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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Xu, Jiahong Liu, Xiaofeng Zhou, Xu Wang, Huan Lin, Qiguang Jiangsu Key Laboratory of Special Cloud Robots Changzhou213022 China Changzhou Key Laboratory of Cloud Robots and Intelligent Technology Changzhou213022 China College of IoT Engineering Hohai University Changzhou213022 China

ISBN: (纸本)9781665436786

In our work, we propose a planner based on game theory and artificial potential field (APF) that enables intruder to confront defender in 2-D environment. The intruder would compute the mixed strategy approximate Nash equilibrium in the space of agent APF trajectory to maximally approach the target region while evading the pursuit of defender. First, according to the improved APF, the finite pure strategy matrix of agents is obtained by considering the opponent's response to the agents' actions iteratively. Then, Particle Swarm optimization (PSO) is taken to compute the mixed strategy approximate Nash equilibrium based on the expected profit composed of pure strategy matrix. Final, extensive simulation tests on agents are conducted to measure the performance of the planner we proposed. The simulation results show that our method not only has great advantages in the face of greedy defenders, but also is superior to the traditional APF planning algorithm. © 2021 IEEE.

关键词： Game theory

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Effect of Thermal and Mechanical Training in Twisted and Coiled Polymer Fiber (TCPF) Artificial Muscle for Improved Actuation Consistency 23rd

Effect of Thermal and Mechanical Training in Twisted and Coi...

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23rd IFToMM China International Conference on Mechanism and Machine Science and Engineering, IFToMM CCMMS 2022

作者： Li, Bo Zhang, Yakun Zhang, Yu Wang, Yanjie Formin, Alexey Chen, Guimin Zhang, Yong Shaanxi Key Lab of Intelligent Robots School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research College of Stomatology Xi’an Jiaotong University Xi’an710049 China Jiangsu Provincial Key Laboratory of Special Robot Technology Hohai University Changzhou Campus Jiangsu Changzhou213022 China Moscow101000 Russia Department of Thoracic Surgery The First Affiliated Hospital of Xi’an Jiaotong University Xi’an710061 China

ISBN: (纸本)9789811993978

Twisted and coiled polymer fiber (TCPF) is a new type of artificial muscles featuring large stroke and high loading capacity that can be benefited in soft robotic actuation. However, due to the multi-step fabrication process with various parameter, the existed TCPF suffers from an inconsistent actuation performance, which hinders its further application. The current paper studies the mechanics of TCPF actuation, revealing the significance of coiling and annealing procedure. Effect of twisting weight, annealing temperature and pre-stretch as mechanical and thermal training, are experimentally investigated, and the contraction forces are characterized. With these mechanical and thermal training, consistent and hysteresis-free contraction force is attain. The findings offer a guidance for the high-performance artificial muscle in compliant structures and tensegrity robot. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Muscle

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Research on capsule network optimization structure by variable route planning

Research on capsule network optimization structure by variab...

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Chu, Xin Xu, Ning Liu, Xiaofeng Yao, Xiao College of IoT Engineering Hohai University Changzhou213022 China Changzhou Key Laboratory of Robotics and Intelligent Technology Changzhou213022 China Jiangsu Key Laboratory of Special Robots Changzhou213022 China

ISBN: (纸本)9781728137261

Convolutional Neural Networks (CNN) perform quite good on image classification and object recognition tasks. To solve the problem that CNN is not robust to affine transformation and can not consider the spatial relationship between objects in images, Capsule Network (CapsNet) is proposed and the best accuracy of MNIST dataset has achieved. It shows developability on other datasets and its performance is significantly higher than CNN. However, the data categories in the existing routing algorithms of the CapsNet need to be given in advance, which is very difficult to estimate. In a complex dataset or a small sample dataset, calculating the maximum likelihood solution is more complicated, and it will lead to an inevitable singularity problem. For these shortcomings of the routing algorithm, this paper first proposes a variational routing algorithm. The algorithm does not bring additional computational burden, can automatically determine the most suitable data category, and can effectively avoid over-fitting problems. The algorithm uses the variational distribution to integrate the prior distribution information to increase the regularization limit, maximize the lower bound of the model evidence, and avoid the singularity problem caused by computing the maximum likelihood function. In this paper, the MNIST dataset is classified by a simple CapsNet architecture, and the result is encouraging. © 2019 IEEE.

关键词： Structural optimization

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Relapse risk prediction for children with henoch-schonlein purpura based on GA-SVM

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International Journal Bioautomation 2020年第2期24卷 117-130页

作者： Liu, Yijun Wang, Beihong Li, Renpu He, Sheng Xi, Haixu Luo, Ye Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology 1801 Zhongwu Road Changzhou213001 China Department of Pediatrics Changzhou No. 2 People's Hospital 29 Xinglong Road Changzhou213003 China

The relapse risk prediction for children with Henoch-Schonlein purpura can help pediatricians make an accurate prognosis and offer personalized and appropriate follow-up nursing and relapse control to patients. In this study, we propose a Genetic algorithm-Support vector machine (GA-SVM) learning method combining the support vector machine with the genetic algorithm for parameter optimization to capture the nonlinear mapping from a panel of biomarkers to the relapse risk of HSP children. The GA-SVM prediction model is created by using the dataset of 40 samples in clinical treatment and observation of patients. The inputs of the model consist of 19 biomarkers including gender, age, immunoglobulin M, immunoglobulin G, immunoglobulin A, prothrombin time, etc. The outputs consist of 1 and-1, where 1 indicates high relapse risk and-1 indicates low relapse risk. For comparison, the GS-SVM prediction model based on parameter optimization of grid search is also created. The experimental results show that the GA-SVM prediction model has a high prediction accuracy of 90% and is strong in generalization ability. The GA-SVM model for predicting the relapse risk of HSP children is a promising decision support tool of clinical prognosis, which provides pediatricians with valuable assistance to offer rehabilitation treatment to patients. © 2020 by the authors.

