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Set-up of active magnetic bearings for control of flexible shaft

AIP Conference Proceedings 2018年第1期2029卷

作者： Adam Piłat Bartosz Sikora Jan Klocek Jan Cieślik 1AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics Mickiewicza 30 30-059 Krakow Poland 2Optister Jalowcowa 50 31-329 Krakow Poland 3AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Manufacturing Systems Mickiewicza 30 30-059 Krakow Poland

This paper presents the new laboratory test rig of the vertical axis flexible shaft supported by two active magnetic bearings. Behind the motivation of establishing this setup is the control of vibration modes of the flexible rotor - shaft assembly, while the rotational speed varies and the unbalanced mass on the whirling disc is introduced to the system. The proposed model is aimed to simulate real operation conditions of the rotating machinery with an applied external load, with a variable structure caused by a change of material properties of the rotor - shaft assembly, or due to a fault of some elements coupled with the rotor.

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Reliability of statistic evaluation of microscopic pictures taken from knitted fabrics

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Journal of Physics: Conference Series 2015年第1期633卷

作者： A Ehrmann T Blachowicz H Zghidi M O Weber Faculty of Engineering Sciences and Mathematics Bielefeld University of Applied Sciences 33609 Bielefeld Germany Institute of Physics - Center for Science and Education Silesian University of Technology 44-100 Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology 44-100 Gliwice Poland Faculty of Textile and Clothing Technology Niederrhein University of Applied Sciences 41065 Mönchengladbach Germany

One of the techniques which can be used to quantitatively evaluate images statistically is the so-called random-walk approach. The resulting Hurst exponent is a measure of the complexity of the picture. Especially long, fine elements in the image, such as fibres, influence the Hurst exponent significantly. Thus, determination of the Hurst exponent has been suggested as new method to measure the hairiness of yarns or knitted fabrics, since existing hairiness measurement instruments are based on different measurement principles which are not comparable. While the principal usability of this method for hairiness detection has been shown in former projects, the absolute value of the calculated Hurst exponents depends on the technique to take the photographic image of a sample, to transfer it into a monochrome picture, and on possible image processing steps. This article gives an overview of edge detection filters, possible definitions of the threshold value between black and white for the transformation into a monochrome image, etc. It shows how these parameters should be chosen in case of typical textile samples and correlates the challenges of this novel method with well-known problems of common techniques to measure yarn and fabric hairiness.

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USMED and Surgery Assist: IMAGO MOL Cluster Biomedical Solutions to Improve Patient Imaging Availability and Noninvasive Diagnosis

USMED and Surgery Assist: IMAGO MOL Cluster Biomedical Solut...

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E-Health and Bioengineering Conference (EHB)

作者： Vlad Ghizdovat Lucian Nita Paul Herghelegiu Simona Caraiman Alina Capitanu Carmeni Mihai Cati Stolniceanu Jalloul Wael Delia Ciobanu Lidia Ionescu Irena-Cristina Grierosu Cipriana Stefanescu Biophysics Dept. “Grigore T. Popa” University of Medicine and Pharmacy Iaşi Romania SC ROMSOFT SRL Iasi Romania Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania North East RDA Piatra Neamt Romania Imago-Mol Cluster Iaşi Romania “Sf. Spiridon” Emergency County Hospital Nuclear Medicine Laboratory Iasi Romania Department of Pathology “Grigore T. Popa” University of Medicine and Pharmacy Iaşi Romania 3rd Surgery Department “Sf. Spiridon” Emergency County Hospital Iasi Romania Imago Mol Cluster “Grigore T. Popa” University of Medicine and Pharmacy Iaşi Romania

For patient well-being and also for cost efficiency, constantly improving the medical act is essential. All patients undergo a set of medical tests before going into surgery. In some cases, these tests have to be repeated in different hospitals due to the lack of a system that facilitates presenting and sharing patient's data. We present two e-Health solutions for hospitals and clinics in the medical data field. Our aim was to design and implement a unique system for medical data management. This system allows easy data transfer between medical facilities. It also improves the medical diagnostic and treatment process and it increases the quality of medical care. Also, it improves the communication between patients and physicians, decreases the duration and costs of hospitalization and improves the patient's quality of life. The second medical solution presented in this paper present the results of the medical tests directly to a screen in the operating room, further reducing the time a patient has to wait before going into surgery and also improving the medical act. Both medical solutions were tested in real-world scenarios with promising results.

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Heat Transfer Optimization of end Winding Rotor Generator by the Use of Modified Axial Spacer: A Case Study

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Journal of Physics: Conference Series 2021年第1期1845卷

作者： Y Baskoro I Jaya A Glowacz M Sulowicz W Caesarendra PT Indonesia Power Indonesia Department of Automatic Control and Robotics AGH University of Science and Technology 30-059 Kraków Poland Institute of Electromechanical Energy Conversion Faculty of Electrical and Computer Engineering Cracow University of Technology Warszawska 24 31-155 Cracow Poland Faculty of Integrated Technologies Universiti Brunei Darussalam Jalan Tungku Link Gadong BE1410 Brunei Darussalam

This paper presents a case study of a rotor fault occurring in the rotor generator of a steam power plant located in Lontar, Banten Province, Indonesia. The steam power plant has 3 x 315 MV capacity and uses hydrogen in the cooling system. The issue arising during the operation was caused by heat transfer exceeding the normal level. The grounding of the rotor occurred through rising vibration and temperature, tripping the generator unit. During repairs, the axial spacer was replaced with a new model, where it is longer than the original, adding a bar spacer at a previously unavailable position and creating a cooling track profile. This is a case study on the replacement of the axial spacer model aimed at preventing the expansion of the winding, so as not to touch with each other, through the installation of a longer axial spacer and the addition of the spacer bar, and lastly adding the track profile to the cooling hydrogen gas flow thereby maximizing the cooling process from the end winding rotor generator. Heat transfer is an analysed parameter related to the model change of the axial spacer. The addition of a track cooling gas flow has been able to maximize the heat transfer process, which is simulated in this paper. The performance of the rotor generator increased through several parameters after the replacement of the axial spacer model.

