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World Congress on Intelligent control and Automation

作者： Zhi-Hua Li Qing-Hao Meng Pei-Feng Qi Yu Zhou Shu-Gen Ma Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems Tianjin University Department of Robotics College of Science and Engineering Ritsumeikan University

ISBN: (纸本)9781467384155

The true-fake detection of Chinese liquors can be considered as a one-class classification problem. Using only positive sample in training process brings about difficulties in determining the optimal parameters of one-class SVM and its kernel. And one-class SVM optimized with conventional grid search method has a low recognition rate of positive test samples. Hence, we proposed a new idea for recognition, namely boosting-based one-class SVM. Six different kinds of liquors were sampled by a self-designed electronic nose system. After the preprocessing and feature extraction of sample data, 18 groups of optimum parameters of one-class SVM were chosen from specified parameter range, each group was used to train a classifier with training algorithm. Then the true-fake decisions were made for test samples with boosting integration rules. Finally the recognition rates of Hongjinjiu and Lanjinjiu positive sample reached 95.24% and 100%, and the recognition rates of both positive and negative sample reached 97.04% and 94.83%, respectively.

关键词： Support Vector Network optimal parameter Electronic Nose Recognition (Psychology) Search methods Chinese Chinese Chinese philosophy Chinese art Feature extraction test sample

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Rapid Detection of Chinese Liquors Using a Portable E-nose Based on C-SVM

Rapid Detection of Chinese Liquors Using a Portable E-nose B...

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World Congress on Intelligent control and Automation

作者： Pei-Feng Qi Qing-Hao Meng Ya-Qi Jing Ming Zeng Shu-Gen Ma Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems Tianjin University Department of Robotics College of Science and Engineering Ritsumeikan University

ISBN: (纸本)9781467384155

We designed a portable electronic nose (e-nose) for rapid detection of Chinese liquors. The e-nose had an evaporation chamber wrapped with silicone heating band, which could vaporize the liquor samples in 1 minute. The sequential sampling and clean-out lasted 100 seconds and 3 minutes, respectively. Hence, one fast detection process could be finished within 3 minutes without regard to clean-out. In order to acquire enough information from array's sampling curves for classification, we chose 60 features (including 6 features per sensor) according to the response curves, the derivative of the response curves and the relative change rate curves of conductivity. Then the dimension of features was reduced by principle component analysis (PCA). Finally, classification of Chinese liquors was performed using C-Support Vector Machine (C-SVM) and the classification rate was 96.7%.

关键词： Sensor arrays Conductivity Principal component analysis Gas detectors Heating Support vector machine classification Support vector machine classification Electronic Nose Gas detectors Sensor arrays principal components analysis conductivity response curves change rate Heating Chinese Chinese Chinese philosophy Chinese art

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Multi-criteria optimisation design of shapers with piece-wise equally distributed time-delay

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IFAC-PapersOnLine 2016年第10期49卷 112-117页

作者： Pilbauer, Dan Michiels, Wim Vyhlidal, Tomas CIIRC - Czech Institute of Informatics Robotics and Cybernetics and Dept. of Instrumentation and Control Eng. Faculty of Mechanical Engineering Czech Technical University in Prague Technická 4 166 07 Praha 6 Czech Republic Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A HeverleeB-3001 Belgium

The paper presents an optimisation based method to parametrize input shapers with time delays of piece-wise-equal distribution. The design respects the two key requirements on the shaper performance that are the prompt response time and the robustness represented by residual vibration functions, which are however conflicting criteria as a rule. Thus, the optimisation task is solved as a constrained multi-objective problem where the constraints respect the additional requirements on the shaper performance such as a non-decreasing step response or zero residual function at some frequency points. The result is a Pareto front that shows a trade-off between the two main requirements. Next to the presentation of the results in an example, a user friendly shaper-design tool is presented. © 2016

关键词： Multiobjective optimization computer control systems Constrained optimization Economic and social effects Time delay Timing circuits Distributed time delays Multi criteria optimisation Multi objective problem Pareto frontiers Residual functions Residual vibrations Signal shaper Vibration suppression

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

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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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Robust Fault-tolerant Iterative Learning control for Discrete Systems via Linear Repetitive processes Theory

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International Journal of Automation and computing 2015年第3期12卷 254-265页

作者： Jian Ding Blazej Cichy Krzysztof Galkowski Eric Rogers Hui-Zhong Yang Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Institute of Control and Computation Engineering University of Zielona Gora Department of Electronics and Computer Science University of Southampton

This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetitive processes is used to develop formulas for gain matrices design, together with convergent conditions in terms of linear matrix inequalities. An extension to deal with model uncertainty of the polytopic or norm bounded form is also developed and an illustrative example is given.

关键词： Iterative learning control linear repetitive processes linear matrix inequality(LMI) discrete linear systems fault-tolerant cont

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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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Weight update sequence in MLP networks

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8669卷 266-274页

作者： Kordos, Miroslaw Rusiecki, Andrzej Kamiński, Tomasz Greń, Krzysztof University of Bielsko-Biala Department of Mathematics and Computer Science Bielsko-Biala Willowa 2 Poland Wroclaw University of Technology Institute of Computer Engineering Control and Robotics Wroclaw Wybrzeże Wyspiańskiego 27 Poland

ISBN: (纸本)9783319108391

The advantages of Variable Step Search algorithm - a simple local search-based method of MLP training is that it does not require differentiable error functions, has better convergence properties than backpropagation and lower memory requirements and computational cost than global optimization and second order methods. However, in some applications, the issue of training time reduction becomes very important. In this paper we evaluate several approaches to achieve this reduction. © Springer International Publishing Switzerland 2014.

关键词： Global optimization

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A robust nonlinear controller for nontrivial quadrotor maneuvers: Approach and verification

A robust nonlinear controller for nontrivial quadrotor maneu...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Yuyi Liu J. M. Montenbruck P. Stegagno F. Allgower A. Zell Department of Computer Science University of Tubingen Tubingen Germany Autonomous Robotics and Human-Machine Systems group Max Planck Institute for Biological Cybernetics Tubingen Germany Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Eberhard Karls Universitat Tubingen Tubingen Baden-WÃ¼rttemberg DE

This paper presents a nonlinear control approach for quadrotor Micro Aerial Vehicles (MAVs), which combines a backstepping-like regulator based on the solution of a certain class of global output regulation problems for the rigid body equations on SO(3), a robust controller for the system with bounded disturbances, as well as a trajectory generator using a model predictive control method. The proposed algorithm is endowed with strong convergence properties so that it allows the quadrotor MAVs to reach almost all the desired attitudes. The control approach is implemented on a high-payload-capable quadcopter with unstructured dynamics and unknown disturbances. The performance of our algorithm is demonstrated through a series of experimental evaluations and comparisons with another control method on normal and aggressive trajectory tracking tasks.

关键词： Attitude control Trajectory Robustness Heuristic algorithms Mathematical model Tracking Aerodynamics

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