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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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PROGRAMS project approach to maintenance management

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IFAC-PapersOnLine 2020年第3期53卷 313-318页

作者： M. Surico R. Ricatto A. Merlo I. Németh A. Sardelis M. Villoslada E. Montejo N. Frenkel P. Aivaliotis I.M. de la Pera Celada J. Sidiropoulos A. Eytan A. Papavasileiou F. Aggogeri FIDIA S.p.A. Corso Lombardia 11 San Mauro Torinese Italy CeSI Centro Studi Industriali via Tintoretto 10 Cologno Monzese (MI) 20093 Italy Department of Manufacturing Science and Engineering Budapest University of Technology and Economics 1111 Budapest Műegyetem rkp. 3 Hungary CASP S.A. 64 Michalakopoulou str Athens 11528 Greece Savvy Data Systems S.L. San Sebastian Gipuzkoa 20018 Spain Ideko S.Coop Arriaga Kalea 2 20870 Elgoibar Gipuzkoa Spain Laboratory for Machine Tools and Production Engineering (WZL) Campus-Boulevard 30 52074 Aachen Germany Laboratory for Manufacturing Systems & Automation University of Patras Rion Patras 26504 Greece Aurrenak S. Coop Bitorialanda Kalea 15 01010 Vitoria-Gasteiz Araba Spagna Grobotics Gizelis Robotics Kormatzini Area Schimatari Viotias 32009 Greece We Plus S.p.A Strada Torino 43 10043 Orbassano (TO) Italy Calpak-Cicero Hellas S.A. 9 Sygrou Ave. 11743 Athens Greece Department of Mechanical and Industrial Engineering University of Brescia via Branze 38 25123 Brescia Italy

Maintenance management is a vital part of the business of a production company. It contributes to determining the long-term success of the company because poorly maintained resources can stop production activities, causing delays, loss of profit and even personal injuries. While the predictive maintenance approach is nowadays a mainstay of modern factory management, usually only big companies with dedicated research department can deploy it, since its application involves scientific knowledge that is not available in smaller production environments. This paper describes how the EU project PROGRAMS answers the needs of small and medium companies that wish to apply an industrial Internet of Things (IIoT) approach to maintenance management.

关键词： Maintenance engineering Preventive maintenance Decision support systems Optimization problems Sensors systems

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Saturated Continuous Twisting Algorithm 15

Saturated Continuous Twisting Algorithm

引用

15th International Workshop on Variable Structure systems, VSS 2018

作者： Golkani, M.A. Fridman, L.M. Koch, S. Reichhartinger, M. Horn, M. Faculty of Electrical and Information Engineering Graz University of Technology Institute of Automation and Control Graz Austria Department of Control Engineering and Robotics Division of Electrical Engineering Engineering Faculty National Autonomous University of Mexico Mexico D.F. Mexico Institute of Automation and Control Graz University of Technology Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz Austria

ISBN: (纸本)9781538664391

In this paper, a second-order system affected by perturbations is considered. A feedback control law adopting the continuous twisting algorithm is designed such that a saturated and continuous control signal is introduced to the system. In the case that a state variable is not measurable, estimate information obtained through a super-twisting observer is incorporated into the design. For the closed-loops (with and without observer), global finite-time stability properties are established. The feasibility and effectiveness of the strategy is shown employing numerical simulations. © 2018 IEEE.

关键词： Stability

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Energetic electron precipitation driven by electromagnetic ion cyclotron waves from ELFIN’s low altitude perspective

arXiv

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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

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Visibility augmentation of the proportional navigation guidance

Visibility augmentation of the proportional navigation guida...

引用

AIAA Guidance, Navigation, and Control Conference, 2017

作者： Tardioli, Luca Franzini, Giovanni Pollini, Lorenzo Innocenti, Mario Automation Robotics and Autonomous Systems Laboratory Department of Information Engineering University of Pisa Pisa56122 Italy

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

The paper presents a visibility augmentation method for a classical proportional navigation guidance law that helps the pursuer maintaining visual contact with the target, and also allows to recover loss of line-of-sight. The concept of visibility is formally introduced in a modified pursuit-evasion differential game, and it appears in the Lagrangian term of the players’ cost functions. Target visibility is then quantified by means of a visibility function, which adds an augmentation term to the proportional navigation structure. Numerical simulations are used for validation. A further simplification of the guidance law is also presented, using the same arguments that lead to the proportional navigation guidance simplest expression, i.e. considering the players on a nominal collision course. The new simplified law is validated in simulation, and compared against its original expression. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Visibility

