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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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Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

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Chinese Journal of Electronics 2023年第5期32卷 972-981页

作者： CHEN Xiaohan HU Xiaoxi WEN Tao CAO Yuan The School of Electronic and Information Engineering Beijing Jiaotong University The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University The National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong University

In the railway transportation industry,fault diagnosis of railway point machines(RPMs) is *** operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signalbased fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently *** overcome these shortcomings, we propose a doublescale wide first-layer kernel convolutional neural network(DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for lowsampling-rate signals. Meanwhile, the proposed doublescale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison,were conducted to demonstrate the robustness and accuracy of the proposed algorithm.

关键词： Vibrations Fault diagnosis Transportation industry Feature extraction Rail transportation Robustness Convolutional neural networks

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Stage Division and CNN-BiLSTM-Attention for Remaining Useful Life Prediction of Rolling Bearings 43

Stage Division and CNN-BiLSTM-Attention for Remaining Useful...

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43rd Chinese control Conference, CCC 2024

作者： Du, Sirui Dong, Feng Zhang, Shumei Tianjin University Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin300072 China Tianjin Renai College School of Information and Intelligent Engineering Tianjin301636 China

ISBN: (纸本)9789887581581

The prediction of the remaining useful life (RUL) of rolling bearings is one of the current research hotspots. However, due to the influence of internal degradation and external interference, the whole life cycle of rolling bearing is extremely complex and changeable, and the prediction accuracy of the existing methods needs to be improved. In order to realize the accurate RUL prediction of complex degradation process, a deep learning model based on stage division is proposed in this paper. Firstly, the feature indexes are extracted from time domain, frequency domain and time-frequency domain, and the canonical variate dissimilarity analysis (CVDA) that can handle dynamic data is used for feature reduction to obtain statistics containing streamlined degradation information. Secondly, the kernel density estimation (KDE) and expert knowledge are used to divide the whole life cycle into multiple stages based on the trend and fluctuation of statistics. Then, with the combination of convolution neural network (CNN) and Bi-directional long short-term memory (Bi-LSTM), the CNN-BiLSTM-Attention network is proposed, in which the attention mechanism is developed to highlight the focus. Moreover, the Bayesian optimization algorithm is used to adjust the network hyperparameters. Finally, the effectiveness is verified by using the accelerated life test data of rolling bearings. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Frequency domain analysis

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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A Novel Second-Order Neurodynamic system to Fixed-Time Nash Equilibrium Seeking 14

A Novel Second-Order Neurodynamic System to Fixed-Time Nash ...

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14th International Conference on information Science and Technology, ICIST 2024

作者： Song, Yao Ju, Xingxing Yao, Chaoli College of Electronics and Information Engineering Sichuan University Chengdu China Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Shanghai China Hainan University Key Laboratory of Engineering Modeling and Statistical Computation of Hainan Province Haikou China

ISBN: (纸本)9798350353334

In this paper, we investigate fixed-time (FT) Nash equilibrium (NE) seeking problem for non-cooperative games. A novel second-order NE seeking neurodynamic system is proposed to accelerate the convergence. The proposed neurodynamic system's trajectory converges to NE within fixed-time under potential function and strongly monotone potential function respectively. It is shown that the upper bounds of the settling time are independent of the initial state of the system. The robustness of the proposed NE seeking neurodynamic system under bounded perturbations is further studied. The practicability of the proposed NE seeking system is illustrated via a simulation example and an analog circuit implementation on Multisim 14.3. © 2024 IEEE.

关键词： fixed-time Nash Equilibrium neurodynamic system Non-cooperative games

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Semantic Coherence Guided Multiview Similarity for Image Classification with Varied Supervision

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3072-3082页

作者： Zhang, Chunjie Cao, Ping Bai, Huihui Zheng, Xiaolong Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State of Key Laboratory of Management and Control for Complex System Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

Pairwise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pairwise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pairwise similarity is also influenced by the number and underlying distribution of labeled images. A well-designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this article, we propose a semantic coherence guided multiview similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multiview information, we also use the semantic coherence guided image similarities to learn multiview classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method. © 2020 IEEE.

关键词： Semantics

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Signal Temporal Logic Based Vehicle Trajectory Planning Under Mandatory Stop Rule for Heavy Vehicle Right Turns at Signal-Free Intersections 27

Signal Temporal Logic Based Vehicle Trajectory Planning Unde...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zuo, Wanqing Xu, Yunwen Chen, Youren Li, Dewei School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9798331505929

The blind spots of heavy vehicles during right turns arise a safety concern for traffic. Hence, the regulation mandating heavy vehicles to come to a complete stop before making right turns is crucial in ensuring intersection safety. However, the current trajectory planning methods struggle to integrate these traffic regulations, and the restrictions imposed by such traffic rules greatly impact traffic efficiency at intersections. This paper proposes a signal temporal logic (STL) based trajectory planning method for connected and autonomous vehicles (CAVs) at a signal-free intersection, where traffic rules for heavy vehicles are conveniently described by STL formulas. All CAVs at the intersection are modeled without predefined paths to enhance the flexibility of trajectories. Each STL formula is encoded in a standardized way as a conjunction of inequality and equality constraints for the optimization, and the whole trajectory planning of CAVs is achieved through solving a mixed-integer nonlinear programming (MINLP) problem. Simulation results indicate that our method improves traffic efficiency in the scenario with heavy vehicles right turns. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Channel parameters-independent multi-hop nondestructive teleportation

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Chinese Physics B 2022年第2期31卷 201-208页

作者： Hua-Yang Li Yu-Zhen Wei Yi Ding Min Jiang School of Electronics&Information Engineering Soochow UniversitySuzhou 215006 China 2School of Information EngineeringHuzhou UniversityHuzhou 313000China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

A multi-hop nondestructive teleportation scheme independent of channel parameters based on Bell pairs is presented,where the coefficients of the quantum channel are unknown to all the communication *** Bell measurement and channel matching technology the unknown channel parameters can be eliminated probabilistically with the help of the intermediate *** the source node Alice can teleport an unknown state to the remote destination node *** our scheme the teleportation is generalized first to the scenario independent of channel parameters,which makes the requirement of quantum channel *** scheme still preserves the initial unknown state even if this teleportation ***,performance analysis shows that our scheme has a higher communication efficiency.

关键词： multi-hop quantum communication entanglement swapping teleportation

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and systems for Video Technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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