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

作者： Zheng, Liangsheng Ma, Yue Li, Mengyao Xiao, Yang Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781665436786

Motor imagery brain-computer interface system based on Electroencephalogram (EEG) is an effective way to help the disabled recover part of their motor abilities. However, decoding the movement intention contained in the EEG signal accurately presents many challenges. In this paper, we propose a time-frequency decomposition-based weighted ensemble learning (TFDWEL) method, which aims to improve the classification performance of motor imagery EEG signals. The TFDWEL method divides the EEG signal into multiple subsets, and uses four time-frequency processing methods to extract the time-frequency sub-bands of each subset. Then the feature extraction model and classifier model of each subset trained by the common spatial pattern (CSP) algorithm and the support vector machine (SVM) algorithm are used to build a set of base learners. The least square error estimation method is used to learn the weight of each base learner, and finally the weighted summation method is used to obtain the final decision. The classification performance of the TFDWEL method is evaluated on the BCI Competition IV Data Set 2b, and the results show that the classification accuracy of 81.58% can be obtained. Superior classification performance indicates that the TFDWEL method can be used in further research to help the rehabilitation of the disabled. © 2021 IEEE.

关键词： Electroencephalography

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Advances in Swarm intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Ying Tan Yuhui Shi Zhen Ji

ISBN: (数字)9783642310201

ISBN: (纸本)9783642310195

This book and its companion volume, LNCS vols. 7331 and 7332, constitute the Proceedings of the Third International conference on Swarm intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 full papers presented were carefully reviewed and selected from 247 submissions. The papers are organized in 27 cohesive sections covering all major topics of swarm intelligence research and developments.

关键词： Algorithm Analysis and Problem Complexity Information systems Applications (incl. Internet) Artificial intelligence Computation by Abstract Devices Computer Communication Networks Data Mining and Knowledge Discovery

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Advances in Swarm intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Ying Tan Yuhui Shi Zhen Ji

ISBN: (数字)9783642309762

ISBN: (纸本)9783642309755

This book and its companion volume, LNCS vols. 7331 and 7332, constitute the proceedings of the Third International Conference on Swarm intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 revised full papers presented were carefully reviewed and selected from 247 submissions. The papers are organized in 27 cohesive sections covering all major topics of swarm intelligence research and developments.

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Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation

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Journal of Bionic Engineering 2018年第4期15卷 751-763页

作者： Liwu Xu Yuanzheng Li Kaicheng Li Gooi Hoay Beng Zhiqiang Jiang Chao Wang Nian Liu State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical Engineering and Electronics Huazhong University of Science and Technology Wuhan 430074 China School of Automation Ministry of Education Key Laboratory of lmage Processing and Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China Nanyang Technological University Singapore 639798 Singapore School of Hydropower and lnformation Engineering Huazhong University of Science and Technology Wuhan 430074 China China Institute of Water Resources and Hydropower Research Beijing 100038 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beij'ing 102206 China

This paper presents an Enhanced Moth-Flame Optimization （EMFO） technique based on Cultural Learning （CL） and Gaussian Mutation （GM）. The mechanism of CL and the operator of GM are incorporated to the original algorithm of Moth-Flame Optimization （MFO）. CL plays an important role in the inheritance of historical experiences and stimulates moths to obtain information from flames more effectively, which helps MFO enhance its searching ability. Furthermore, in order to overcome the disadvantage of trapping into local optima, the operator of GM is introduced to MFO. This operator acts on the best flame in order to generate several variant ones, which can increase the diversity. The proposed algorithm of EMFO has been comprehensively evaluated on 13 benchmark functions, in comparison with MFO. Simulation results verify that EMFO shows a significant improvement on MFO, in terms of solution quality and algorithmic reliability.

关键词： bioinspired computing moth-flame optimization cultural learning Gaussian mutation benchmark functions

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Continuous ZND (Zhang Neural Dynamics) Model for Generalized Sinkhorn Scaling of Time-Varying Matrix 11

Continuous ZND (Zhang Neural Dynamics) Model for Generalized...

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11th International Conference on Intelligent control and Information Processing, ICICIP 2021

作者： Xiao, Jianzhen Chen, Canhui Zhang, Yunong Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen518057 China

ISBN: (纸本)9781665425155

In this paper, we first propose a Zhang neural dynamics (ZND) model for the generalized Sinkhorn scaling of time-varying matrix. Specifically, by using the dimensional reduction technique, a continuous-time ZND model of time-varying matrix scaling is proposed and analyzed. In addition, the corresponding theoretical proofs are given, which prove the theoretical validity of the proposed ZND model. Moreover, two numerical experiments containing a square case and a rectangle case are also conducted. Numerical experiments and results substantiate the effectiveness and accuracy of the proposed ZND model. © 2021 IEEE.