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Distributed Leader-follower Formation Control of Nonholonomic Mobile Robots

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IFAC-PapersOnLine 2019年第15期52卷 67-72页

作者： Qun Lu Zhiqiang Miao Dan Zhang Li Yu Wenjun Ye Simon X. Yang Chun-Yi Su College of Electrical Engineering Yancheng Institute of Technology Yancheng 224003 China College of Electrical and Information Engineering Hunan University Changsha 410082 China College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal QC H3G 1M8 Canada Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph Guelph ON N1G 2W1 Canada School of Automation Guangdong University of Technology Guangzhou 510006 China on leave from Concordia University Montreal QC H3G 1M8 Canada

This paper considers the problem of distributed leader-follower formation control for nonholonomic mobile robots only using local interactions among the robots. First, a distributed estimation strategy is presented for each follower robot to estimate the leader’s state. Then the dynamic equations of the formation tracking errors are formulated and a distributed formation control scheme is designed, which yields globally uniformly ultimately bounded stability for the closed-loop systems without requiring persistent excitation conditions. Finally, a simulation example demonstrates the effectiveness of the proposed approach.

关键词： Distributed formation control nonholonomic mobile robots distributed formation control persistent exciting conditions

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Augmented Reality based Visualization of CAM Instructions towards Industry 4.0 paradigm: a CNC Bending Machine case study

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Procedia CIRP 2018年 70卷 368-373页

作者： Dimitris Mourtzis Vasilios Zogopoulos Ioannis Katagis Panagiotis Lagios Laboratory for Manufacturing Systems and Automation (LMS) Department of Mechanical Engineering and Aeronautics University of Patras Rio Patras Greece Gizelis Robotics Robot system integration Schimatari Viotias 32009 Kormatzini Area Greece

Skilled shop-floor technicians are playing an important role in modern Industry 4.0 manufacturing environments, especially in small and customized manufacturing systems. Moving towards Industry 4.0 business models, it is essential to develop advanced solutions that will reinforce the position of technicians in manufacturing, by implementing key enabling technologies. Augmented Reality (AR) has arisen as a key enabling technology, which will enable the transfer of information from digitalized design systems and databases towards human operators, especially including production and maintenance information. Reinforced by the development of advanced communication technologies that support high speed data transfer, such as 5G, and networks that combine low latency with increased availability, such as tactile internet, AR proposes a way for agile information distribution and increased shop-floor status awareness. This study presents a mobile application for visualizing Computer Aided Manufacturing (CAM) instructions for bending processes using Augmented Reality. The application integrates step-by-step process instructions, highly usable menus and advanced visualization that includes safety zones, indications, and text instructions for the operators. The developed framework is applied and validated in a CNC bending machine in a real world scenario provided by a CNC machines manufacturer.

关键词： Augmented Reality Computer-Aided Manufacturing Production Monitoring

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3D Finite Element Modeling Based Investigations of Micro-textured Tool Designs in Machining Titanium Alloy Ti-6Al-4V

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Procedia Manufacturing 2017年 10卷 536-545页

作者： Olleak A. Özel T. Manufacturing and Automation Research Laboratory Department of Industrial and Systems Engineering Rutgers University Piscataway NJ 08854 United States

Surface texturing on cutting tools is considered a potential improvement to the tool performance both in dry machining with use of solid lubricants and in wet/cryogenic machining. This paper presents investigations on the effects of micro-textured tool designs that include grooves that are parallel, perpendicular, and diagonal to the main cutting edge as well as with pitted and diagonal pitted forms in the case of dry cutting titanium alloy Ti-6Al-4 V by using 3D Finite Element modelling based simulations. The results include predicted forces, stress, temperature, and wear rate distributions which possess certain advantages of micro-texture tool designs. © 2017

关键词： Cutting Tool Finite Element Method Surface Texturing Titanium Alloy

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Parallel Driving in CPSS:A Unified Approach for Transport automation and Vehicle Intelligence

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 577-587页

作者： Fei-Yue Wang Nan-Ning Zheng Dongpu Cao Clara Marina Martinez Li Li Teng Liu IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Advanced Vehicle Engineering Center Cranfield University Department of Automation Tsinghua University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Qingdao Huituo Intelligent Machine Company

The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel *** proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.

关键词： ACP theory connected automated driving cyber-physical-social systems(CPSS) iHorizon parallel driving parallel horizon parallel learning parallel reinforcement learning parallel testing

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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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