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Chinese control and Decision Conference, CCDC

作者： Boja Grčar Peter Cafuta Gorazd Štumberger Anton Hofer Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia TU Graz Institute for Control and Automation Austria

In the contribution new results in a control design of a general three-dimensional non-holonomic integrator (NHI) including drift terms is proposed. Beside global asymptotic stability (GAS) proposed non-linear controller assure also minimal input norm during transients and in steady states and high dynamic in output response for a piecewise constant reference input. Due to appropriate input modulation internal states follow natural periodic orbits necessary to fulfill non-holonomic constraints. Introducing a non-linear transformation that describe error dynamics feedback stability is proven by using Lyapunov argumentation. Practical value of the proposed design is illustrated on the example of induction machine torque control along with a simulation and experimental results.

关键词： Torque Rotors Steady-state Stators Inductance control design Induction motors

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Physical Modelling of Energy Consumption of Industrial Articulated Robots

Physical Modelling of Energy Consumption of Industrial Artic...

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International Conference on control, automation and Systems

作者： Akeel Othman Kvetoslav Belda Pavel Burget Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Department of Adaptive Systems Institute of Information Theory and Automation of the CAS

ISBN: (纸本)9781479999767

The paper deals with a modelling of the energy consumption of industrial articulated robots. The proposed modelling way is based on graphically-oriented computer-aided concept that exploits CAD software Solidworks and simulation environment MATLAB/Simulink with SimMechanics and SimPowerSystems libraries. These software tools are used for the composition of a dynamical simulation model that represents both mechanical robot structure and robot drives during robot motions. The paper addresses mathematical analysis and interpretation of the considered simulation model. Here, equations of the motion for the mechanical robot structure and appropriate dynamical equations of the robot drives are introduced. Using these equations, the energy consumption equation is defined. The proposed way is demonstrated by simulation experiments for several different velocities of the robot motion along a selected trajectory. For the experiments, industrial articulated robot KUKA KR 5 arc driven by PMSM drives is considered.

关键词： Industrial articulated robots Energy consumption Mathematical analysis Computer-aided modelling

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Global Finite-Time Tracking control for Rigid Manipulators Based on Adding a Power Integrator

Global Finite-Time Tracking Control for Rigid Manipulators B...

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第三十四届中国控制会议

作者： Mohamed Elamin Sahabi Xiangyu Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Faculty of Engineering Blue Nile University

ISBN: (纸本)9781467374439

This paper investigates the finite-time tracking control problem for rigid manipulator systems. The proposed controller is based on adding a power integrator technique, which guarantees global finite-time convergence of tracking errors. Compared with the backstepping control scheme, the proposed control scheme provides a faster convergence rate for the tracking error system. Simulation results show the effectiveness of the proposed control scheme.

关键词： Manipulator,tracking control,finite-time control,adding a power integrator technique

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Research on self-learning method on generation and optimization of industrial firewall rules

Research on self-learning method on generation and optimizat...

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International Conference on Information Security and Cyber Forensics (InfoSec)

作者： Wenli Shang Ming Wan Peng Zeng Quansheng Qiao Chinese Academy of Science Shenyang Institute of Automation Shenyang China Information & Control Engineering Faculty Shenyang Jianzhu University Shenyang China

In order to make the rule setting more convenient, this paper presents a self-learning method on the generation and optimization of industrial firewall rules. This method includes three parts: packet preprocessing, rule self-learning and rule optimization. First, packet capturing and analysis accomplish the data preprocessing; Secondly, by using the self-learning module, this method generates the firewall rule set. Finally, the rule set is optimized to reduce the number of rules and shorten the packet matching length. Experiments and comparative results show that the proposed method meet effectiveness and real-time requirements of industrial control system communication control.

关键词： Firewalls (computing) Protocols Industrial control Optimization Algorithm design and analysis Ports (Computers)

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Self-adaptive cuckoo search algorithm for hybrid flowshop makespan problem

Self-adaptive cuckoo search algorithm for hybrid flowshop ma...

