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7th International Joint Conference on Computational Intelligence, IJCCI 2015

作者： Kubalìk, Jiřì Alibekov, Eduard Žegklitz, Jan Babuška, Robert Czech Institute of Informatics Robotics and Cybernetics CTU in Prague Prague Czech Republic Department of Cybernetics Faculty of Electrical Engineering CTU in Prague Prague Czech Republic Delft Center for Systems and Control Delft University of Technology Delft Netherlands

ISBN: (纸本)9783662535240

This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact version of the best-performing graph to the beginning and to the end of the population, respectively, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on five symbolic regression benchmarks and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to improve the performance of the SNGP algorithm. We then propose two variants of hybrid SNGP utilizing a linear regression technique, LASSO, to improve its performance. The proposed algorithms have been compared to the state-of-the-art symbolic regression methods that also make use of the linear regression techniques on four real-world benchmarks. The results show the hybrid SNGP algorithms are at least competitive with or better than the compared methods. © Springer-Verlag Berlin Heidelberg 2016.

关键词： Genetic programming

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Design considerations for a large quadrotor with moving mass control

Design considerations for a large quadrotor with moving mass...

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International Conference on Unmanned Aircraft systems

作者： Tomislav Haus Matko Orsag Stjepan Bogdan the University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 10000 Zagreb

ISBN: (纸本)9781467393355

In this paper we present our ongoing efforts to build a heavy lift multirotor platform capable of lifting over 50kg of payload. Such a system requires a paradigm shift in the design of the UAV. Therefore we propose using miniature two stroke internal combustion engines to supply the necessary lift and endurance and combine them with a novel control concept based on the variations of the center of gravity (CoG) of the system. In this paper we present a detailed stability and sensitivity analysis of the proposed control scheme and discuss its underlining effect on the construction design parametrization. We present simulation results from a Gazebo based simulator that confirm the results of our mathematical analysis.

关键词： Design considerations large

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Aerial-ground Robotic System for Autonomous Delivery Tasks

Aerial-ground Robotic System for Autonomous Delivery Tasks

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IEEE International Conference on robotics and Automation

作者： Barbara Arbanas Antun Ivanovic Marko Car Tomislav Haus Matko Orsag Tamara Petrovic Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 10000 Croatia

ISBN: (纸本)9781467380270

In this paper we present a study of a robotic system that consists of an unmanned aerial vehicle equipped with a pair of manipulator arms (MMUAV), and unmanned ground vehicles (UGVs). The envisioned application scenario includes autonomous packet transportation, where MMUAV is used for picking/placing packets, while both MMUAV and UGV can be used for packet transportation, with different energy consumption profiles. We propose a reactive method for decentralized task planning and coordination of robots using hierarchical task decomposition based on TAEMS framework. Our approach takes into account low-level motion-planning aspects of the system as well as high-level mission specification, making this a multi-layered system. For low-level planning we use sampling-based planner combined with obstacle-free trajectory generation. Methods are verified in simulations and on an experimental testbed, using 3D robotics quadcopter and Pioneer 3DX mobile robots with the results showing stability and robustness of the presented methods.

关键词： System Autonomous Delivery

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Jacobian motion planning of nonholonomic robots: The Lagrangian Jacobian algorithm 10

Jacobian motion planning of nonholonomic robots: The Lagrang...

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10th International Workshop on Robot Motion and control, RoMoCo 2015

作者： Tchoń, Krzysztof Góral, Ida Ratajczak, Adam Cybernetics and Robotics Electronics Faculty Wroclaw University of Technology Wroclaw50-372 Poland Automation Mechatronics and Control Systems Electronics Faculty Wroclaw University of Technology Wroclaw50-372 Poland

ISBN: (纸本)9781479970438

For nonholonomic robots whose kinematics are described by a driftless control system with outputs a new Jacobian inverse and a Jacobian motion planning algorithm are proposed. This inverse is based on solving a Lagrange-type constrained optimization problem, generalizing the usual Jacobian pseudoinverse. A case study illustrates a potential applicability of the new algorithm to shaping the system's motion. © 2015 IEEE.

关键词： Motion planning

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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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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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Selection of 5 kW converter leg for power electronic system for residential buildings

Selection of 5 kW converter leg for power electronic system ...

