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arXiv 2019年

作者： Kvalheim, Matthew D. Revzen, Shai Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Ecology and Evolutionary Biology Department Robotics Institute University of Michigan Ann ArborMI48109 United States

We consider C1 dynamical systems having an attracting hyperbolic fixed point or periodic orbit and prove existence and uniqueness results for Ck (actually Clock,α) linearizing semiconjugacies-of which Koopman eigenfunctions are a special case-defined on the entire basin of attraction. Our main results both generalize and sharpen Sternberg's Ck linearization theorem for hyperbolic sinks, and in particular our corollaries include uniqueness statements for Sternberg linearizations and Floquet normal forms. Using our main results we also prove new existence and uniqueness statements for Ck Koopman eigenfunctions, including a complete classification of C∞ eigenfunctions assuming a C∞ dynamical system with semisimple and nonresonant linearization. We give an intrinsic definition of "principal Koopman eigenfunctions" which generalizes the definition of Mohr and Mezić for linear systems, and which includes the notions of "isostables" and "isostable coordinates" appearing in work by Ermentrout, Mauroy, Mezić, Moehlis, Wilson, and others. Our main results yield existence and uniqueness theorems for the principal eigenfunctions and isostable coordinates and also show, e.g., that the (a priori non-unique) "pullback algebra" defined in [MM16b] is unique under certain conditions. We also discuss the limit used to define the "faster" isostable coordinates in [WE18, MWMM19] in light of our main *** Codes 37C05, 37C10, 37C15, 37C27, 34C15, 34C20, 37D99 Copyright © 2019, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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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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Smart decentralised energy management

Smart decentralised energy management

引用

CIRED 2020 Berlin Workshop (CIRED 2020)

作者： Stefan Werner Thomas Walter Christian Wiezorek Christian Backe Miguel Bande Firvida Thomas Vögele Peter Conradi Samrat Bose Enrique Kremers Easy Smart Grid GmbH (ESG) Karlsruhe Germany Technical University of Berlin (TUB) Department of Electrical Engineering and Computer Sciences Berlin Germany German Research Center for Artificial Intelligence (DFKI) Robotics Innovation Center Bremen Germany ALL4IP Technologies Darmstadt Germany European Institute for Energy Research (EIFER) Department of Complex Systems Research Karlsruhe Germany

The German–Finnish research project FUture Smart Energy shows, how flexible devices, consuming or producing electricity in electric grids, can be self-organised in a fully decentralised way, using autonomous algorithms integrated with the devices' controllers. By shifting operation time, existing flexible devices are hereby utilised as ‘virtual batteries’, providing high storage capacity and power. To gain sufficient flexibility, a large number of devices like combined heat and power generators, heat pumps (HP), heaters, coolers, charging stations, pumps, household appliances and industrial plants, has to be coordinated. This results in a high system complexity for which the evaluated method provides an easy, resilient, cyber-secure and cost-effective solution. This novel technology uses a new market approach for electric energy systems. A real-time price signal is generated directly out of grid state variables, like frequency, voltage, power or current, and broadcast to the flexible devices. Without a need for central control, the flexible devices react like a natural swarm to the price signal. The system is easily and highly scalable, as adding and removing flexibilities does not imply adapting a central control system. The system can be operated parallel or in addition to existing energy markets.

关键词： Internet of Things smart power grids energy management systems decentralised control power distribution control decision making pricing

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Precision of sung notes in carnatic music 19

Precision of sung notes in carnatic music

引用

19th International Society for Music Information Retrieval Conference, ISMIR 2018

作者： Viraraghavan, Venkata Subramanian Murthy, Hema A. Aravind, R. TCS Research and Innovation Embedded Systems and Robotics Bangalore India Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)9782954035123

Carnatic music is replete with continuous pitch movement called gamakas and can be viewed as consisting of constant-pitch notes (CPNs) and transients. The stationary points (STAs) of transients – points where the pitch curve changes direction – also carry melody information. In this paper, the precision of sung notes in Carnatic music is studied in detail by treating CPNs and STAs separately. There is variation among the nineteen musicians considered, but on average, the precision of CPNs increases exponentially with duration and settles at about 10 cents for CPNs longer than 0.5 seconds. For analyzing STAs, in contrast to Western music, rāga (melody) information is found to be necessary, and errors in STAs show a significantly larger standard deviation of about 60 cents. To corroborate these observations, the music was automatically transcribed and re-synthesized using CPN and STA information using two interpolation techniques. The results of perceptual tests clearly indicate that the grammar is highly flexible. We also show that the precision errors are not due to poor pitch tracking, singer deficiencies or delay in auditory feedback. © V S Viraraghavan, H A Murthy, R Aravind.

关键词： Music

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

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Assis - cicerone robot with visual obstacle avoidance using a stack of odometric data

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2018年第1期45卷 219-227页

作者： Mendesy, Mateus Coimbray, A. Paulo Crisóstomo, Manuel M. ESTGOH Polytechnic Institute of Coimbra Portugal ISR - Institute of Systems and Robotics Dept. of Electrical and Computer Engineering University of Coimbra Portugal

Modern research has shown that intelligent behaviour is, to a great extent, strongly supported on the use of a sophisticated memory. Additionally, vision is the main source of information for the average human. Constant increase of sensing and processing power and constant decrease of the cost of memory have made vision-based approaches in robotics more attractive. ASSIS is a prototype of an assistant robot which uses vision and memory-based navigation, including intelligent obstacle avoidance. ASSIS uses a parallel implementation of a Sparse Distributed Memory to store images and use them later for localisation and navigation. Obstacle avoidance is achieved using a stack-based method and odometric data. Users can command the robot through a web-based interface. ASSIS implements behaviours such as courier or cicerone.

关键词： Navigation

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Bearing-only formation control under persistence of excitation

arXiv

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arXiv 2020年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal I3S-CNRS Université Côte d'Azur Nice-Sophia Antipolis France I3S-CNRS Institut Universitaire de France Nice-Sophia Antipolis France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper addresses the problem of bearing-only formation control in d (d ≥ 2)-dimensional space by exploring persistence of excitation (PE) of the desired bearing reference. By defining a desired formation that is bearing PE, distributed bearing-only control laws are proposed, which guarantee exponential stabilization of the desired formation only up to a translation vector. The key outcome of this approach relies in exploiting the bearing PE to significantly relax the conditions imposed on the graph topology to ensure exponential stabilization, when compared to the bearing rigidity conditions, and to remove the scale ambiguity introduced by bearing vectors. Simulation results are provided to illustrate the performance of the proposed control method. Copyright © 2020, The Authors. All rights reserved.

关键词： Stabilization

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Predictive Inverse Kinematics: optimizing Future Trajectory through Implicit Time Integration and Future Jacobian Estimation

Predictive Inverse Kinematics: optimizing Future Trajectory ...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ko Ayusawa Wael Suleiman Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI13218/RL Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (IS-AIST) Japan Electrical and Computer Engineering Department Faculty of Engineering University of Sherbrooke Canada

This paper presents an inverse kinematics (IK) method which can control future velocities and accelerations for multi-body systems. The proposed IK method is formulated as a quadratic programing (QP) that optimizes future joint trajectories. The features of the proposed IK are: (1) the evaluation of accelerations at future time instances, (2) the trajectory representation that can implicitly integrate the time integral formula into QP, (3) the computation of future Jacobian matrices based on the comprehensive theory of differential kinematics proposed in our previous work. Those features enable a stable and fast IK computation while evaluating the future accelerations. We also conducted thorough numerical studies to show the efficiency of the proposed method.

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Editor-in-Chief

引用

IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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