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arXiv 2020年

作者： Zaytsev, Mikhail E. Wang, Yuliang Zhang, Yuhang Lajoinie, Guillaume Zhang, Xuehua Prosperetti, Andrea Zandvliet, Harold J.W. Lohse, Detlef Physics of Fluids Max Planck Center Twente for Complex Fluid Dynamics J.M. Burgers Centre for Fluid Mechanics MESA+ Institute for Nanotechnology University of Twente P.O. Box 217 Enschede7500AE Netherlands Physics of Interfaces and Nanomaterials MESA+ Institute for Nanotechnology University of Twente Enschede7500AE Netherlands Robotics Institute School of Mechanical Engineering and Automation Beihang University Beijing100191 China Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States TechMed Centre University of Twente Enschede Netherlands Department of Chemical and Materials Engineering University of Alberta 12-211 Donadeo Innovation Centre for Engineering EdmontonAB Canada Max Planck Institute for Dynamics and Self-Organization Am Fassberg 17 Göttingen37077 Germany

The understanding of the shrinkage dynamics of plasmonic bubbles formed around metallic nanoparticles immersed in liquid and irradiated by a resonant light source is crucial for the usage of these bubbles in numerous applications. In this paper we experimentally show and theoretically explain that a plasmonic bubble during its shrinkage undergoes two different phases: first, a rapid partial bubble shrinkage governed by vapor condensation and, second, a slow diffusion-controlled bubble dissolution. The history of the bubble formation plays an important role in the shrinkage dynamics during the first phase, as it determines the gas-vapor ratio in the bubble composition. Higher laser powers lead to more vaporous bubbles, while longer pulses and higher dissolved air concentrations lead to more gaseous bubbles. The dynamics of the second phase barely depends on the history of bubble formation, i.e. laser power and pulse duration, but strongly on the dissolved air concentration, which defines the concentration gradient at the bubble interface. Finally, for the bubble dissolution in the second phase, with decreasing dissolved air concentration, we observe a gradual transition from a R(t) ∝ (t0 − t)1/3 scaling law to a R(t) ∝ (t0 − t)1/2 scaling law, where t0 is the lifetime of the bubble and theoretically explain this transition. Copyright © 2020, The Authors. All rights reserved.

关键词： Shrinkage

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Tissue P systems with rule production/removal 18th

Tissue P systems with rule production/removal

引用

18th International Conference on Membrane Computing, CMC 1018

作者： Pan, Linqiang Song, Bosheng Zhang, Gexiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China School of Electric and Information Engineering Zhengzhou University of Light Industry ZhengzhouHenan450002 China Robotics Research Center and Key Laboratory of Fluid and Power Machinery Xihua University Chengdu610039 China

ISBN: (纸本)9783319733586

Tissue P systems are computational models inspired by the way of biochemical substance movement/exchange between two cells or between a cell and the environment, where all communication (symport/antiport) rules used in a system are initially set up and keep unchanged during any computation. In this work, a variant of tissue P systems, called tissue P systems with rule production/removal (abbreviated as TRPR P systems) is considered, where rules in a system are dynamically changed during a computation, that is, at any computation step new rules can be produced and some existing rules can be removed. The computation power of TRPR P systems is investigated. It is proved that Turing universality is achieved for TRPR P systems with one cell, and using symport rules of length at most 1, antiport rules of length at most 2 or symport rules of length at most 2 and working in a maximally parallel manner. We further show that TRPR P systems with two cells, using symport rules of length at most 1, and working in a flat maximally parallel manner, are Turing universal. © 2018, Springer International Publishing AG.

关键词： Cells

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Research on Variable Stiffness Actuator of Compliance Robot Joint

Research on Variable Stiffness Actuator of Compliance Robot ...

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2018 2nd International Conference on Modeling, Simulation and Optimization Technologies and Applications(MSOTA 2018)

作者： Xia-gang LIU Yu-wang LIU Lu ZHANG Qiang CHENG School of Mechanical Engineering and Automation Northeastern University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Space Utilization Technology and Engineering Center for Space UtilizationChinese Academy of Sciences

In the complex human-machine collaboration environment, it is particularly important that the robot joint has a certain variable compliance performance. The variable stiffness joint based on the antagonistic principle is a research hotspot of the compliant robot joint. In this paper, a variable stiffness actuator is proposed. This design eliminates the antagonistic effect of the elastic component in the antagonistic joint and improves the energy storage density. Theoretical analysis of nonlinear characteristics is carried out. In addition, it was studied by simulation and experiment.

关键词： Antagonistic principle Spring Variable stiffness Simulation

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Bounded Control of Robotic Manipulators via Fuzzy Logic Systems 36

Bounded Control of Robotic Manipulators via Fuzzy Logic Syst...