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European Conference on Power Electronics and Applications

作者： Marcin Zygmanowski Jaroslaw Michalak Michal Jelen Grzegorz Jarek Jan Popczyk Faculty of Electrical Engineering Dept. of Power Electronics Electrical Drives and Robotics SILESIAN UNIVERSITY OF TECHNOLOGY ul. B. Krzywoustego 2 Gliwice Poland Faculty of Electrical Engineering Institute of Power Systems and Control SILESIAN UNIVERSITY OF TECHNOLOGY ul. B. Krzywoustego 2 Gliwice Poland

ISBN: (纸本)9781509014101

Three converter leg variants are analyzed for low power converter used for power electronic system for residential buildings. The two-level Si-IGBT and SiC-MOSFET converters are compared with Si-IGBT three-level T-type converter. Power losses generated in each of these converters over a predicted period of 20 years of operation are contrasted with the cost of converter options. The detailed selection procedure for output inductor is presented in this paper. This procedure shows the influence of the inductor parameters like number of turns, air gap length on its losses, cost and size. Theoretical approach is verified with simulations and experimental results.

关键词： Design IGBT Silicon Carbide (SiC) MOSFET Microgrid

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Eye-RHAS Manipulator: From Kinematics to Trajectory control

Eye-RHAS Manipulator: From Kinematics to Trajectory Control

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RSI/ISM International Conference on robotics and Mechatronics

作者： Ebrahim Abedloo Soheil Gholami Hamid D. Taghirad Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence (ICCE) Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781467372350

One of the challenging issues in the robotic technology is to use robotics arm for surgeries, especially in eye operations. Among the recently developed mechanisms for this purpose, there exists a robot, called Eye-RHAS, that presents sustainable precision in vitreo-retinal eye surgeries. In this work the closed-form dynamical model of this robot has been derived by Gibbs-Appell method. Furthermore, this formulation is verified through SimMechanics Toolbox of MATLAB . Finally, the robot is simulated in a real time trajectory control in a teleoperation scheme. The tracking errors show the effectiveness and applicability of the dynamic formulation to be used in the teleoperation schemes.

关键词： Eye Surgery Eye-RHAS Gibbs-Appell Phantom Omni Real Time Trajectory control SimMechanics

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Optimization of KNTU Delta Robot for Pick and Place Application

Optimization of KNTU Delta Robot for Pick and Place Applicat...

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RSI/ISM International Conference on robotics and Mechatronics

作者： Amin Khorasani Soheil Gholami Hamid D. Taghirad Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence (ICCE) Faculty of Electrical Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781467372350

In this paper, the desired configuration for installation of Delta robot is formulated as an optimization problem and has been solved to reach to the highest rate of pick and place operation. The optimization is performed considering the actuators speed and acceleration limitation of the robot within the workspace. Furthermore, energy consumption is considered next as the other optimization objective, and it is shown that the optimal region for the first optimization problem lies within that of the latter one, and therefore, there is no need to propose a multi-objective optimization problem in this case. As a proof of concept, KNTU Delta robot is designed and implemented in practice by using the optimal configuration, and it is observed that the optimal design is very promising in practice.

关键词： Delta robot installation configuration energy consumption optimal solution pick and place application genetic algorithm real time implementation

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Agent Motivational and Repulsion Forces Optimization in Agent Basic Movement Model

Agent Motivational and Repulsion Forces Optimization in Agen...

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Asian control Conference

作者： Nurulaqilla Khamis Mohammad Fadzli Haniff Hazlina Selamat Omar Farouq Lutfy Centre for Artificial Intelligence & Robotics Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Centre for Artificial Intelligence & Robotics Electrical Engineeering Faculty Universiti Teknologi Malaysia Centre for Artificial Intelligence & Robotics Electrical Engineering Faculty Universiti Teknologi Malaysia Control & Systems Engineering Department University of Technology

ISBN: (纸本)9781479978632

Human will have their own preferences when they are walking such as direction in their movement, destination and walking speed. These preferences and walking patterns can be affected by several factors including psychological behavior of evading other people or obstacle to avoid any collision by exerting certain repulsion forces. To demonstrate plausible human movement pattern, agent simulation model as a tool has been developed in this research studies where the agent will represent the human with the physical characteristics of body radius and Field of View (FoV) to navigate their surroundings. The modification in the proposed agent model was done by utilizing the physical force approach which results in a simpler mathematical representation compared to the original model. In the proposed model, two parameters, namely, K_(ad) and K_(ro), are used to represent the agent motivational force to move towards its target destination and the obstacle repulsion, respectively. Moreover, heuristics and non-linear parameters were used in the proposed model to provide more realistic agent motion by maintaining their safety boundary from obstacles. To increase the accuracy of the modified model, the Particle Swarm Optimization (PSO) algorithm is used to find the optimal settings for the parameters of the agent basic movement model. The PSO has achieved superior results by optimizing the model parameters to provide safe distance trajectories between agents and obstacles.

关键词： Agents research report Repulsion model parameters original model walking barriers Human Force Movements Diaphysis of radius Two-Parameter Models Physical characteristics

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