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作者： Gromov, Vladislav S. Borisov, Oleg I. Vedyakov, Alexey A. Pyrkin, Anton A. Shavetov, Sergey V. Bobtsov, Alexey A. Salikhov, Vladimir I. Aranovskiy, Stanislav V. Department of Control Systems and Informatics ITMO University Kronverkskiy pr. 49 Saint Petersburg197101 Russia Laboratory "Control of Complex Systems" Institute for Problems of Mechanical Engineering Bolshoy pr. V.O. 61 Saint Petersburg199178 Russia NON-A team INRIA-LNE Pare Scientifique de la Haute Borne 40 avenue Halley Bat.A Park Plaza Villeneuve d'Ascq59650 France

In this paper the problem of adaptive tracking of unknown multisinusoidal signal is addressed. The control input is characterized by the known time delay. It is assumed that all the parameters of the plant are known. In order to demonstrate efficiency of the proposed approach it is implemented to the robotic application. Detailed description of the experimental results are presented in the paper. In addition to the latter comparison between the presented algorithm and the proportional controller is performed. © 2016

关键词： Time delay

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Adaptive fuzzy sliding mode active vibration control for rail vehicle pantograph

Adaptive fuzzy sliding mode active vibration control for rai...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Tsung-Chih Lin Jian-Lin Yeh Chia-Hao Kuo Yu-Chen Lin Valentina E. Balas Department of Electronic Engineering Feng-Chia University Taichung Taiwan Ph.D. Program of Electrical and Communications Engineering Feng-Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9781509006274

In this paper, the uncertain dynamic active pantograph-catenary system which considers the effect of time-varying stiffness resulting from the uneven overhead wire is investigated to improve current collection for light rail vehicle. In order to achieve stable current collection, and hence improve the performance of the overall electricity supply, sliding mode control strategy and adaptive fuzzy sliding mode control scheme are combined. The uplift force which is applied to pantograph frame by actuator is constructed by incorporating fuzzy neural network (FNN) and sliding mode control to attenuate the chattering problem resulting from the sliding mode control. Based on the Lyapunov stability theorem, the active control law and adaptive laws for the free parameters of the FNNs can be derived to effectively suppress vibration of the pantograph system.

关键词： Force Cats Dynamics Wires Sliding mode control Fuzzy neural networks Fuzzy control

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A tactile-based fabric learning and classification architecture

A tactile-based fabric learning and classification architect...

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International Conference on Information and Automation for Sustainability (ICIAfS)

作者： A.A. Khan M. Khosravi S. Denei P. Maiolino W. Kasprzak F. Mastrogiovanni G. Cannata Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy Computing Department University of London London United Kingdom Institute of Control and Computation Eng. Warsaw University of Technology Warszawa Poland

ISBN: (纸本)9781509061334

This paper proposes an architecture for tactile-based fabric learning and classification. The architecture is based on a number of SVM-based learning units, which we call fabric classification cores, specifically trained to discriminate between two fabrics. Each core is based on a specific subset of the fully available set of features, on the basis of their discriminative value, determined using the p-value. During fabric recognition, each core casts a vote. The architecture collects votes and provides an overall classification result. We tested seventeen different fabrics, and the result showed that classification errors are negligible.

关键词： Fabrics Feature extraction Sensor phenomena and characterization Tactile sensors Frequency modulation Frequency-domain analysis

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Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

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Robotics and biomimetics 2017年第1期4卷 3页

作者： K M Goher A M Almeshal S A Agouri A N K Nasir M O Tokhi M R Alenezi T Al Zanki S O Fadlallah Department of Informatics and Enabling Technologies Lincoln University Lincoln New Zealand. Electronics Engineering Technology Department College of Technological Studies Public Authority for Applied Education and Training (PAAET) Adailiya Kuwait. Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK. Mechanical Engineering Department Auckland University of Technology Auckland New Zealand.

This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems.

