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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Jiang, Xu Guan, Nan Liang, Haochun Tang, Yue Qiao, Lei Yi, Wang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China City University of Hong Kong Hong Kong Beijing Institute of Control Engineering China Uppsala University Sweden

ISBN: (纸本)9781665428026

Federated scheduling is a promising approach to schedule parallel real-time tasks, where each task exclusively executes on a set of dedicated processors. However, federated scheduling suffers significant resource wasting since a task typically only uses part of the processing capacity allocated to it, while the unused part cannot be shared with other tasks. To solve this problem, we present a virtually-federated scheduling approach, which both enjoys the good analyzability of federated scheduling and allows tasks to efficiently share processors with others. The main idea is to construct virtual processors on physical processors, and let a task exclusively execute on a set of virtual processors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. On the other hand, as each task exclusively executes on its own virtual processor set, the good analyzability of federated scheduling can be carried into to our virtually-federated scheduling approach. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings. ©2021 IEEE

关键词： Scheduling

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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

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

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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Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse Optimal control 13

Nash Pursuit Strategy for Nonzero-Sum MPC Game via Inverse O...

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13th Asian control Conference, ASCC 2022

作者： Qiu, Tianyu Zhang, Han Wang, Jingchuan Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9788993215236

In this paper, a traditional one v.s. one pursuit and evasion scenario is considered. The evader aims to reach her target and avoid being captured by the pursuer, while the pursuer, without the knowledge of the evader's cost function, intends to capture the evader before she successfully escapes. We formulate this problem as a Model Predictive control Decoupled Pursuit and Evasion Game (MPC-DPEG) model with incomplete information. We apply Inverse Optimal control (IOC) technique to estimate the evader's cost function and give a Nash pursuit strategy for MPC-DPEG as the optimal strategy for the pursuer. Numerical examples reveal the improvement of pursuit performance of the Nash pursuit strategy based on the estimated cost function over naive pursuit strategies. © 2022 ACA.

关键词： Simulation Optimal control Games Predictive models Cost function Numerical models Predictive control

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Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning

arXiv

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arXiv 2023年

作者： Li, Zhi Xin, Jinghao Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with DRL, and first propose a Cumulative Curriculum Reinforcement Learning (CCRL) training framework to alleviate the issue of catastrophic forgetting faced by general CL. Besides, we present a novel state representation, which considers a local egocentric map and a global exploration map resized to the fixed dimension, so as to flexibly adapt to environments with various sizes and shapes. Additionally, for facilitating the fast training of DRL models, we develop a lightweight grid-based simulator, which can substantially accelerate simulation compared to popular robot simulation platforms such as Gazebo. Based on the customized simulator, comprehensive experiments have been conducted, and the results show that the CCRL framework not only mitigates the catastrophic forgetting problem, but also improves the sample efficiency and generalization of DRL models, compared to general CL as well as without a curriculum. Our code is available at https://***/BeamanLi/CCRL_Exploration. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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A Born-Again Takagi-Sugeno-Kang Fuzzy Classifier with Decoupled Fuzzy Dark Knowledge Distillation

SSRN

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SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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Indirect effects among biodiversity loss of mutualistic ecosystems

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National Science Open 2022年第2期1卷 8-18页

作者： Guangwei Wang Xueming Liu Guanrong Chen Hai-Tao Zhang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and TechnologyWuhan 430074China State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical Engineering City University of Hong KongHong KongChina

Drastic reduction in biodiversity has been a severe threat to ecosystems,which is exacerbated when losing few species leads to disastrous and even irreparable ***,revealing the mechanism underlying biodiversity loss is of uttermost *** this study,we show that abundant indirect interactions among mutualistic ecosystems are critical in determining species’*** topological and ecological characteristics,we propose an indicator derived from a dynamic model to identify keystone species and quantify their influence,which outperforms widely-used indicators like degree in realistic and simulated ***,we demonstrate that networks with high modularity,heterogeneity,biodiversity,and less intimate interactions tend to have larger indirect effects,which are more amenable in predicting decline of biodiversity with the proposed *** findings shed some light onto the influence of apposite biodiversities,paving the way from complex network theory to ecosystem protection and restoration.

关键词： biodiversity indirect effect complex network mutualism

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Research and Application of Composite Stochastic Resonance In Enhancement Detection

SSRN

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SSRN 2023年

作者： Gao, Rui Jiao, Shangbin Xue, Qiongjie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi’an710048 China School of Electronic and Electrical Engineering Baoji University of Arts and Sciences Baoji721016 China

Aiming at the problem that the intermediate potential part of the traditional tristable method cannot be adjusted independently, a new composite stochastic resonance (NCSR) model is proposed by combining the Woods Saxon (WS) model and the improved piecewise bistable model. All the parameters in the new model have no coupling characteristics. Moreover, the model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation. Understable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has better noise utilization and better detection effect. Finally, through image denoising experiments, the output signal-to-noise ratio (SNR) and correlation number of NCSR method is higher than that of other commonly used linear denoising methods and classical SR methods under the same conditions, the effectiveness of the new model is verified. © 2023, The Authors. All rights reserved.

关键词： image denoising

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OadTR: Online Action Detection with Transformers

OadTR: Online Action Detection with Transformers

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International Conference on Computer Vision (ICCV)

作者： Xiang Wang Shiwei Zhang Zhiwu Qing Yuanjie Shao Zhengrong Zuo Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China

ISBN: (纸本)9781665428132

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism and gradient vanishing, hence it is hard to be optimized. In this paper, we propose a new encoder-decoder framework based on Transformers, named OadTR, to tackle these problems. The encoder attached with a task token aims to capture the relationships and global inter-actions between historical observations. The decoder extracts auxiliary information by aggregating anticipated future clip representations. Therefore, OadTR can recognize current actions by encoding historical information and predicting future context simultaneously. We extensively evaluate the proposed OadTR on three challenging datasets: HDD, TVSeries, and THUMOS14. The experimental results show that OadTR achieves higher training and inference speeds than current RNN based approaches, and significantly outperforms the state-of-the-art methods in terms of both mAP and mcAP. Code is available at https://***/wangxiang1230/OadTR.

关键词： Training Visualization Recurrent neural networks Streaming media Transformers Encoding Spatiotemporal phenomena

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A pushing-grasping collaborative method based on deep q-network algorithm in dual perspectives

arXiv

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arXiv 2021年

作者： Gang, Peng Jinhu, Liao Shangbin, Guan Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Aiming at the traditional grasping method for manipulators based on 2D camera, when faced with the scene of gathering or covering, it can hardly perform well in unstructured scenes that appear as gathering and covering, for the reason that can’t recognize objects accurately in cluster scenes from a single perspective and the manipulators can’t make the environment better for grasping. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual perspectives is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point cloud from two perspectives, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

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