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IEEE International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Han Zhonghua Dong Xiaoting Lv Xisheng Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang China Department of Digital Factory Shenyang Institute of Automation CAS Shenyang China

ISBN: (纸本)9781479987313

As a typical NP-hard combination optimization problem, the hybrid flow shop widely exists in manufacturing systems. In this paper, a mathematical model of hybrid flow shop is formulated, and then a new encoding and decoding method based on matrix is designed, together with Self-Adaptive Cuckoo Search(SACS) algorithm to minimize the makespan of this problem. The main contribution of this paper is to develop a new approach hybridizing CS with bottleneck heuristic method to fully exploit the bottleneck stage, and then bring in a self-adaptive parameter adjusting strategy along with iterations to enhance the ability to jump out of local extreme value and maintain the evolution energy. furthermore, elite learning strategies and some local search methods are applied to enhance the local search ability. The comparison between the proposed algorithm and several effective algorithms show that the SACS algorithm is feasible and practical.

关键词： Sociology Statistics Job shop scheduling Optimization Search problems Floors Algorithm design and analysis

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Icing sensor on the overhead powerlines wires

Icing sensor on the overhead powerlines wires

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International Conference on Computational Problems of Electrical engineering (CPEE)

作者： Oleksandr Kozlovskyi Dmitro Trushakov Serhiy Rendzinyak Faculty of Automation and Power Engineering Kirovograd National Technical University Kirovograd Ukraine Institute of Electrical Power Engineering and Control Systems Lviv Polytechnic National University Lviv Ukraine

In systems intended for icing diagnosis on overhead power lines in exploitation conditions the imperfect icing sensors are used on their wires. Their parameters are different from parameters of controlled wires. The construction of icing sensor was developed based on overhead power lines with uninsulated wires of A, AC types. The threshold values of the ohmic resistance of the sensor were determined for two types of ice-hoarfrost sediments: rime and ice. It is proved that sensor length may be reduced and precision of measurement will be the same. It can be achieved by increasing of the subsidiary electrodes number at equality of central angles between them. It is proposed to use the sensor in systems of technical diagnosis at the early stage of the ice appearing.

关键词： Decision support systems Yttrium

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A study on tracking peformance of the pneumatic system with enhanced NPID controller

A study on tracking peformance of the pneumatic system with ...

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Asian control Conference

作者： Sy Najib Sy Salim Aliza Che Amran A. A. M. Faudzi Z. H. Ismail M. F. Rahmat N. H. Sunar Shamsul Anuar Shamsudin Department of Control Instrumentation and Automation Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Department of Control Universiti Teknikal Malaysia Melaka Department of Control and Mechatronic Engineering Universiti Teknologi Malaysia Centre for Artificial Intelligence & Robotics (CAIRO) Universiti Teknologi Malaysia Kuala Lumpur Malaysia Faculty of Mechanical Engineering Universiti Teknikal Malaysia Melaka

An investigation on the performance of enhanced Nonlinear Proportional Integral Derivative (ENPID) controller is presented in this paper. The study is focused on the potential of the controller to perform well in tracking set point of pneumatic actuator system. Two control strategies of the ENPID are considered in this paper, namely Multi-rate NPID (MN-PID) and Self-regulation NPID (SN-PID). These control strategies are tested against NPID and conventional PID controller. Also, the dead-zone compensation (DZC) is applied to the system to overcome the dead-band of the valve. Feed forward path is also included to improve the tracking performance. Two different input signals i.e. sinusoidal and S-curve waveforms are used to evaluate the performance of the techniques. To further examine the controller performance, the frequency and amplitude of the sinusoidal test signal are varied. The effectiveness of the proposed controllers is verified through simulation and experimental studies.

关键词： Pneumatic actuators Valves Mathematical model Trajectory Pistons Fuzzy logic

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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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Preface

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Advances in Intelligent Systems and Computing 2017年 617卷 v-vi页

作者： Vittorini, Pierpaolo Gennari, Rosella Di Masciow, Tania Rodríguez, Sara De la Prieta, Fernando Ramos, Carlos Silveira, Ricardo Azambuja Department of Life Health and Environmental Sciences University of L’Aquila L’Aquila Italy Computer Science Faculty Free University of Bozen-Bolzano Bolzano Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Computer Science and Automation Control University of Salamanca Salamanca Spain Politécnico do Porto GECAD Instituto Superior de Engenharia do Porto Porto Portugal Department of Computer Science and Statistics Federal University of Santa Catarina FlorianópolisSC Brazil

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Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

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Neural Computing and Applications 2015年第6期27卷 1771-1784页

作者： Chen, Jiejie Zeng, Zhigang Jiang, Ping Tang, Huiming School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.

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