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2nd IEEE Conference on control Technology and Applications, CCTA 2018

作者： Sekunda, Andre Ding, Steven Kjolstad Poulsen, Niels Niemann, Henrik Department of Electrical Engineering Automation and Control Tech. University of Denmark Kgs. LyngbyDk-2800 Denmark Dept. of Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany Dept. of Applied Mathematics and Computer Science Tech. University of Denmark Kgs. LyngbyDk-2800 Denmark

ISBN: (纸本)9781538676981

systems are often controlled using feedback loops. Fault diagnosis schemes are usually designed assuming that there is no feedback loop. Therefore fault diagnosis methods need to accommodate for the feedback loop. One such method is active fault diagnosis based on a fault signature system. This method is derived using the YJBK parametrisation, named after Youla, Jabr, Bongiorno and Kucera. It uses the derived fault signature system to determine the detectability of possible faults in the system. Deriving the fault signature system requires knowledge about the controller. This paper demonstrates the possibility of estimating the detectability of faults in the fault signature system using a gap-metric. The gap-metric has the advantage of only requiring knowledge about the plant. By using the gap-metric it is possible to estimate the detectability of faults without using information about the controller. © 2018 IEEE.

关键词： Fault detection

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Streaming Process Discovery for Lambda Architecture-based Process Monitoring Platform

Streaming Process Discovery for Lambda Architecture-based Pr...

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International Scientific and Technical Conference on computer Science and Information technologies

作者： Anatoliy Batyuk Volodymyr Voityshyn Dept. of Automated Control Systems Institute of Computer Science and Information Technologies Lviv Polytechnic National University Lviv Ukraine

Having precise information about how business processes are performed in real-life is an important competitive advantage for a modern company. The digital footprint left by processes in IT systems can be transformed to event data for further analysis by process mining techniques. One of the biggest challenge of process mining is to deal with streaming event data and provide operational support for the on-going processes. Current paper is devoted to a streaming process discovery algorithm implemented by the authors upon a near real-time process monitoring platform which is based on the lambda architecture.

关键词： Process mining Streaming process discovery Lambda architecture Heuristic Miner Event data Event logs XES Digital footprint

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Closed Loop Fault Detectability Based on a Gap-Metric

Closed Loop Fault Detectability Based on a Gap-Metric

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control Technology and Applications (CCTA),

作者： André Sekunda Steven Ding Niels Kjølstad Poulsen Henrik Niemann Department of Electrical Engineering Automation and Control Tech. University of Denmark Denmark Dept. of Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany Dept. of Applied Mathematics and Computer Science Tech. University of Denmark Kgs. Lyngby Denmark

关键词： Fault diagnosis Measurement Kernel Closed loop systems Fault detection Sensors

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Active Fault Diagnosis in Sampled-data systems

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IFAC-PapersOnLine 2015年第21期48卷 883-888页

作者： Niemann, Henrik Poulsen, Niels Kjolstad Department of Electrical Engineering Automation and Control Technical University of Denmark Building 326 DK-2800 Kgs. Lyngby Denmark Dept. of Applied Mathematics and Computer Science Section for Dynamical Systems Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

The focus in this paper is on active fault diagnosis (AFD) in closed-loop sampleddata systems. Applying the same AFD architecture as for continuous-time systems does not directly result in the same set of closed-loop matrix transfer functions. For continuous-time systems, the LFT (linear fractional transformation) structure in the connection between the parametric faults and the matrix transfer function (also known as the fault signature matrix) applied for AFD is not directly preserved for sampled-data system. As a consequence of this, the AFD methods cannot directly be applied for sampled-data systems. Two methods are considered in this paper to handle the fault signature matrix for sampled-data systems such that standard AFD methods can be applied. The first method is based on a discretization of the system such that the LFT structure is preserved resulting in the same LFT structure in the fault signature matrix as obtained for continuous-time systems. The other method is an approximation method, where the same structure is obtained for small parametric faults. © 2015

关键词： Continuous time systems

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Active fault diagnosis in sampled-data systems 9

Active fault diagnosis in sampled-data systems

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9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

作者： Niemann, Henrik Poulsen, Niels Kjølstad Department of Electrical Engineering Automation and Control Technical University of Denmark Building 326 Kgs. LyngbyDK-2800 Denmark Dept. of Applied Mathematics and Computer Science Section for Dynamical Systems Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

关键词： Continuous time systems

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Further development on the LPV fault tolerant control for vehicle dynamics 9

Further development on the LPV fault tolerant control for ve...

