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arXiv 2021年

作者： Ma, Qian Huang, Jianwei Bąsar, Tamer Liu, Ji Chen, Xudong The School of Intelligent Systems Engineering Sun Yat-sen University China The School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States The Department of Electrical and Computer Engineering Stony Brook University United States The Department of Electrical University of Colorado Boulder Computer and Energy Engineering United States

We study the economic interactions among sellers and buyers in online markets. In such markets, buyers have limited information about the product quality, but can observe the sellers' reputations which depend on their past transaction histories and ratings from past buyers. Sellers compete in the same market through pricing, while considering the impact of their heterogeneous reputations. We consider sellers with limited as well as unlimited capacities, which correspond to different practical market scenarios. In the unlimited seller capacity scenario, buyers prefer the seller with the highest reputation-price ratio. If the gap between the highest and second highest seller reputation levels is large enough, then the highest reputation seller dominates the market as a monopoly. If sellers' reputation levels are relatively close to each other, then those sellers with relatively high reputations will survive at the equilibrium, while the remaining relatively low reputation sellers will get zero market share. In the limited seller capacity scenario, we further consider two different cases. If each seller can only serve one buyer, then it is possible for sellers to set their monopoly prices at the equilibrium while all sellers gain positive market shares;if each seller can serve multiple buyers, then it is possible for sellers to set maximum prices at the equilibrium. Simulation results show that the dynamics of reputations and prices in the longerterm interactions will converge to stable states, and the initial buyer ratings of the sellers play the critical role in determining sellers' reputations and prices at the stable state. Copyright © 2021, The Authors. All rights reserved.

关键词： Competition

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Human-collective visualization transparency

arXiv

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arXiv 2020年

作者： Roundtree, Karina A. Cody, Jason R. Leaf, Jennifer Onan Demirel, H. Adams, Julie A. School of Mechanical Industrial and Manufacturing Engineering Oregon State University Department of Electrical Engineering and Computer Science United States Military Academy Collaborative Robotics and Intelligent Systems Institute Oregon State University

Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective transparency research has focused on how the design of the algorithms, visualizations, and control mechanisms influence human-collective behavior. Traditional collective visualizations have shown all of the individual entities composing a collective, which may become problematic as collectives scale in size and heterogeneity, and tasks become more demanding. Human operators can become overloaded with information, which will negatively affect their understanding of the collectives current state and overall behaviors, which can cause poor teaming performance. An analysis of visualization transparency and the derived visualization design guidance, based on remote supervision of collectives, are the primary contributions of this manuscript. The individual agent and abstract visualizations were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. The abstract visualization provided better transparency by enabling operators with different individual differences and capabilities to perform relatively the same and promoted higher human-collective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Transparency

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Evaluating the robustness of HJB optimal feedback control 52

Evaluating the robustness of HJB optimal feedback control

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52nd International Symposium on robotics, ISR 2020

作者： Lutter, Michael Clever, Debora Belousov, Boris Listmann, Kim Peters, Jan Computer Science Department Technical University of Darmstadt Darmstadt Germany ABB Corporate Research Center Ladenburg Ladenburg Germany ABB Future Labs Switzerland Baden-Dättwil Switzerland Robot Learning Group Max Planck Institute for Intelligent Systems Tübingen Germany

ISBN: (纸本)9783800754298

Developing and tuning a feedback controller including dynamics compensation is a challenging but essential part of many control applications. In contrast, describing a control task using a cost function and learning the corresponding optimal controller can simplify this controller development. However, current popularized deep reinforcement learning methods to obtain these controllers automatically, do not achieve good control policies w.r.t. the required smoothness, generalization and robustness w.r.t. parameter uncertainty of the approximate dynamics model. In this paper we describe HJB optimal control (HJBopt) a different approach to obtain optimal feedback policies by optimization rather than repeated sampling actions. This approach optimizes the residual of the Hamilton-Jacobi-Bellman differential equation on the complete state domain to obtain an optimal value function that directly implies a continuous time optimal policy on the complete state domain. The experiments show that the proposed HJBopt learns a good approximation of the optimal policy and this approximation exhibits much better smoothness and generalization compared to the deep reinforcement learning baselines. In addition, we show empirically that these characteristics enable HJBopt to obtain much more robust policies compared to these baselines. © 2020 VDE Verlag GMBH.

关键词： Cost functions

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DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

DirectShape: Direct Photometric Alignment of Shape Priors fo...

