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Research on optimization method of routing buffer linkage based on Q-learning

Journal of Physics: Conference Series 2022年第1期2187卷

作者： Zhonghua Han Hongfeng Qi Daliang Chang Wei Gong Shilong Dai School of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China. Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 China. Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified.

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Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Michael Pritchard Abraham Itzhak Weinberg John A R Williams Felipe Campelo Harry Goldingay Diego R Faria Aston Robotics Vision and Intelligent Systems Laboratory (ARVIS Lab) College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Aston Institute of Photonic Technologies Aston University Birmingham B4 7ET UK

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

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Targeted Delivery: An Integrated Microrobotic Platform for On‐Demand, Targeted Therapeutic Interventions (Adv. Mater. 6/2014)

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Advanced Materials 2014年第6期26卷

作者： Stefano Fusco Mahmut Selman Sakar Stephen Kennedy Christian Peters Rocco Bottani Fabian Starsich Angelo Mao Georgios A. Sotiriou Salvador Pané Sotiris E. Pratsinis David Mooney Bradley J. Nelson Institute of Robotics and Intelligent Systems ETH Zurich Zurich CH‐8092 Switzerland Wyss Institute for Biologically Inspired Engineering School of Engineering and Applied Sciences Harvard University Cambridge MA 02318 USA Micro and Nanosystems ETH Zurich Zurich CH‐8092 Switzerland Particle Technology Laboratory ETH Zurich Zurich CH‐8092 Switzerland Center for Nanotechnology & Nanotoxicology Harvard School of Public Health Boston MA 02115 USA

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Entropy-Modulated Short-Chain Cathode for Low-Temperature All-Solid-State Li−S Batteries

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Angewandte Chemie 2024年第1期137卷

作者： Wei Zhao Yan Zhang Qingsong Liu Yajie Song Xu Li Liping Ren Geping Yin Shuaifeng Lou Jiajun Wang MOE Engineering Research Center for Electrochemical Energy Storage and Carbon Neutrality in Cold Regions School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China These authors contributed equally. State Key Laboratory of Robotics and Systems (HIT) Harbin Institute of Technology Harbin 150001 China Suzhou Research Institute of HIT Suzhou 215104 P. R. China

All-solid-state Li−S batteries (ASSLSBs) due to high theoretical energy density and exceptional safety are highly desirable for electric aircraft. However, as the flight altitude rises, the low-temperature performance is hampered by inadequate practical capacity. Here, we discover that low-temperature sulfur utilization is constrained by the multi-step endothermic conversion reaction. By introducing multi-chalcogen to modulate the local entropy, a short-chain molecule cathode is designed to shorten the reduction pathways and enhance low-temperature discharge capacity. Furthermore, the mismatched lithiation lattice of the short-chain cathode reduces the decomposition energy barriers, thus enhancing low-temperature charge/discharge reversibility. The designed short-chain cathode exhibits high cathode utilization (99.4 %) and cycling stability (400 cycles, 92.2 % retention) at room temperature, as well as delivers excellent discharge capacity (579.6 mAh g −1 , −40 °C) and cycling performance (100 cycles, 98.4 % retention, 394.9 Wh kg − 1electrode, −20 °C) at low temperature. This study presents new opportunities to stimulate the development of low-temperature ASSLSBs.

关键词： All-solid-state Li−S batteries low temperature reaction kinetic short-chain molecule high cathode utilization

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Editorial Special issue on approximate dynamic programming and reinforcement learning

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控制理论与应用（英文版） 2011年第3期 309-309页

作者： Silvia Ferrari Jagannathan Sarangapani Frank L. Lewis Laboratory for Intelligent Systems and Control(LISC)Department of Mechanical Engineering & Materials Science Box 90300 Duke University Durham NC 27708-0005 Department of Electrical & Computer Engineering University of Missouri-Rolla MO 65401 U.S.A. Automation and Robotics Research Institute The University of Texas at Arlington 7300 Jack Newell Blvd. S Ft. Worth Texas 76118-7115 U.S.A.

We are extremely pleased to present this special issue of the Journal of Control Theory and *** dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain environments over *** optimizes the sensing objectives accrued over a future time interval with respect to an adaptive control law,conditioned on prior knowledge of the system,its state,and uncertainties.A numerical search over the present value of the control minimizes a Hamilton-Jacobi-Bellman (HJB) equation providing a basis for real-time,approximate optimal control.

关键词： reinforcement learning dynamic programming optimal control effective approach prior knowledge Control Theory real-time control law problems special search future ADP basis

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Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection

arXiv

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arXiv 2019年

作者： Han, Changhee Rundo, Leonardo Araki, Ryosuke Nagano, Yudai Furukawa, Yujiro Mauri, Giancarlo Nakayama, Hideki Hayashi, Hideaki Machine Perception Group Graduate School of Information Science and Technology University of Tokyo Tokyo113-8657 Japan Research Center for Medical Big Data National Institute of Informatics Tokyo100-0003 Japan Department of Radiology University of Cambridge CambridgeCB2 0QQ United Kingdom Cancer Research UK Cambridge Centre CambridgeCB2 0RE United Kingdom Department of Informatics Systems and Communication University of Milano-Bicocca Milan20126 Italy Machine Perception and Robotics Group Graduate School of Engineering Chubu University Aichi487-8501 Japan Department of Psychiatry Jikei University School of Medicine Tokyo105-8461 Japan Human Interface Laboratory Department of Advanced Information Technology Kyushu University Fukuoka819-0395 Japan