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第36届中国控制会议

作者： Linghuan Kong Wei He Yiting Dong Chenguang Yang Guang Li School of Automation Engineering and Center for Robotics University of Electronic Science and Technology of China School of Automation and Electrical Engineering University of Science and Technology Beijing Department of Mechanical Engineering Texas Tech University Zienkiewicz Centre for Computational Engineering Swansea University School of Engineering and Materials Science Queen Mary University of London

ISBN: (纸本)9781538629185

In this paper, a fuzzy neural network controller is proposed for a class of robotic manipulators with unknown dynamics and input constraints. First, to cope with the unknown dynamics, fuzzy logic systems are designed to approximate it. Second,considering the case where an aggressive control input may lead to the poor performance, even resulting in instability in practice,the controller is designed with the function tanh（·）, and the function has the ability to make the controller keep in the predefined range. At last, evidence based on the Lyapunov theory proves the system errors converge to a small range near zero. Simulations are carried out to show the effectiveness of the designed control.

关键词： Fuzzy Logic System Neural Networks Input Constraints Robotic Manipulators

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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A Tunable Helmholtz Resonator for Electromagnetic Energy Harvesting

A Tunable Helmholtz Resonator for Electromagnetic Energy Har...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mohammad Amin Askari Farsangi Hassan Zohoor School of Mechanical Engineering Sharif University of Technology Tehran Iran Center of Excellence in Design Robotics and Automation Sharif University of Technology Academician Academy of Sciences of IR Iran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

In this paper, acoustic energy harvesting using a tunable Helmholtz Resonator (HR) is investigated. The frequency of resonator can be changed by varying the radial force in a membrane which is used in one of its face. Applying acoustic pressure, vibrates the air inside HR neck and therefore changes pressure in HR chamber which results in membrane vibration. Attached magnet to the membrane will vibrate and its relative motion to a fixed coil generates voltage on the basis of Faraday's law of induction. Modeling approach is developed considering equivalent nonlinear membrane stiffness and coupling of electrical circuit and mechanical device. At the end, system behavior is studied numerically and effects of several factors such as radial force, electrical resistance and geometric parameters has been taken into account.

关键词： Acoustics Force Energy harvesting Magnetoacoustic effects Resonant frequency Immune system Vibrations

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3D Pose Estimation for Robotic Grasping Using Deep Convolution Neural Network

3D Pose Estimation for Robotic Grasping Using Deep Convoluti...

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IEEE International Conference on robotics and Biomimetics

作者： Yao Wang Ying Xu Xiaohui Zhang Zhen Sun Yafang Zhang Guoli Song Junchen Wang School of Mechanical Engineering and Automation Beihang University Beijing China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China

ISBN: (纸本)9781728103785;9781728103778

With the progress of artificial intelligence, robots begin to enter family service. Autonomous object grasping in a cluttered scene is the most frequent operation of a service robot in daily life while it is still a challenging problem in the field of robotics. In this paper, we develop a robot system using a deep convolution neural network for 3D object grasping. The system is composed by a color camera, and a robot arm with a gripper. The color camera provides robotic vision about surrounding environments; the deep neural network performs an end-to-end mapping from vision images to the 3D poses of the object of interest; the robot arm with the gripper is then driven to grasp the object. In addition, we also present an automatic data labeling method for the training of the convolution neural network. Preliminary experiments were performed to evaluate our robot system and the results have confirmed its effectiveness.

关键词： Conferences Robots Biomimetics

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PID2018 benchmark challenge: Learning feedforward control

arXiv

引用

arXiv 2018年

作者： Zhao, Yang Dehghan, Sina Ates, Abdullah School of Control Science and Engineering Center for Robotics Shandong University Jinan Shandong250061 China University of California Merced MercedCA95340 United States School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedforward control

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A method for fingertips detection using RGB-D image and convolution neural network 13

A method for fingertips detection using RGB-D image and conv...

引用

13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017

作者： Nguyen, Hai Duong Kim, Young Chul Kim, Soo Hyung Na, In Seop School of Electronic and Computer Engineering Chonnam National University Gwangju Korea Republic of Agriculture Robotics and Automation Research Center Chonnam National University Gwangju Korea Republic of

ISBN: (纸本)9781538621653

Fingertips detection is one of interesting problems in computer vision whose the aim is to locate fingertips in an input image. In this paper, we propose a method for fingertips detection using RGB-D image and convolution neural network. A deep convolutional network based model is used detect hands in an input image. Then we locate the fingertips based on the texture of the detected regions with some rules. The experimental results show that our method is a promising technique for fingertips detection in real time. © 2017 IEEE.

关键词： Textures

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