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IEEE Annual Conference on Decision and control

作者： F. D. Brunner T. M. P. Gommans W. P. M. H. Heemels F. Allgowert Institute for Systems Theory and Automatic Control (IST) University of Stuttgart 70550 Stuttgart Germany Control System Technology group Department of Mechanical Engineering Eindhoven University of Technology

ISBN: (纸本)9781479978878

In this paper, we propose a self-triggered estimator for discrete-time linear systems subject to unknown but bounded disturbances affecting both the system states and outputs. The proposed self-triggered estimator is a set-valued estimator that employs rollout techniques to reduce the communication between the sensors and the estimator with respect to a periodic sampling strategy. Moreover, at each time instant it provides an estimate of the plant state and a guaranteed bound on the difference between the true plant state and the estimate.

关键词： Linear system discrete-time discrete time systems Unknown Sensors Communication Research

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OWC wave power plants capture chamber modelling

OWC wave power plants capture chamber modelling

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OCEANS

作者： Aitor J. Garrido Erlantz Otaola Izaskun Garrido Jon Lekube Pedro Liria Julien Mader Department of Automatic Control and Systems Engineering EUITI de Bilbao University of the Basque Country Bilbao Spain Automatic Control Group University of the Basque Country (UPV/EHU) Bilbao Spain Marine Research. AZTI-Tecnalia Pasaia Spain

The European Union is promoting the use of renewable energies as a path to fight global warming. Among them are diverse ocean energies and in particular, Oscillating Water Column (OWC)-based wave energy stands as one of the most promising technologies. An OWC device is basically composed by a capture chamber with a turbo-generator module attached to it. Although the study of the induction power module can be considered a well-known matter, the control-oriented modelling of the capture chamber composes still an open issue. In this work, an analytical model of the OWC chamber is developed and numerically implemented. The proposed model is then successfully tested and validated by means of experimental results for the particular case of Mutriku OWC wave power plant, affording excellent results.

关键词： Turbines Art Mathematical model Wave power Numerical models Conferences

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An Efficient Distributed Algorithm for Multi-Stage Robust Nonlinear Predictive control

An Efficient Distributed Algorithm for Multi-Stage Robust No...

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European control Conference

作者： Ruben Marti Sergio Lucia Daniel Sarabia Radoslav Paulen Sebastian Engell Cesar de Prada Department of Systems Engineering and Automatic Control University of Valladolid Process Dynamics and Operations Group Technische Universitt Dortmund Department of Electromechanical Engineering Escuela Politecnica Superior University of Burgos

ISBN: (纸本)9781467371605

This paper presents an algorithmic development in the framework of computationally efficient robust Non-linear Model Predictive control (NMPC) which deals with a parametric plant-model mismatch, where the description of the evolution of the uncertainty is done using a scenario tree, known as multi-stage approach. In order to reduce the computational time and memory requirements of the multistage NMPC, the calculations of the optimal control inputs can be done scenario-wise in parallel. These parallelized calculations must enforce the satisfaction of the non-anticipativity constraints, which is negotiated iteratively among the scenarios using Lagrangean or price-driven decomposition. The main challenge in using such scheme is the determination of the values of the aggregate variables that are used to coordinate the scenario-wise computations for convergence to the feasibility of the non-anticipativity constraints. The proposed approach uses parametric sensitivities of the optimal model states with respect to the control inputs which are used for the iterative determination of the values of aggregated variables. The proposed method achieves good performance and faster convergence compared to traditional decomposition schemes. The potential of the approach is demonstrated in simulation example of an hydrodesulphurisation unit.