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IeeE International Conference on robotics and Automation (ICRA)

作者： Rui Wang Nan Yang Jörg Stückler Daniel Cremers Department of Computer Science Technical University of Munich Garching bei MUnchen Germany Max Planck Institute for Intelligent Systems Tubingen Tubingen Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Scene understanding from images is a challenging problem encountered in autonomous driving. On the object level, while 2D methods have gradually evolved from computing simple bounding boxes to delivering finer grained results like instance segmentations, the 3D family is still dominated by estimating 3D bounding boxes. In this paper, we propose a novel approach to jointly infer the 3D rigid-body poses and shapes of vehicles from a stereo image pair using shape priors. Unlike previous works that geometrically align shapes to point clouds from dense stereo reconstruction, our approach works directly on images by combining a photometric and a silhouette alignment term in the energy function. An adaptive sparse point selection scheme is proposed to efficiently measure the consistency with both terms. In experiments, we show superior performance of our method on 3D pose and shape estimation over the previous geometric approach and demonstrate that our method can also be applied as a refinement step and significantly boost the performances of several state-of-the-art deep learning based 3D object detectors. All related materials and demonstration videos are available at the project page https://***/research/vslam/direct-shape.

关键词： Shape Three-dimensional displays Two dimensional displays Automobiles Current measurement Solid modeling Image reconstruction

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

引用

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

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InfoMat 2020年第6期2卷 1131-1162页

作者： Qiongfeng Shi Bowei Dong Tianyiyi He Zhongda Sun Jianxiong Zhu Zixuan Zhang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore117576Singapore Center for Intelligent Sensors and MEMS National University of SingaporeSingapore117608Singapore Hybrid-Integrated Flexible(Stretchable)Electronic Systems Program National University of SingaporeSingapore117608Singapore NUS Suzhou Research Institute(NUSRI) Suzhou215123China NUS Graduate School for Integrative Science and Engineering National University of SingaporeSingapore117456Singapore

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so *** to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and ***,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other ***,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so *** self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage ***,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart *** the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

关键词： artificial intelligence energy harvesting human-machine interface internet of things wearable electronics wearable photonics

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Variable Stiffness Control with Strict Frequency Domain Constraints for Physical Human-Robot Interaction

arXiv

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arXiv 2020年

作者： Zou, Wulin Duan, Pu Chen, Yawen Yu, Ningbo Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Xeno Dynamics Co. Ltd Shenzhen China Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Learning counterfactual representations for estimating individual dose-response curves

arXiv

引用

arXiv 2019年

作者： Schwab, Patrick Linhardt, Lorenz Bauer, Stefan Buhmann, Joachim M. Karlen, Walter Institute of Robotics and Intelligent Systems ETH Zurich Switzerland Department of Computer Science ETH Zurich Switzerland MPI for Intelligent Systems Tübingen Germany

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual doseresponse curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Emergency Clinical Procedure Detection With Deep Learning

Emergency Clinical Procedure Detection With Deep Learning

引用

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

作者： Lingfeng Li Richard A. Paris Conner Pinson Yan Wang Joseph Coco Jamison Heard Julie A. Adams Daniel V. Fabbri Bobby Bodenheimer Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee Vanderbilt University Medical Center Nashville Tennessee Department of Electrical and Microelectronic Engineering Rochester Institute of Technology Rochester NY Collaborative Robotics and Intelligent Systems Institute Oregon State University Oregon

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care. In this paper we explore the use convolutional neural networks (CNNs) on raw video data to determine how well video data alone can automatically identify clinical procedures. We apply multiple deep learning models to this problem, with significant variation in results. Our findings indicate performance improvements compared to prior work, but also indicate a need for more training data to reach clinically deployable levels of success.

关键词： Adaptation models Training Testing Machine learning Hospitals Cameras

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

arXiv

引用

arXiv 2021年

作者： Mitchell, Daniel Blanche, Jamie Harper, Sam Lim, Theodore Gupta, Ranjeetkumar Zaki, Osama Tang, Wenshuo Robu, Valentin Watson, Simon Flynn, David Smart Systems Group Institute of Sensors Signals and Systems School of Engineering and Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems Group CWI Amsterdam1098 XG Netherlands Delft2628 XE Netherlands University of Manchester Department of Electrical and Electronic Engineering Oxford Road ManchesterM13 9PL United Kingdom

A global trend in increasing wind turbine size and distances from shore is emerging within the rapidly growing offshore wind farm market. In the UK, the offshore wind sector produced its highest amount of electricity in the UK in 2019, a 19.6% increase on the year before. Currently, the UK is set to increase production further, targeting a 74.7% increase of installed turbine capacity as reflected in recent Crown Estate leasing rounds. With such tremendous growth, the sector is now looking to robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital

关键词： Offshore wind farms

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