关键词： Forecasting

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Obstacle induced stochastic tree for fast path planning

Obstacle induced stochastic tree for fast path planning

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Liu, Xiaofeng Li, Jinming Hu, Fang Yang, Chenguang College of IoT Engineering Hohai University Changzhou213022 China Changzhou Key Laboratory of Robotics and Intelligent Technology Changzhou213022 China Jiangsu Key Laboratory of Special Robots Changzhou213022 China School of Electromechanical and Automobile Engineerng Changzhou Vacational Institue of Engineering Changzhou213164 China Bristol Robotics Laboratory University of the West of England BristolBS16 1QY United Kingdom

ISBN: (纸本)9781728137261

Rapidly-Exploring Random Tree (RRT) has been extensively applied to robot navigation field as a fast and effective path planning method. Rapidly-Exploring Random Tree Star (RRT*), one of the extensions of RRT, outperforms other methods because it can approach the optimal path with the number of iterations increases. Nonetheless, as the number of sampling points increases, it takes too long to get an optimal path and the convergence speed slows dramatically for RRT and all its variants, since the range of sampling is the entire configuration space. In this paper, we propose a novel method of robot path planning in the global knowledge scenario: obstacle induced stochastic tree algorithm. This algorithm regards the center point in obstacles as the sampling point, which significantly reduces the sampling range then shorts the time for path planning. Simulation results demonstrate that our algorithm can find a feasible path at a rapid rate. Compared with RRT and its variants algorithms, the algorithm has better performance in both time and path cost. © 2018 IEEE

关键词： Motion planning

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LED Visible Light Localization Based on Illuminance Analysis

LED Visible Light Localization Based on Illuminance Analysis

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Chenting Gu Xiaofeng Liu Yongsheng Ou Chao Liu Hao Tao Tianyun Shi HoHai University Nanjing China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Key Laboratory of Special Robots Changzhou China Changzhou Key Laboratory of Robots and Intelligent Technology Changzhou China College of IoT Engineering Hohai University Changzhou China Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China China Ship Development and Design Center Wuhan Tian-yun Shi China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

LED based positioning technology has recently become a popular choice due to the low cost, convenience for maintenance and strong adaptability to the environment. In this paper, we introduce an illuminance-distance analysis method with a rolling shutter camera, for a low-cost indoor positioning system to improve robustness in the case of insufficient LEDs. Firstly, the binary threshold method is used to decode the light signals. Then the gray value information of image is extracted to analyze the depth of LEDs, using the illuminance attenuation theorem. It provides potential solution to the position of devices with the camera by combining depth information, image and camera intrinsic. Our proposed method reduced the minimum number of LEDs observed by the camera from three to two, using only the camera. We evaluated the proposed method with real scene data and demonstrated the effectiveness of illumination-distance model based localization.

关键词： Light emitting diodes Cameras Receivers Light sources robots Mathematical model Image sensors

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Target vehicle detection and tracking in surveillance videos based on exponential forgetting and perceptual hash algorithm

Advances in Transportation Studies

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Advances in Transportation Studies 2018年第Special issue期2卷 97-106页

作者： Fan, H.H. Zhu, H.J. School of Computer Engineering Jiangsu University of Technology Changzhou213001 China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Changzhou213001 China

In order to obtain the stable background of a traffic surveillance video, the application scenarios, computational complexity, and results of the Gaussian background model were analyzed. In the background modeling process, many artifacts were generated, making it difficult to reconstruct a highly stable background image. In order to solve this problem, we proposed a background model based on exponential forgetting algorithm. This method satisfies the background reconstruction of complex environment, the computation is small and the background result is pure. To further improve the accuracy of the test, shadows of the vehicle were detected to reduce the error caused on vehicle detection. The perceptual hash algorithm was used to track the target vehicle. The proposed algorithm is verified by different video data. The experimental results show that a good vehicle tracking effect is achieved in a variety of weather conditions. © 2018, Gioacchino Onorati Editore. All rights reserved.

关键词： Vehicles

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

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A Forward-Secure Multi-Receiver Signcryption Scheme

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Journal of Harbin Institute of technology(New Series) 2016年第5期23卷 53-61页

作者： Zhimin Yu Zhengjun Jing Hua Yang Chunsheng Gu Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou Jiangsu 213001 China College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

In order to protect historical ciphertext when the private key leaked in the broadcasting system,the forward-secure multi-receiver signcryption scheme is designed based on the generic graded multilinear mapping encoding structure,which effectively prevents illegal access from intruder to the ciphertext in the past time period when the private key in current time period is *** the generalization of the existing multilinear mapping encoding system,it proposes the generic graded multilinear mapping encoding structure and the generic graded decision Diffie-Hellman *** of the generic graded multilinear mapping encoding system adopted,almost all candidate multilinear mapping encoding systems can automatically adapt to our *** the assumption of generic graded decision Diffie-Hellman problem,it has proved that the scheme has the information confidentiality and unforgeability in the current time *** putting forward the security model of forward-secure multi-receiver signcryption scheme,and under the assumption of generic graded decision Diffie-Hellman problem,it has proved that the scheme has the message forward-confidentiality and *** with other forward-secure public key encryption schemes,the relationship between our scheme and time periods is sub-linear,so it is less complex.

关键词： multilinear mapping forward-secure multi-receiver signcryption confidentiality unforgeability

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781509003914

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms Data mining Algorithm design and analysis

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