关键词： Continuous time systems

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No 8-Instant ZTD (Zhang Time Discretization) Formula with Quintic Precision or Higher as Proved

No 8-Instant ZTD (Zhang Time Discretization) Formula with Qu...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Yunong Zheng, Kangze Guo, Jinjin School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen518057 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China

ISBN: (纸本)9781665426473

Since 2014, Zhang time discretization (ZTD, also termed Zhang et al. discretization) formulas have been put forward as a new method for time discretization by Zhang et al. Originally, ZTD formulas were used to obtain discrete Zhang neural network (ZNN) model from continuous ZNN one. ZTD formulas were also used later to discretize other continuous-time systems. So far, ZTD formulas with various number of instants have been proposed and applied, and the highest precisions of the 2-instant ZTD formula to 7-instant ZTD formulas have been discovered. Instinctively, we want to find out the highest precision of 8-instant ZTD formulas. During our investigation, we ascertain that any convergent 8-instant ZTD formula cannot possess quintic precision or higher. Combined with previous work, we conclude that any 8-instant ZTD formula converges with a truncation error in proportion to the 4-th power of the sampling interval or greater. The conclusion is proved in our paper. © 2021 IEEE

关键词： Continuous time systems

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DPF-SLAM: Dense semantic SLAM based on dynamic probability fusion in dynamic environments

DPF-SLAM: Dense semantic SLAM based on dynamic probability f...

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

作者： Liu, Xin Wen, Shuhuan Yuan, Mingxing Li, Pengjiang Zhao, Yongjie Manfredi, Luigi Yanshan University Eng. Res. Ctr. of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Nankai University School of Artificial Intelligence China University of Dundee United Kingdom

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the development of simultaneous localization and mapping (SLAM) technology, Dynamic SLAM has been a challenging research topic. This paper presents a dynamic probability fusion SLAM (DPF-SLAM) algorithm, which adds a semantic segmentation thread and a dense reconstruction thread to ORB-SLAM2. We integrate dynamic prior probability obtained by semantic segmentation with dynamic probability obtained by dynamic point detection, which can decrease the effect of dynamic objects on the localization accuracy and meanwhile achieve the dense reconstruction of a static background. We evaluate DPF-SLAM system on the public TUM RGBD dataset. The experimental results show that the proposed algorithm has better localization accuracy than DS-SLAM and ORB-SLAM2, which are two relatively new algorithms in this area, and can obtain a good dense reconstruction effect. Moreover, through the performance comparison with our previous work, it is found that the algorithm speed and positioning accuracy are improved. © 2022 IEEE.

关键词： Semantics

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

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6th International Symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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A novel soft exosuit based on biomechanical analysis for assisting lower extremity 22

A novel soft exosuit based on biomechanical analysis for ass...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Liu, Youfu Chen, Chunjie Lu, Jun Liu, Yida Zhang, Yu Wang, Zhuo College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%. © 2021 IEEE.

关键词： Exoskeleton (Robotics)

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A novel gait prediction method for soft exosuit base on limit cycle and neural network 22

A novel gait prediction method for soft exosuit base on limi...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Chen, Lingxing Chen, Chunjie Fang, Tao Zhang, Yu Liu, Youfu Wu, Xinyu Shenzhen Institutes of Adanced Technology Chinese Academy of Sciences Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

The important purpose of soft exosuit is to decrease the energy consumption of users by providing assistance. If there is something wrong with the judgment of the human gait during the assisting process, the assisting effect will be poor and even people's walking will be affected. A novel method is designed to predict human gait information in the paper, which utilizes the curve shape and mathematical characteristics of the Rayleigh oscillator equation in limit cycle to fit gait information. Only 1/4 gait cycle data is needed to input into the trained neural network to output an Rayleigh oscillator equation that can better predict the remaining gait cycle information. Data of four subjects on different terrains which include flat ground and upstairs are collected. Experiment results showed that the trained ANN model costs about 0.006s in CPU, it has a prediction speed similar to traditional prediction methods. Simultaneously the Rayleigh oscillator equation has good performance in predicting gait information, and it can show a higher stability and accuracy compared with traditional gait prediction methods. © 2021 IEEE.

关键词： Forecasting

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