Convolutional Neural Networks (CNNs) can achieve excellent computer-assisted diagnosis performance, relying on sufficient annotated training data. Unfortunately, most medical imaging datasets, often collected from various scanners, are small and fragmented. In this context, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution;researchers have improved classification by augmenting images with noise-to-image (e.g., random noise samples to diverse pathological images) or image-to-image GANs (e.g., a benign image to a malignant one). Yet, no research has reported results combining (i) noise-to-image GANs and image-to-image GANs or (ii) GANs and other deep generative models, for further performance boost. Therefore, to maximize the DA effect with the GAN combinations, we propose a two-step GAN-based DA that generates and refines brain MR images with/without tumors separately: (i) Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for highresolution image generation, first generates realistic/diverse 256 256 images-even a physician cannot accurately distinguish them from real ones via Visual Turing Test;(ii) UNsupervised Image-to-image Translation or SimGAN, image-to-image GAN combining GANs/Variational AutoEncoders or using a GAN loss for DA, further refines the texture/shape of the PGGAN-generated images similarly to the real *** thoroughly investigate CNN-based tumor classification results, also considering the influence of pre-training on ImageNet and discarding weird-looking GAN-generated images. The results show that, when combined with classic DA, our two-step GAN-based DA can significantly outperform the classic DA alone, in tumor detection (i.e., boosting sensitivity from 93:63% to 97:53%) and also in other medical imaging tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Research on hybrid bounding box interference check algorithm for robot offline simulation system

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Journal of Physics: Conference Series 2021年第1期1948卷

作者： Jing Li Zhen Zhong ChangQi Fan NanYan Shen YiHao Lu ZeYuan Feng ZhenLiang Dai Hong Yu Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai 200444 China Shanghai Mobile Internet Industry Promotion Center Room 614 306 Taopu Road Putuo District Shanghai 200444 China Yanfeng Plastic Omnium Automotive Exterior Systems Company Limited No.540 Moyu Road Anting Shanghai 200444 China

Aiming at the problem that the traditional industrial robot may interfere with the workpiece when running in the offline simulation environment, a hybrid bounding box algorithm based on octree is proposed. First, AABB is used for rough interference checks, which quickly eliminates the pairs of impossible objects to intersect, then OBB hierarchical bounding box is used for precise interference checks. A new OBB construction method is proposed, which recalculates the semi-axis length and centre of the constructed traditional OBB to make it surround the object as much as possible and reduce the space gap. A traversal method based on distance priority is proposed, which traversed the closest node every time by maintaining a priority queue. And in the update strategy, 'update on demand' is adopted, which discards updating the entire tree before each intersection and only updates the current node during the intersection traversal to achieve the purpose of partial update. Experiments show that the proposed hybrid bounding box algorithm can detect interference faster and perform better real-time performance and accuracy.

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Shape-Memory Polymers: Mechanically Robust and UV-Curable Shape-Memory Polymers for Digital Light Processing Based 4D Printing (Adv. Mater. 27/2021)

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Advanced Materials 2021年第27期33卷

作者： Biao Zhang Honggeng Li Jianxiang Cheng Haitao Ye Amir Hosein Sakhaei Chao Yuan Ping Rao Yuan-Fang Zhang Zhe Chen Rong Wang Xiangnan He Ji Liu Rui Xiao Shaoxing Qu Qi Ge Frontiers Science Center for Flexible Electronics (FSCFE) Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials and Engineering (IBME) Northwestern Polytechnical University 127 West Youyi Road Xi'an 710072 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen 518055 China School of Engineering and Digital Arts University of Kent Canterbury Kent CT2 7NT UK State Key Laboratory for Strength and Vibration of Mechanical Structures Department of Engineering Mechanics Xi'an Jiaotong University Xi'an 710049 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 China Digital Manufacturing and Design Centre Singapore University of Technology and Design Singapore 487372 Singapore State Key Laboratory of Fluid Power and Mechatronic System Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Zhejiang University Hangzhou 310027 China

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Author Correction: Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus

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Nature communications 2024年第1期15卷 1510页

作者： Wenli Li Pei He Dangyuan Lei Yulong Fan Yangtao Du Bo Gao Zhiqin Chu Longqiu Li Kaipeng Liu Chengxu An Weizheng Yuan Yiting Yu Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. dangylei@cityu.edu.hk. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. The Institute of AI and Robotics Fudan University Shanghai 200433 China. Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an 710119 China. Department of Electrical and Electronic Engineering Joint Appointment with School of Biomedical Sciences The University of Hong Kong Hong Kong 999077 China. State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China. Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. yyt@***.

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0193 Sleepiness and Slow Rolling Eye Movements are Increased During Autonomous Versus Manual DrivingGet access

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Sleep 2019年第SUPPLEMENT_1期42卷 A79–A79页

作者： Wong, Lily R Kuriyagawa, Yukiyo Gowda, Nikhil Cravalho, Patrick Wilaiprasitporn, Theerawit Garcia, Javier Flynn-Evans, Erin E San Jose State University Research Foundation Moffett Field CA USA NASA Ames Research Center Fatigue Countermeasures Laboratory Human Systems Integration Division Moffett Field CA USA Nihon University College of Industrial Technology Chiba Japan Vidyasirimedhi Institute of Science & Technology School of Information Science and Technology Bio-inspired Robotics and Neural Engineering Lab Rayong Thailand U.S. Army Research Laboratory Aberdeen Proving Ground MD USA

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