrial Internet of Things necessitates deterministic transmission of hybrid traffic with varying real-time requirements. Time-Sensitive Networking offers schemes combining scheduling mechanisms like the Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). Current methods utilize the combination of TAS&CQF to schedule Time-Triggered (TT) and Audio-Video Bridging (AVB) traffic under corresponding mechanisms, respectively. However, the prior TT traffic orchestration restricts the scheduling solution space of AVB traffic, decreasing the effectiveness of combination and finally resulting in low schedulability. In this paper, we focus on enhancing the schedulability of TT&AVB scheduling by integrating TAS and Cyclic Specified Queuing and Forwarding (CSQF), which brings larger scheduling solution space to AVB traffic. The integration of TAS&CSQF is proposed by analyzing the impact of TAS on CSQF to expand the allocatable resource for AVB traffic. Then TAS&CSQF scheduling model is formulated to ensure deterministic requirements. Within this model, Timeslot Clustering Optimization (TCO) is proposed to optimize the scheduling solution space of AVB traffic and slice timeslot by refining the scheduling of TAS. Based on TCO, Flow Congestion Metric (FCM) is designed to improve schedulability by the congestion level of each link. An FCM-based algorithm is further presented to generate TT&AVB solutions incrementally. Simulation results demonstrate that our method reduces scheduling time cost by over 168 times and improves schedulability by 32% for 3000 flows compared to existing methods. © 2024 IEEE.

关键词： Traffic congestion

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3D Machine Vision Enabled Shrinkage Pin Detection of Aviation Connectors in the Aircraft EWIS 43

3D Machine Vision Enabled Shrinkage Pin Detection of Aviatio...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Yao Ren, Cheng Xu, Jiaxin Chen, Cailian Guan, Xinping Shanghai Jiao Tong University And Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581581

Aviation connectors are responsible for information and energy transmission in the aircraft electrical wiring interconnection system (EWIS). Even a tiny shrinkage of any connector pin may lead to a critical impact on the reliability and stability of the EWIS. However, the current detection method of connector pin shrinkage mainly relies on manual visual inspection, which is prone to missed and false detection. Additionally, traditional 2D machine vision methods face challenges in dealing with fine pins and deep calibers of connectors. To address this issue, this paper designs a defect detection device and proposes a 3D-enabled shrinkage pin detection (3D-SPD) algorithm. For the device, a 4-degree-of-freedom (4-DOF) support mechanism is designed to collect the connector point cloud based on the imaging principle of a 3D structured light camera. Then, a preprocessed method is presented to extract defective regions from the raw point cloud. Finally, 3D-SPD algorithm can automatically select the reference plane and detect the pin position, enabling online detection of connector shrinkage pins. Experiment results show that 3D-SPD algorithm is effective in detecting shrinkage pin defects on various types of aviation connectors, with an accuracy rate of 98.62% and a shrinkage pin detection rate of 100%. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Machine vision

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Facial Expression Recognition In-the-Wild with Deep Pre-trained Models 17th

Facial Expression Recognition In-the-Wild with Deep Pre-tra...

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17th European Conference on Computer Vision, ECCV 2022

作者： Li, Siyang Xu, Yifan Wu, Huanyu Wu, Dongrui Yin, Yingjie Cao, Jiajiong Ding, Jingting Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Ant Group Hangzhou China

ISBN: (纸本)9783031250743

Facial expression recognition (FER) is challenging, when transiting from the laboratory to in-the-wild situations. In this paper, we present a general framework for the Learning from Synthetic Data Challenge in the 4th Affective Behavior Analysis In-The-Wild (ABAW4) competition, to learn as much knowledge as possible from synthetic faces with expressions. To cope with four problems in training robust deep FER models, including uncertain labels, class imbalance, mismatch between pretraining and downstream tasks, and incapability of a single model structure, our framework consists of four respective modules, which can be utilized for FER in-the-wild. Experimental results on the official validation set from the competition demonstrated that our proposed approach outperformed the baseline by a large margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Optimization of Tiered Pricing Strategy for Smart Grid Considering Clean Energy

Optimization of Tiered Pricing Strategy for Smart Grid Consi...

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2024 China Automation Congress, CAC 2024

作者： Wang, Xiaocheng Li, ZeLong Sun, Pengjiao Han, Qiaoni Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China School of Electrical and Information Engineering Tianjin University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China

ISBN: (纸本)9798350368604

With the rapid development of new energy technology, it has become a trend to combine wind and solar power generation into the smart grid. In this paper, wind power generation and photovoltaic power generation are introduced into the smart grid, and taking into account the consumption capacity of consumers at different levels, a tiered pricing scheme considering the uncertainty of wind and solar energy is proposed. Firstly, we establish probability density functions of power generation for wind power generation and photovoltaic power generation respectively to express the uncertainty of wind energy and solar energy. To meet the energy demand of consumers at all levels, we present the energy demand model of consumers after considering their electricity demand and temperature influence. Then, we formulate the optimal pricing problem for the electricity market under the framework of tiered pricing, so as to make the energy market get satisfactory income while meeting the energy demand of consumers. Finally, we use the particle swarm optimization algorithm to determine the optimal pricing strategy of the energy market. The simulation results show that when meeting the energy demand of consumers in different temperature conditions, the optimal pricing can be found that meets the satisfactory income of the energy market. © 2024 IEEE.