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9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

作者： Fergani, S. Sename, O. Dugard, L. Gáspár, P. GIPSA-Lab. Control Systems Dept. CNRS-Grenoble INP ENSE3 BP 46 St.Martin-d'Hères CedexF-38402 France Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 BudapestH-1111 Hungary

This paper aims at presenting the efficiency of the Linear Parameter Varying method for vehicle dynamics control, in particular when some actuators may be in failure. The case of the suspension actuators failure and braking actuators failure are presented. The main objective is to enhance the vehicle dynamics even with faulty actuators through the suspension control (for comfort and road holding improvements) and the braking/steering control (for road handling and safety). Indeed, the LPV/H∞ fault tolerant MIMO gain-scheduled Vehicle Dynamic control (VDSC) involves the steering actuators, rear brakes and four suspension systems, and aims at enhancing the yaw stability, lateral and vertical car performances (see Poussot-Vassal et al. (2011b), Doumiati et al. (2013)). This strategy is scheduled by 3 varying parameters (ρb,ρs and ρl). These parameters depend on a special monitoring system defined to evaluate the impact of braking/suspension actuator failures on the vehicle dynamical performances. The proposed LPV control structure then allows to handle such failures by an online adaptation of the control input distribution. Simulation results performed on a nonlinear model experimentally validated on a vehicle Renault Mégane Coupé MIPS (Mulhouse) subject to critical driving situations show that the proposed methodology is effective and robust. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Steering

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Remote monitoring of children with asthma, being treated in multidisciplinary hospital

Remote monitoring of children with asthma, being treated in ...

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IEEE Region 8 International Conference on Computational technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Leyla S. Namazova-Baranova Alexey I. Molodchenkov Elena A. Vishneva Elena V. Antonova Vladimir I. Smirnov Federal State Budgetary Institution “Scientific Centre of Children Health” Moscow Russia lab. Intelligent technologies and systems Federal Research Center “Computer Science and Control” of RAS Moscow Russia Dept. of standardization and clinical pharmacology Federal State Budgetary Institution “Scientific Centre of Children Health” Moscow Russia

The increase in the prevalence of allergic diseases requires the active implementation of the capabilities of modern technology in the process of providing health care for children with asthma. The paper describes the system for remote monitoring for asthma patients receiving treatment atthe Scientific Center of Children Health. We describe the method of estimating of the state of patients, the method of selection of medication therapy, the knowledge base and the rules implemented in it, interaction with the mobile application. The use of the system is necessary to improve disease control and prevention of exacerbations and improve quality of life as a sick child and his family.

关键词： Pediatrics Remote monitoring Servers Diseases Knowledge based systems

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Active fault isolation in MIMO systems 19

Active fault isolation in MIMO systems

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Niemann, Henrik Poulsen, Niels Kjølstad Department of Electrical Engineering Automation and Control Technical University of Denmark Building 326 LyngbyDK-2800 Kgs. Denmark Dept. of Applied Mathematics and Computer Science Section for Dynamical Systems Technical University of Denmark LyngbyDK-2800 Kgs. Denmark

ISBN: (纸本)9783902823625

Active fault isolation of parametric faults in closed-loop MIMO systems are considered in this paper. The fault isolation consists of two steps. The first step is group-wise fault isolation. Here, a group of faults is isolated from other possible faults in the system. The group-wise fault isolation is based directly on the input/output signals applied for the fault detection. It is guaranteed that the fault group includes the fault that had occurred in the system. The second step is individual fault isolation in the fault group. Both types of isolation are obtained by applying dedicated auxiliary inputs and the associated residual outputs. © IFAC.

关键词： Fault detection

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Editorial: Sliding-Mode Based Disturbance Estimation, Attenuation and Fault Detection

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IET control Theory & Applications 2015年第4期9卷 511-514页

作者： PROF. Leonid Fridman PROF. Shihua Li PROF. Arie Levant PROF. Xinkai Chen PROF. Xinhguo Yu Guest Editors Departamento de Ingeniera de Control y Robótica División de Ingeniera Eléctrica Facultad de Ingeníera UNAM Ciudad Universitaria D.F. 04510 México School of Automation Southeast University Si Pai Lou 2 Nanjing 210096 China Applied Mathematics Dept. School of Mathematical Sciences Tel-Aviv University Ramat-Aviv. Tel-Aviv 69978 Israel Department of Electronic and Information Systems Shibaura Institute of Technology Saitama-shi Saitama 337-8570 Japan Platform Technologies Research Institute RMIT University Melbourne VIC 3001 Australia

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