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Ripple-Oriented Degree Distribution of Rateless Codes: A Backward Design Approach for Finite Block Lengths

IEEE Transactions on Vehicular Technology 2025年

作者： Huang, Haobo Yin, Junpeng Fu, Yusun Wang, Zihao Shanghai Jiao Tong University Ningbo Artificial Intelligence Institute Ningbo315000 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Ministry of Education of China School of Electronic Information and Electrical Engineering the Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Rateless codes are a class of error correction codes designed for efficient and reliable data transmission. Luby transform (LT) codes are the first realization of rateless codes. The degree distribution is crucial for the performance of LT codes, but the existing degree distributions do not perform well in finite block lengths, especially in short block lengths. Different from the forward design, such as robust soliton distribution (RSD), we provide a backward design of degree distribution for LT codes in finite block lengths, which starts from the detailed design of ripple size evolution and then arrives at the corresponding degree distribution. This design pattern addresses the limitation of the traditional forward design that cannot guarantee a high probability of successful decoding under finite block lengths. Specifically, we argue that the ripple should increase and then decrease during the decoding process by analyzing the trade-off between redundancy and ripple disappearance. Therefore, a new random walk model is proposed based on adjustable release probability to describe the ideal ripple size evolution. Furthermore, we present a novel design framework to achieve the desired ripple size evolution and the resulting degree distribution is called ripple-oriented degree distribution (RODD). Simulation results show that RODD is superior to other degree distributions in decoding success rate, block error rate, complexity, etc. 1967-2012 IEEE.

关键词： Size distribution

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Research on Vibration Isolation control of Six wheel-legged Robot Based on Impedance control

Research on Vibration Isolation Control of Six wheel-legged ...

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第三十九届中国控制会议

作者： Binkai Yue Shoukun Wang Zhihua Chen Kang Xu Junzheng Wang Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology

The electric parallel six wheel-legged robot（EPSWLR） is characterized by heavy load and fast moving speed. But the body will shake and vibrate while passing through rough roads, which affects its stability. In order to solve this problem, the single-leg structure based on Stewart structure was equivalent to the impedance system composed of mass block, spring and damping in this paper. Due to changes in environmental forces and environmental impedance parameters caused by environmental changes, the traditional impedance control（IC） still causes force error at the foot end of robot and thus results in poor vibration isolation effect. Therefore, a new adaptive algorithm was proposed in this paper and added to IC algorithm to reduce the force error caused by environmental changes and thus improve the vibration isolation effect. In this paper, the characteristic equation of adaptive impedance control（AIC） algorithm was first obtained and the condition for system stability was calculated based on Routh-Hurwitz stability criterion. Finally, the stability of this system was verified. The simulation experiment combining MATLAB and Adams was designed and performed with EPSWLR. The effectiveness of AIC was proved by monitoring the changes in the force of single leg and the attitude of robot body.

关键词： Electric parallel six wheel-legged robot Impedance control Adaptive algorithm Stewart structure

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Distributed Observer Design for Achieving Asymptotical Omniscience over Time-variant Disconnected Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 3565-3570页

作者： Haotian Xu Jingcheng Wang Hongyuan Wang Shangwei Zhao Hai Lin Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi 710021 China

This paper investigates the distributed observer design for linear system under time-variant disconnected communication network. By constructing basic eigenvectors of 0-eigensubspace of disconnected Laplacian Matrix and using LMIs method, we prove the distributed observer cannot achieve omniscience asymptotically under switching topology without constraining the system matrix or alternative topologies set. To deal with this problem, this paper investigates three kinds of constraints and the system matrix only needs to satisfy any one of them. Then a group of sufficient conditions corresponding to the asymptotic omniscience of distributed observer under switching topology are proved by Lyapunov analysis. Finally, a numerical simulation shows the validity of our method.

关键词： Distributed observer Continuous time system estimation Consensus Sensor networks Switching topology

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An Optimal Tracking control Method with Q-learning for Discrete-time Linear Switched system

An Optimal Tracking Control Method with Q-learning for Discr...

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第三十九届中国控制会议

作者： Shangwei Zhao Jingcheng Wang Hongyuan Wang Haotian Xu Automation Department Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

In this paper, a novel approach combining Q-learning method and two-stage optimization is proposed to solve linear quadratic tracking(LQT) problem for unknown discrete-time switched system. An augmented system consisting of switched subsystem and reference trajectory is built and Q-learning method is introduced to identify each subsystem without requiring any knowledge of augmented system state dynamics. Then, based on the optimal control laws for each subsystem obtained by Q-learning in advance, the optimal hybrid control law including switching mode and control input is obtained by the two-stage optimization framework. Furthermore, the optimality of the algorithm is proved. Finally, a simulation case is used to testify the effectiveness of the proposed algorithm.

关键词： Tracking control Linear switched system Q-learning

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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Model-Predictive-control-based Simultaneous Trajectory Tracking and Speed control for intelligent Vehicles*

Model-Predictive-Control-based Simultaneous Trajectory Track...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Hanyang Zhuang Yikai Hu Chunxiang Wang Ming Yang University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and Technology Liuzhou China Shanghai Jiao Tong University Shanghai Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665431545

Trajectory tracking and speed control for intelligent vehicles are put forward higher requirements on accuracy by the high traffic density scenario. A standard Model Predictive control (MPC) algorithm focuses on the trajectory tracking but ignores the speed control. This paper proposes a simultaneous trajectory tracking and speed control algorithm based on MPC approach to address fine control in both space and time dimensions for intelligent vehicles. A threefold cost function including lateral and longitudinal speed as well as comfort has been proposed to improve the control algorithm. The simulations and comparisons demonstrate that the proposed method has a better tracking and speed control performance than the existing method.