关键词： control input Nonlinear predictive control nonlinear model predictive control distributed algorithms memory requirements Converge computational time Parametric

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Real-time and model-free object tracking using particle filter with Joint Color-Spatial Descriptor

Real-time and model-free object tracking using particle filt...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Shile Li Seongyong Koo Dongheui Lee Chair of Automatic Control Engineering Department of Electrical Engineering and Computer Engineering Technische Universitat Munchen Munich Germany Autonomous Intelligent Systems (AIS) Group Computer Science Institute VI University of Bonn Germany

This paper presents a novel point-cloud descriptor for robust and real-time tracking of multiple objects without any object knowledge. Following with the framework of incremental model-free multiple object tracking from our previous work [5][7][6], 6 DoF pose of each object is firstly estimated with input point-cloud data which is then segmented according to the estimated objects, and incremental model of each object is updated from the segmented point-clouds. Here, we propose Joint Color-Spatial Descriptor (JCSD) to enhance the robustness of the pose hypothesis evaluation to the point-cloud scene in the particle filtering framework. The method outperforms widely used point-to-point comparison methods, especially in the partially occluded scene, which is frequently happened in the dynamic object manipulation cases. By means of the robust descriptor, we achieved unsupervised multiple object segmentation accuracy higher than 99%. The model-free multiple object tracking was implemented by using a particle filtering with JCSD as a likelihood function. The robust likelihood function is implemented with GPU, thus facilitating real-time tracking of multiple objects.

关键词： Data models Robustness Adaptation models Object tracking Three-dimensional displays Image color analysis Real-time systems

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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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Educational Innovation: Interaction and Relationship Inside A Sub-Module

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Procedia - Social and Behavioral Sciences 2015年 186卷 395-400页

作者： Fernando Oterino-Echavarri Jose Manuel Lopez-Guede Ekaitz Zulueta Manuel Graña Department of Electronics University College of Engineering of Vitoria Basque Country University (UPV/EHU) C/Nieves Cano 12 01006 Vitoria-Gasteiz Spain Department of Systems Engineering and Automatic Control University College of Engineering of Vitoria Basque Country University (UPV/EHU) C/Nieves Cano 12 01006 Vitoria-Gasteiz Spain Computational Intelligence Group Basque Country University (UPV/EHU) Department of Computer Science and Artificial Intelligence Faculty of Informatics Basque Country University (UPV/EHU) Paseo Manuel de Lardizabal 1 20018 Donostia-San Sebastián Spain

In this paper authors will expose a work developed with the aim to submit an educational innovation project proposal to a competitive call for Educational Innovation Projects 2013-2015 of the Education Advisory Service of the Basque Country University (UPV/EHU, Spain). This project is being carried out in the Computer Structure and Computer Architecture sub-module of the Degree in Computer Management and Information systems engineering of the University College of engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU). The project is based on the active learning, more specifically, on cooperative learning. In this paper we have given deeper insight the dependencies between all the subjects belonging to the analyzed sub-module, which is composed of two subjects named Computer Structure and Computer Architecture. We have included in such analysis a previous subject named Principles of Digital systems Design, which does not belong to that sub-module, but plays an important role in the acquisition of the knowledge and competencies of the two previously referenced subjects.

关键词： Active Learning Cooperative Learning Computer engineering Computer Structure Computer Architecture

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Adaptive FOCV-based control Scheme to improve the MPP Tracking Performance: an experimental validation

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IFAC Proceedings Volumes 2014年第3期47卷 4967-4971页

作者： A. Frezzetti S. Manfredi A. Suardi Automatic Control Group Department of Electrical Engineering and Technology Information (DIETI) University of Naples ”Federico II” IT Control and Power Group and Circuits and Systems Group Department of Electrical and Electronic Engineering Imperial College London UK