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Properties of carbide electro-spark coatings after laser processing

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AIP Conference Proceedings 2018年第1期2017卷

作者： Norbert Radek Izabela Pliszka Aneta Gądek-Moszczak Jarosław Rolek Jana Petru 1Kielce University of Technology Faculty of Mechatronics and Mechanical Engineering Al. 1000-lecia Państwa Polskiego 7 25-314 Kielce Poland 2Cracow University of Technology Institute of Applied Informatics Faculty of Mechanical Engineering Al. Jana Pawła II 37 31-864 Cracow Poland 3Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Al. 1000-lecia Państwa Polskiego 7 25-314 Kielce Poland 4VSB-TU Ostrava Faculty of Mechanical Engineering Department of Machining Assembly and Engineering Metrology 17. listopadu 15/2172 70833 Ostrava-Poruba Czech Republic

The aim of the study was to determine the influence of the laser processing on the properties of electro-spark coatings. The properties were assessed by analyzing the coating microstructure, X-ray radiation, microhardness, corrosion resistance, porosity and tribological tests. In addition, the paper presents the results of measurements and analysis of electrical pulse during ESD. The tests were carried out on WC-Cu coating (the anode) obtained by electro spark deposition over carbon steel C45 (the cathode) and molten with a laser beam. The coatings were deposited by means of the EIL-8A and they were laser treated with the Nd:YAG. The tests show that the laser-treated electro-spark deposited WC-Cu coatings are characterized by lower hardness and friction force, higher seizure resistance, roughness. The laser treatment process causes the homogenization of the chemical composition, the structure refinement and the healing of microcracks and pores of the electro-spark deposited coatings. Laser treated electro-spark deposited coatings are likely to be applied in sliding friction pairs and as protective coatings.

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Adaptive observer for nonlinear fractional-order systems

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International Journal of Adaptive control and Signal Processing 2016年第3期31卷

作者： Ibrahima N'doye Taous-Meriem Laleg-Kirati Mohamed Darouach Holger Voos Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Research Center for Automatic Control of Nancy (CRAN UMR 7039 CNRS) University of Lorraine IUT de Longwy 186 rue de Lorraine 54400 Cosnes et Romain France Faculty of Science Technology and Communication (FSTC) University of Luxembourg 6 rue Richard Coudenhove-Kalergi L-1359 Luxembourg

In this paper, an adaptive observer is proposed for the joint estimation of states and parameters of a fractional nonlinear system with external perturbations. The convergence of the proposed observer is derived in terms of linear matrix inequalities (LMIs) by using an indirect Lyapunov *** proposed adaptive observer is also robust against Lipschitz additive nonlinear uncertainty. The performance of the observer is illustrated through some examples including the chaotic Lorenz and Lü's systems. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： adaptive observer design fractional-order nonlinear system indirect Lyapunov approach linear matrix inequality (LMI) chaotic Lorenz system chaotic Lü's system

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MARKOV DECISION PROCESSES WITH UNCERTAIN TRANSITION RATES: SENSITIVITY AND MAX HYPHEN MIN control

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Asian Journal of control 2008年第2期6卷

作者： Suresh Kalyanasundaram Edwin K. P. Chong Ness B. Shroff Motorola India Electronics Limited No. 66/1 Plot 5 Bagmane Techpark C. V. Raman Nagar Post Bangalore 560 093 India. Department of Electrical and Computer Engineering Colorado State University Fort Collins CO 80523-1373 USA. Professor Edwin K. P. Chong received the B.E.(Hons.) degree with First Class Honors from the University of Adelaide South Australia in 1987 graduating top of his class and the M.A. and Ph.D. degrees in 1989 and 1991 respectively both from Princeton University where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991 where he was named a University Faculty Scholar in 1999 and promoted to Full Professor in 2001. Since August 2001 he has been a Professor of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. His current interests are in communication networks and optimization methods. He coauthored the best-selling book An Introduction to Optimization 2nd Edition Wiley-Interscience 2001. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He coauthored a paper that was awarded Best Paper in the journal Computer Networks 2003. Professor Chong is a Fellow of the IEEE. He was founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and until recently served as an IEEE Control Systems Society Distinguished Lecturer. He has been on the editorial board of the IEEE Transactions on Automatic Control. He is currently on the editorial board of the journal Computer Networks. He has also served on the organizing committees of several international conferences. He has been on the program committees for the IEEE Conference on Decision and Control the American Control Conference the IEEE International Symposium on Intelligent Control IEEE Symposium on Computers and Communications and the IEEE Global Telecommunications Conference. He has also served in the executive committees for the IEEE Co

Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per-unit-time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max-min optimal policy, which maximizes the worst-case average per-unit-time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time-varying transition rates.

关键词： Markov decision processes sensitivity max-min control call admission control policy iteration

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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