关键词： Clean energy

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Path Planning Based on A-Star and Dynamic Window Approach Algorithm for Wild Environment

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Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 725-736页

作者： DONG Dejin DONG Shiyin ZHANG Lulu CAI Yunze Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai200240China Shanghai Electro-Mechanical Engineering Institute Shanghai201109China

The path planning problem of complex wild environment with multiple elements still poses *** paper designs an algorithm that integrates global and local planning to apply to the wild environmental path *** modeling process of wild environment map is *** optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning ***,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional *** the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple *** improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.

关键词： path planning path re-planning wild environment A-Star algorithm dynamic window approach

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Bootstrapping Autonomous Lane Changes with Self-supervised Augmented Runs 17th

Bootstrapping Autonomous Lane Changes with Self-supervised ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Xiang, Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250682

In this paper, we want to strengthen an autonomous vehicle’s lane-change ability with limited lane changes performed by the autonomous system. In other words, our task is bootstrapping the predictability of lane-change feasibility for the autonomous vehicle. Unfortunately, autonomous lane changes happen much less frequently in autonomous runs than in manual-driving runs. Augmented runs serve well in terms of data augmentation: the number of samples generated from augmented runs in a single one is comparable with that of samples retrieved from real runs in a month. In this paper, we formulate the Lane-Change Feasibility Prediction problem and also propose a data-driven learning approach to solve it. Experimental results are also presented to show the effectiveness of learned lane-change patterns for the decision making. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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Multi-Human Pose Estimation by Deep Learning-Based Sequential Approach for Human keypoint Position and Human Body Detection

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Journal of Shanghai Jiaotong University (Science) 2023年 1-11页

作者： Tahir, Rizwan Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Recent multimedia and computer vision research has focused on analyzing human behavior and activity using images. Skeleton estimation, known as pose estimation, has received a significant attention. For human pose estimation, deep learning approaches primarily emphasize on the keypoint features. Conversely, in the case of occluded or incomplete poses, the keypoint feature is insufficiently substantial, especially when there are multiple humans in a single frame. Other features, such as the body border and visibility conditions, can contribute to pose estimation in addition to the keypoint feature. Our model framework integrates multiple features, namely the human body mask features, which can serve as a constraint to keypoint location estimation, the body keypoint features, and the keypoint visibility via mask region-based convolutional neural network (Mask-RCNN). A sequential multi-feature learning setup is formed to share multi-features across the structure, whereas, in the Mask-RCNN, the only feature that could be shared through the system is the region of interest feature. By two-way up-scaling with the shared weight process to produce the mask, we have addressed the problems of improper segmentation, small intrusion, and object loss when Mask-RCNN is used, for instance, segmentation. Accuracy is indicated by the percentage of correct keypoint, and our model can identify 86.1% of the correct keypoints. © 2023, Shanghai Jiao Tong University.

关键词： Convolutional neural networks

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Near-perfect replication on amorphous alloys through active force modulation based on machine learning/neural network parameter prediction

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 177-187页

作者： Senkuan Meng Zheng Wang Ruisong Zhu Ruijie Liu Jiang Ma Lina Hu Weihua Wang Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Songshan Lake Materials Laboratory Dongguan 523808China Institute of Physics Chinese Academy of SciencesBeijing 100190China

As a microforming technique,micro/nano-structural replication possesses advantages of high precision and *** the remarkable superplasticity in the supercooled liquid region,amorphous alloys or metallic glasses(MGs)are regarded as ideal materials for miniature ***,due to the intrinsic metastable nature of supercooled liquids,the design of imprinting processes for MGs poses a *** the past,process parameters have largely relied on trial-and-error *** this work,a low-frequency active force modulation system is employed to apply a stable,precise,and real-time feedback stress field for imprinting of MG ***-frequency vibrations can facilitate the filling of microstructures on the template surface by reducing the effective viscosity of the supercooled *** the dataset composed of orthogonal experiments,a machine learning strategy based on back-propagation(BP)neural networks was utilized to construct a 3D parameter space for temperature,stress,and time,and to predict the corresponding filling ***,the optimal combination of imprinting process parameters was identified,and its filling ratio was experimentally validated to reach as high as *** near-perfect replication of microstructures confirms the superiority of the active force modulation system and the data-driven strategy of machine learning-assisted parameter *** the same time,this one-step microforming process provides a new approach to addressing the accuracy-cost trade-off dilemma in precision manufacturing.

关键词： metallic glass amorphous alloy microforming imprinting machine learning

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Recovering unknown topology in a two-layer multiplex network:One layer infers the other layer

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Science China(Technological Sciences) 2022年第7期65卷 1493-1505页

作者： LIU Hui SHANG ZhiCheng REN ZiYi LI Yan ZENG ZhiGang LU JunAn School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China School of Mathematics and Statistics Wuhan UniversityWuhan 430072China

There have been increasing interests in studying multiplex dynamical networks *** paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer *** a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a *** proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological *** with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology ***,numerical simulations validate the effectiveness of the proposed methods.

关键词： multiplex network synchronization topology identiflcation

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