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Proceedings of the 2016 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

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Spatio-Temporal Databases 1

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丛书名： Lecture Notes in computer science

1000年

作者： Timos K. Sellis Manolis Koubarakis Andrew Frank Stéphane Grumbach Ralf Hartmut Güting Christian Jensen Nikos A. Lorentzos Yannis Manolopoulos Enrico Nardelli Barbara Pernici Babis Theodoulidis Nectaria Tryfona Hans-Jörg Schek Michel O. Scholl

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Accelerating the Evolution of Personalized Automated Lane Change Through Lesson Learning

SSRN

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SSRN 2024年

作者： Hu, Jia Lei, Mingyue Li, Duo Li, Zhenning So, Jaehyun Wang, Haoran Key Laboratory of Road and Traffic Engineering Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Department of Transportation Systems Engineering Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si16499 Korea Republic of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China

Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived safety using Gaussian discriminant analysis. Real-time corrections to trajectory planning rewards are enacted through apprenticeship learning. Guided by the objective of optimizing rewards within the constraints of the driving zone, this approach employs model predictive control for trajectory planning. This lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulating, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves a successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds. © 2024, The Authors. All rights reserved.

关键词： Discriminant analysis

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Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning

arXiv

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arXiv 2024年

作者： Hu, Jia Lei, Mingyue Li, Duo Li, Zhenning So, Jaehyun Wang, Haoran Key Laboratory of Road and Traffic Engineering The Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering The Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Department of Transportation Systems Engineering Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si16499 Korea Republic of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China

关键词： Discriminant analysis

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles

Motion Planning Integrated with Vehicle-Terrain Interactions...

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International Conference on Intelligent Transportation

作者： Jianhua Yin Xianyuan Zhan Tianying Ji Bingrong Xu Yi He Daqing Zhang Lingxi Li Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for AI Industry Research Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Department of Computer Science and Technology Tsinghua University Beijing China School of Automation Wuhan University of Technology Wuhan China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha China Sunward Intelligent Equipment Co. Ltd. Changsha China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains.

关键词： Dynamic response Geometry Navigation Heuristic algorithms Reinforcement learning Land vehicles Hazards Planning Sensors Vehicle dynamics

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衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

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Engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

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Robust medical instrument segmentation challenge 2019

arXiv

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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Roadmap on Quantum Thermodynamics

arXiv

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arXiv 2025年

作者： Campbell, Steve D’Amico, Irene Ciampini, Mario A. Anders, Janet Ares, Natalia Artini, Simone Auffèves, Alexia Oftelie, Lindsay Bassman Bettmann, Laetitia P. Bonança, Marcus V.S. Busch, Thomas Campisi, Michele Cavalcante, Moallison F. Correa, Luis A. Cuestas, Eloisa Dag, Ceren B. Dago, Salambô Deffner, Sebastian Del Campo, Adolfo Deutschmann-Olek, Andreas Donadi, Sandro Doucet, Emery Elouard, Cyril Ensslin, Klaus Erker, Paul Fabbri, Nicole Fedele, Federico Fiusa, Guilherme Fogarty, Thomás Folk, Joshua Guarnieri, Giacomo Hegde, Abhaya S. Hernández-Gómez, Santiago Hu, Chang-Kang Iemini, Fernando Karimi, Bayan Kiesel, Nikolai Landi, Gabriel T. Lasek, Aleksander Lemziakov, Sergei Monaco, Gabriele Lo Lutz, Eric Lvov, Dmitrii Maillet, Olivier Mehboudi, Mohammad Mendonça, Taysa M. Miller, Harry J.D. Mitchell, Andrew K. Mitchison, Mark T. Mukherjee, Victor Paternostro, Mauro Pekola, Jukka Perarnau-Llobet, Martí Poschinger, Ulrich Rolandi, Alberto Rosa, Dario Sánchez, Rafael Santos, Alan C. Sarthour, Roberto S. Sela, Eran Solfanelli, Andrea Souza, Alexandre M. Splettstoesser, Janine Tan, Dian Tesser, Ludovico Van Vu, Tan Widera, Artur Halpern, Nicole Yunger Zawadzki, Krissia School of Physics University College Dublin Belfield Dublin 4 Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin 4 Ireland School of Physics Engineering and Technology University of York York United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Boltzmanngasse 5 ViennaA-1090 Austria Institute of Physics and Astronomy University of Potsdam Potsdam14476 Germany Department of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom Università degli Studi di Palermo Dipartimento di Fisica e Chimica - Emilio Segrè via Archirafi 36 PalermoI-90123 Italy MajuLab CNRS-UCA-SU-NUS-NTU International Joint Research Laboratory France Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore NEST Istituto Nanoscienze-CNR and Scuola Normale Superiore Pisa56127 Italy School of Physics Trinity College Dublin College Green Dublin 2 Ireland Gleb Wataghin Physics Institute The University of Campinas Campinas São Paulo13083-859 Brazil Quantum Systems Unit OIST Graduate University Okinawa Onna904-0495 Japan Department of Physics University of Maryland Baltimore County BaltimoreMD21250 United States Quantum Science Institute University of Maryland Baltimore County BaltimoreMD21250 United States Universidad de La Laguna La Laguna38203 Spain Departamento de Física Universidad de La Laguna La Laguna38203 Spain Jülich54245 Germany Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics Harvard University 17 Oxford Street CambridgeMA02138 United States National Quantum Laboratory College ParkMD20740 United States Department of Physics and Materials Science University of Luxembourg LuxembourgL-1511 Luxembourg Donostia International Physics Center San SebastianE-20018 Spain Au

The last two decades has seen quantum thermodynamics become a well established field of research in its own right. In that time, it has demonstrated a remarkably broad applicability, ranging from providing foundational advances in the understanding of how thermodynamic principles apply at the nano-scale and in the presence of quantum coherence, to providing a guiding framework for the development of efficient quantum devices. Exquisite levels of control have allowed state-of-the-art experimental platforms to explore energetics and thermodynamics at the smallest scales which has in turn helped to drive theoretical advances. This Roadmap provides an overview of the recent developments across many of the field’s sub-disciplines, assessing the key challenges and future prospects, providing a guide for its near term progress. Copyright © 2025, The Authors. All rights reserved.

关键词： Temperature

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