关键词： Space vehicles Costs Mechatronics Trajectory tracking intelligent vehicles Velocity control Prediction algorithms

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Fast S-transform for fault line selection in distribution network system 13

Fast S-transform for fault line selection in distribution ne...

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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Shang-Bin, Jiao Yun-Peng, Yang Yan-Jin, Shi Wen-Wen, Shi Jing-Wen, Guo Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9781538637579

The current of the distribution networks under 66kV is widely operating in small current grounding mode. The problem of extracting the fault feature in each line would be quite difficult due to the short duration of the transient zero sequence current with the non-stationary and non-linear characteristics when the single-phase-to-ground fault occurs in the system. Therefore, a novel method of single-phase-to-ground fault line selection for small current based on Fast S-transform (FST) is developed in this paper. Firstly, FST is presented to transform the transient electric current signal in each feeder, after that the one-dimension modulus and phase angle value can been obtained. Then, the feeder with the largest energy value calculated by the modulus obtained above is selected as the alternative possible fault line. Finally, line failure or bus failure can been determined by evaluating the difference of the phase angle between the fault line and the comparison line that has been selected from the non-alternative feeders before. The results of the simulative and experimental circuits reveal that the proposed method using FST can avoid the influence of neural point grounding type. Additionally, the method of line selection is quite accurate and real-time. © 2018 IEEE.

关键词： Electric grounding

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A strategy of multi-UAV cooperative path planning based on CCPSO

A strategy of multi-UAV cooperative path planning based on C...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： Liu, Yuanna Lu, Hao Northwestern Polytechnical University Dept. School of Electronics and Information Xi'an China Intelligent Agent Cooperation Laboratory Dept. Science and Technology on Complex System Control Beijing China

ISBN: (纸本)9781728137926

Multi-UAV cooperative formation flying has significant advantages in broadening the scope of UAV operations and improving combat effectiveness. How to realize the cooperative path planning of multi-UAV is an open issue. In order to solve the search performance degradation as dimension increases, a strategy of multi-UAV cooperative path planning based on Cooperative Coevolution Particle Swarm Optimization (CCPSO) is proposed in this paper. Firstly, the initial Dubins path is generated for each UAV in the formation, of which the longest one is selected as the reference path. Intermediate waypoints are designed to modify the initial paths to avoid collisions with obstacles. Then, CCPSO algorithm is applied to optimize path parameters and adjust the path length to be equal to the reference path to ensure time coordination. Collision avoidance between UAVs is achieved in the collaborative evaluation phase. Finally, an formation rendezvous example and comparison with CCGA are given to explain the validity and excellent search ability of the proposed method. © 2019 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain Stimulation Electrode Implantation

Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain...

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IEEE International Conference on Robotics and Biomimetics

作者： Weihang Huang Shenghao Dai Anze Xu Xianyi He Shaoping Huang Anzhu Gao School of Mechanical Engineering Shanghai Jiao Tong University Shanghai Jiao Tong University Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665405362

Deep brain stimulation (DBS) has been widely used in clinical practice as an effective therapy for neurological disorders. MR-conditional robot-assisted electrode implantation has attracted more attention for DBS because of its great accuracy and high-quality guiding images by using MRI. This paper develops a pneumatically actuated MR-safe parallel robot for deep brain electrode implantation. Its fully plastic components and MR-safe pneumatic actuators enable the robotic system to be MR-safe, posing no known hazards in MR environment. A 6-DOF parallel robot is developed using six slider-crank mechanisms for prismatic motions. Inverse kinematics is built to investigate its workspace and achieve the precise control. Experiments are carried out to validate the feasibility, and results shows that the average error of the motion is 0.11 mm. The developed parallel robot shows the potential advantages in the automatic and precise robot-assisted neurosurgical surgery in future.

关键词： Electrodes Pneumatic actuators Neurological diseases Parallel robots Magnetic resonance imaging Satellite broadcasting Deep brain stimulation

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A New Intrusion Detection Method Based on Adaptive Feature Extraction

A New Intrusion Detection Method Based on Adaptive Feature E...

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第三十八届中国控制会议

作者： Ya-Li Wu Guo-Ting Li Yu-Long Fu Xiao-Peng Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Compared with traditional machine learning, intrusion detection based on deep learning has the advantage of feature extraction. Auto-encoders, owing to having good feature extraction and dimensionality reduction ability, are known for the superior performance. However, the network structure selection is time-consuming and laborious. The essential features cannot be automatically extracted according to different distribution characteristics of the data set. Moreover, the existing data pre-processing method fails to adapt the model better. Based on the stack sparse auto-encoders(SSAE), we propose a new intrusion detection model named DBSO-SSAE. The algorithm is based on difference brain storm optimization(DBSO) which automatically adapts to selection network framework. Meanwhile, a new normalization strategy is introduced to further improve the accuracy of the model. The simulation results show that DBSO-SSAE greatly reduces the complexity of manual tuning and increases the adaptability of the network model.

关键词： intrusion detection feature extraction SSAE deep learning difference brain storm optimization

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