Nowadays the photovoltaic (PV) is one of the most renewable source device used in the harvesting system to support the life time of stand alone devices like sensor node. One of the most diffused method to increase the PV efficiency is the Fractional Open-Circuit Voltage (FOCV) that allows the PV to work around its estimated Maximum Power Point (MPP). To increase the solar harvesting system efficiency both static (in terms of efficiency) and dynamic (in terms of MPP tracking responsiveness) performances are of crucial interest. Most of works in the literature focus on improving the static system performance by giving a more accurate MPP estimation. In this paper, it is proposed a FOCV-based algorithm to improve system dynamic performance in terms of MPP tracking performance and energy conveyed to the load under varying solar irradiance conditions. The MPP system dynamically adapts the MPP estimation to the solar irradiance condition by mean of a ”smart timer” circuitry that continuously adjusts the operative frequency of the sample and hold component. A detailed hardware implementation description of a smart timer as well as a related analytical analysis used for component design are presented. An experimental validation and a comparison of the proposed approach than the standard FOCV based method are carried out. The results show the effectiveness of the proposed scheme in improving dynamic system performance in terms of tracking performance and timeless to convey solar energy to the load storage component (i.e. ultracapacitor).

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Embedding of nonlinear systems in a linear parameter-varying representation 19

Embedding of nonlinear systems in a linear parameter-varying...

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

作者： Abbas, Hossam S. Tóth, Roland Petreczky, Mihály Meskin, Nader Mohammadpour, Javad Department of Electrical Engineering College of Engineering Qatar University PO Box 2713 Doha Qatar Control Systems Group Eindhoven University of Technology P.O. Box 513 Eindhoven5600 MB Netherlands Department of Computer Science and Automatic Control Ecole des Mines de Douai Douai59508 France Intelligent Control Systems Lab. College of Engineering University of Georgia AthensGA30602 United States Electrical Engineering Department Faculty of Engineering Assiut University Assiut Egypt

ISBN: (纸本)9783902823625

This paper introduces a systematic approach to synthesize linear parameter-varying (LPV) representations of nonlinear (NL) systems which are originally defined by control affine state-space representations. The conversion approach results in LPV state-space representations in the observable canonical form. Based on the relative degree concept of NL systems, the states of a given NL representation are transformed to new coordinates that provide its normal form. In the SISO case, all nonlinearities of the original system are embedded in one NL function which is factorized to construct the LPV form. An algorithms is proposed for this purpose. The resulting transformation yields an LPV model where the scheduling parameter depends on the derivatives of the inputs and outputs of the system. In addition, if the states of the NL model can be measured or estimated, then the procedure can be modified to provide LPV models scheduled by these states. Examples are included for illustration. © IFAC.

关键词： State space methods

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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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Computer-aided diagnosis of abdominal aortic aneurysm after endovascular repair using active learning segmentation and texture analysis

Computer-aided diagnosis of abdominal aortic aneurysm after ...

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13th Mediterranean Conference on Medical and Biological engineering and Computing 2013, MEDICON 2013

作者： García, G. Maiora, J. Tapia, A. Graña, M. De Blas, M. Systems Engineering and Automatic Control Department -EUP University of the Basque Country San Sebastián Spain Electronic Technology Department -EUP University of the Basque Country San Sebastián Spain Computational Intelligence Group University of the Basque Country San Sebastián Spain Radiology Department Donostia Hospital San Sebastián Spain

ISBN: (纸本)9783319008455

Endovascular repair is a minimal invasive alternative to open surgical therapy. From a long term perspective, complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear influencing the evolution of treatment. The objective of this work is to develop a Computer-Aided Diagnosis system (CAD) for an automated classification of EndoVascular Aneurysm Repair (EVAR) progression from Computed Tomography Angiography (CTA) images. The CAD system is based on the extraction of texture features from segmented thrombus aneurysm samples and a posterior classification. Image segmentation is produced by an interactive Active Learning procedure based on Random Forest (RF) classification. An initial set of labeled points are required to start training an RF. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. Three texture-analysis methods such as the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), and the gray level difference method (GLDM), were applied to each ROI to obtain texture features. Classification of the ROI is carried out by an ensemble of SVM classifiers (ECs). The final decision is based on the application of a voting scheme across the outputs of the individual SVMs. The performance of the classifier was evaluated using 10-fold cross validation and the average of accuracy (93.84% ± 0.29), sensitivity (94.72 ± 0.23), and specificity (91.48 ± 0.35) results. © Springer International Publishing Switzerland 2014.

关键词： Repair

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