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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Yang, Jing Song, Zhen Wu, Ruikai Wang, Jingchuan Dalian Polytechnic University China Min. of Educ. of China Shanghai Eng. Res. Ctr. of Intelligent Contr. and Mgmt. Inst. of Med. Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the intensification of the aging trend, the promotion of the design of aging facilities is conducive to improving the livelihood and welfare of the elderly and is of great significance to promoting the comprehensive, coordinated and sustainable development of the cause of aging. The transfer concept of the robot designed in this article is to enable the elderly and the disabled who are living at home to complete the actions of standing, transferring, and sitting like normal people. In view of the lack of analysis of the user's own body state in the current transfer assist equipment in the design process, the robot's lifting mechanism is optimized from the perspective of biomechanics, and finally the optimal size parameter combination of the robot's lifting mechanism is obtained. © 2022 IEEE.

关键词： Machine design

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A Standing Assist Trajectory Generation Method of Transfer Robot

A Standing Assist Trajectory Generation Method of Transfer R...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

The increase in the number of the elderly and the disabled in China has brought a huge pressure to daily nursing work. Standing assistance for the elderly and the disabled is an important part of daily nursing work. Robots are used to assist nursing staff in completing this work, which reduces the work pressure of nursing staff. This article focuses on the research of a transfer robot system used to assist the elderly and the disabled to stand and transfer. Since the predefined standing assist trajectory need to collect the user's standing motion, this paper proposes a standing assist trajectory generation method based on the direct collocation method, which can generate the standing assist trajectory according to the user's height, weight and body state. © 2022 IEEE.

关键词： Trajectories

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Object relationship graph reasoning for object detection of remote sensing images 6

Object relationship graph reasoning for object detection of ...

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6th International Conference on Image, Vision and Computing, ICIVC 2021

作者： Li, Zeng Liu, Yifan Liu, Jingdong Yuan, Ye Raza, Asif Huo, Hong Fang, Tao 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)9781665443685

In recent years, deep learning has been successfully applied in object detection of remote sensing images, due to its powerful feature extraction and representation capabilities. However, it usually ignores the relationships among different objects, which greatly hinders the further improvement of the detection precision. In this paper, a novel object relationship graph reasoning deep learning method is proposed for object detection of remote sensing images. The reasoning is carried out on a graph constructed by region proposals. It mainly contains three modules for relationship generation in consideration of different factors that related to object detection: 1) Location-based relationship generation module (LRGM) aims to find spatial relationships among objects according to their positions;2) Feature-based relationship generation module (FRGM) focuses on mining semantic relationships among objects according to their appearances that implied in feature maps and 3) Class-dependent relationship generation module (CRGM) introduces class dependencies into the relationship generation process. Class dependencies are the prior knowledge that is obtained by computing class co-occurrence. The experimental results on a public twenty-class remote sensing object detection dataset have shown that the proposed method has better detection performance compared to other object detection deep networks with no considering object relationships. © 2021 IEEE.

关键词： Object detection

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Multi-target tracking control system of phased array antenna with mechanical scanning and beam scanning method

Multi-target tracking control system of phased array antenna...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Jijun Ma Dewei Li Lianghai Li Shibei Xue Yuning Wang Jun Jia Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Beijing Research Institute of Telemetry Beijing China

In the field of telemetry, track, and command system, this study presents a phased array antenna control system for achieving multi-target tracking tasks through a set of systems. Firstly, electric beam scanning of a planar array phased array antenna is used in this paper to implement multi-target tracking. In addition, the phased array antenna is mounted on the antenna pedestal, which can operate on the azimuth and elevation mechanical axes. Thirdly, the mechanical scanning method of the azimuth-elevation antenna pedestal and the beam scanning method of the phased array antenna are combined to realize multi-target tracking tasks. Finally, the experiments of flight tracking are completed by employing the technique of UAV tagging, and the results validate the viability of the system.

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Identity Re recognition Technology Based on Improved Lightweight ConvNetx Model

Identity Re recognition Technology Based on Improved Lightwe...

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Chinese control Conference (CCC)

作者： Peng Siting Li Lei Hongbo Zhu Ren Jun Song Zhichao Jiabing Yang The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions.

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Optimal Sequential False Data Injection Attack Scheme: Finite-Time Inverse Convergence

Optimal Sequential False Data Injection Attack Scheme: Finit...

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IEEE Conference on Decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Peng Cheng Jianping He The Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China

In this paper, we explore the relationship between the injected attack signal and the attack selection strategy in networked control systems where the adversary desires to steer the system state to the expected malicious one. We construct a sequential attack framework, i.e., the injected false data varies with the sampling time in discrete-time systems, and then derive an optimal sequential FDI attack strategy. The optimal sequential FDI attack strategy reveals the strongly coupled relationship between the injected attack signal and the attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack strategy and saves computing sources. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack and the relationship between the attack signal and the attack selection strategy.

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DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation

arXiv

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arXiv 2022年

作者： Shen, Yueling Wang, Guangming Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China

3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These methods make the network structure complicated and limit the improvement of MOT accuracy. we proposed a 3D MOT framework based on simultaneous optimization of object detection and scene flow estimation. In the framework, a detection-guidance scene flow module is proposed to relieve the problem of incorrect inter-frame assocation. For more accurate scene flow label especially in the case of motion with rotation, a box-transformation-based scene flow ground truth calculation method is proposed. Experimental results on the KITTI MOT dataset show competitive results over the state-of-the-arts and the robustness under extreme motion with rotation. © 2022, CC BY-NC-SA.

关键词： Object recognition

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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Vehicle detection based on point cloud intensity and distance clustering 5

Vehicle detection based on point cloud intensity and distanc...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Enabling Stateful TCP Performance Profiling with Key Event Capturing

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IEEE Transactions on Network and Service Management 2025年

作者： Geng, Ruopeng Lu, Jianyuan Fang, Chongrong Zhang, Shaokai Zhao, Jiangu Zong, Zhigang Lyu, Biao Zhu, Shunmin Cheng, Peng Chen, Jiming Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Alibaba Group Hangzhou310052 China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

TCP ensures reliable transmission through its stateful implementation and remains crucial today. TCP performance profiling is essential for tasks like diagnosing network performance problems, optimizing transmission performance, and developing new TCP variants, etc. Existing profiling methods lack enough attention to TCP state transition to provide detailed insights on TCP performance. Thus, we build TcpSight, a tool focusing on TCP state transition throughout connection lifetimes. TcpSight conducts stateful analysis by capturing key events using an efficient per-connection lock-free data management mechanism. Besides, TcpSight enhances profiling by integrating application layer information collected from the TCP stack. With the profiling results, users can identify the culprit of TCP performance degradation, and evaluate the performance of TCP algorithms. We design optional modules and filtering mechanisms to reduce TcpSights overhead. Our evaluation presents that TcpSight incurs an additional CPU consumption of about 16.6% (without filtering) and 10.6% (with filtering) when the server's load is 55.7%, and generates storage consumption about 1.88 KB per connection on average. We also give application cases of TcpSight and the deployment experiences in Alibaba Cloud. TcpSight helps in revealing meaningful findings and insights into exploiting TCP in the production deployment. © 2004-2012 IEEE.

关键词： information management

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