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Generalized Norm Optimal Iterative Learning control with Intermediate Point and Sub-interval Tracking

International Journal of Automation and computing 2015年第3期12卷 243-253页

作者： David H.Owens Chris T.Freeman Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield School of Electronics and Computer Science University of Southampton Advanced Robotics Division Italian Institute of Technology

Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.

关键词： Iterative learning control learning control optimization linear systems robotics.

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Towards multi-functional light-weight long-term real-time coastal ocean observation system 11

Towards multi-functional light-weight long-term real-time co...

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Jiang, Zhenfeng Ma, Yanming Chen, Jiali Wang, Zigeng Peng, Zheng Liu, Jun Han, Guitao Smart Ocean Industry Research Institute Nantong Jiangsu226000 China Computer Science and Engineering Department University of Connecticut StorrsCT06269 United States Computer Science Deparment City College of New York NY10031 United States Computer Science and Technology Department Jilin University Changchun Jilin130012 China State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

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Robot 2015: Second Iberian robotics conference: Advances in robotics, volume 1 2nd

Robot 2015: Second Iberian Robotics conference: Advances in ...

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2nd Iberian robotics Conference, ROBOT 2015

作者： Reis, Luís Paulo Moreira, António Paulo Lima, Pedro U. Montano, Luis Muñoz-Martinez, Victor University of Minho School of Engineering Information Systems Department Guimarães Portugal University of Porto INESC-TEC Porto Portugal University of Lisbon Instituto Superior Técnico Institute for Systems and Robotics Lisboa Portugal University of Zaragoza School of Engineering and Architecture Computer Systems Engineering Department Zaragoza Spain University of Malaga Superior Technical School of Industrial Engineers Automatic Control Systems Engineering Department Málaga Spain

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A simulator for swarm AUVs acoustic communication networking 11

A simulator for swarm AUVs acoustic communication networking

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Li, Guannan Liu, Jun Wang, Xue Xu, Hongli Cui, Jun-Hong Shenyang Institute of Automation CAS University of Chinese Academy of Sciences No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Changchun China Shenyang Institute of Automation CAS Northeast University No. 114 Nanta Street Shenhe District Shenyang China Shenyang Institute of Automation CAS No. 114 Nanta Street Shenhe District Shenyang China College of Computer Science and Technology Jilin University Changchun China Underwater Sensor Network Lab. University of Connecticut StorrsCT06269 United States

ISBN: (纸本)9781450346375

This paper presents a simulator for swarm operations designed to verify algorithms for a swarm of autonomous underwater robots (AUVs), specifically for constructing an underwater communication network with AUVs carrying acoustic communication devices. This simulator consists of three nodes: a virtual vehicle node (VV), a virtual environment node (VE), and a visual showing node (VS). The modular design treats AUV models as a combination of virtual equipment. An expert acoustic communication simulator is embedded in this simulator, to simulate scenarios with dynamic acoustic communication nodes. The several simulations we have performed demonstrate that this simulator is easy to use and can be further improved.

关键词： Simulators

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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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Nonlinear soft tissue mechanics based on polytopic Tensor Product modeling 14

Nonlinear soft tissue mechanics based on polytopic Tensor Pr...

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IEEE 14th International Symposium on Applied Machine Intelligence and Informatics, SAMI 2016

作者： Takács, Árpád Haidegger, Tamás Galambos, Péter Kuti, József Rudas, Imre J. Antal Bejczy Center for Intelligent Robotics Óbuda University Bécsi út 96/b Budapest1034 Hungary Dept. of Telecommunication and Media Informatics Budapest University of Technology and Economics Magyar Tudósok Krt. 2. Budapest1117 Hungary Institute for Computer Science and Control Hungarian Academy of Sciences Kende u. 13-17 Budapest1111 Hungary Austrian Center for Medical Innovation and Technology Viktor-Kaplan-Strasse 2 Wiener Neustadt2700 Austria

ISBN: (纸本)9781467387408

Achieving reliable force control is one of the main design goals of robotic teleoperation. It is essential to grant safe and stable performance of these systems, regarding HMI control, even under major disturbing conditions such as time delay or model parameter uncertainties. This paper discusses the systematic derivation of polytopic qLPV model from the nonlinear dynamics of typical soft tissues of the human body based on recent experimental results. The derivation is based on the Tensor Product (TP) Model Transformation. The presented method is a crucial step in laying the foundations of adequate force control in telesurgery. The proposed approach could form the basis of LMI-based controller design. © 2016 IEEE.

关键词： Force control

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Functional-reliability model of a services system with path reconfiguration ability

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2015年 307卷 167-187页

作者： Zamojski, Wojciech Sugier, Jaroslaw Wroclaw University of Technology Poland Institute of Computer Engineering Control and Robotics Wroclaw Poland

In this work we analyse operation of a services network which is built from processing nodes serving dedicated services and communication links transmitting required information resources. For task executions specific subsets of network resources – so called communication paths – are allocated. Execution of a task in communication paths is disrupted by occurring faults which are eliminated by renewal procedures leading to delays in task completion time or even to task cancellation. One of the method for overcoming the negative effects of the faults is to apply a path reconfiguration mechanism i.e. a redirection of the communication traffic which bypasses the damaged link. We propose a functional-reliability dependability model of the services network which takes into account possible path reconfigurations. We also define a network dependability parameter which evaluates network efficiency by finding the degree of task losses among all the jobs being executed in the network. © Springer International Publishing Switzerland 2015

关键词： computers

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Nonlinear soft tissue mechanics based on polytopic Tensor Product modeling

Nonlinear soft tissue mechanics based on polytopic Tensor Pr...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Árpád Takács Tamás Haidegger Péter Galambos József Kuti Imre J. Rudas Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Dept. of Telecommunication & Media Informatics Budapest University of Technology and Economics Budapest Hungary

关键词： Optimized production technology Mathematical model Biological systems Biological system modeling

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computer-assisted surgical trainer for minimally invasive surgery

Computer-assisted surgical trainer for minimally invasive su...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Ryszard Klempous Jan Nikodem Andrzej Wytyczak-Partyka Institute of Computer Engineering Control and Robotics Wroclaw University of Technology Wroclaw Poland

The paper presents the current state of research on developing a computer-assisted surgical trainer to be used in a minimally-invasive surgery training program. Along with wide adoption of laparoscopic surgery a strong need for systematic training in the field of minimally-invasive surgery has developed. The goal of the described system is to provide surgeons specialising in minimally-invasive operating techniques an objective performance evaluation method and a safe setting for practicing elementary laparoscopic skills.

关键词： Training Three-dimensional displays Computed tomography Solid modeling Minimally invasive surgery Splines (mathematics)

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More safe optimal input signals for parameter estimation of linear systems described by ODE

IFIP Advances in Information and Communication Technology

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IFIP Advances in Information and Communication Technology 2014年 443卷 267-277页

作者： Rafajlowicz, Ewaryst Rafajlowicz, Wojciech Institute of Computer Engineering Control and Robotics Wroclaw Poland

Our starting point is the ascertainment that D-optimal input signals recently considered by the same authors [12] can be too dangerous for applying them to real life system identification. The reason is that they grow too fast in time. In order to obtain more safe input signals, but still leading to a good estimation accuracy of parameter estimates, we propose a quality criterion that is a mixture of D-optimality and a penalty for too fast growth of input signals in *** derivations are parallel to those in [12] up to a certain point only, since we obtain different optimality conditions in the form of an integral equation. We also briefly discuss a numerical algorithm for its solution. © IFIP International Federation for Information Processing 2014.

关键词： Integral equations

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