关键词： Bacteria chemotaxis Hybrid fuzzy logic control PD-like fuzzy logic control Spiral dynamics Two-wheeled inverted pendulum with new configuration

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Long-Term Prediction for T1DM Model During State-Feedback control

Long-Term Prediction for T1DM Model During State-Feedback Co...

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IEEE International Conference on control and Automation

作者： Peter Szalay Zoltan Benyo Levente Kovacs Department of Control Engineering and Information Technology Budapest University of Technology and Economics University Research and Innovation Center and Physiological Controls Group Biomatics Institute John von Neumann Faculty of Informatics Obuda University

ISBN: (纸本)9781509017393

Avoiding low glucose concentration is critically important in type-1 diabetes treatment. Predicting the future plasma glucose levels could ensure the safety of the patient. However, such estimation is no trivial task. The current paper proposes a predictor framework which stems from Unscented Kalman filter and works during closed-loop control, that can predict hazardous glucose levels in advance. Once the blood glucose concentration starts to rise, the predictor activates and estimates future glucose levels up to 3 hours, confirming whether the controller can endanger the patient. The capabilities of the framework is presented through simulations based on the SimEdu validated in-silico simulator.

关键词： Sugar Insulin Observers Blood Kalman filters Noise measurement

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Vehicle license plate image segmentation system using cellular neural network optimized by adaptive fuzzy and Neuro-Fuzzy algorithms

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous engineering 2016年第12期11卷 383-400页

作者： Rahmat, Basuki Joelianto, Endra Ketut Eddy Purnama, I. Purnomo, Mauridhi Hery Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Informatics Engineering Faculty of Industrial Technology Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya60294 Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia

Vehicle License Plate Images Segmentation is a substantial stage for developing an Automatic License Plate Recognition (ALPR) system. In this paper, it is considered an efficient segmentation algorithm for extracting vehicle license plate images using Cellular Neural Networks (CNN). The learning CNN templates values are formulated as an optimization problem to achieve the desired performances which can be found by means of Adaptive Fuzzy (AF) algorithm and Neuro-Fuzzy (NF) algorithm techniques. The main objective of the paper is to compare the performances of standard CNN, Adaptive Fuzzy (AF), and Neuro-Fuzzy (NF) on real data of several vehicle license plate images of standard Indonesia License Plates. The results are then compared with ideal vehicle license plate images. Quantitative analysis between ideal vehicle license plate images and segmented vehicle license plate images is presented in terms of Peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). From the performance analysis, the CNN template optimized by ANFIS algorithm is more recommended than the standard CNN edge detector or the CNN template optimized by Adaptive Fuzzy algorithm in vehicle license plate image segmentation. It is shown from the calculation that PSNR is 80% better than the standard CNN, and the resulted MSE and RMSE are 70% better than the standard CNN. Whereas the CNN template optimized by Adaptive Fuzzy algorithm achieves the PSNR 90% better than the standard CNN, but it yields the MSE and RMSE 40% worse than the standard CNN. © 2016 SERSC.

关键词： Signal to noise ratio

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Simple adaptive control for quadcopters with saturated actuators

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AIP Conference Proceedings 2017年第1期1798卷

作者： Oleg I. Borisov Alexey A. Bobtsov Anton A. Pyrkin Vladislav S. Gromov 1Department of Control Systems and Informatics ITMO University Kronverkskiy pr. 49 St. Petersburg 197101 Russia 2Laboratory “Control of Complex Systems” Institute for Problems of Mechanical Engineering Bolshoy pr. 61 St. Petersburg 199178 Russia

The stabilization problem for quadcopters with saturated actuators is considered. A simple adaptive output control approach is proposed. The control law “consecutive compensator” is augmented with the auxiliary integral loop and anti-windup scheme. Efficiency of the obtained regulator was confirmed by simulation of the quadcopter control problem.

关键词：

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Cross-entropy optimization for neuromodulation

Cross-entropy optimization for neuromodulation

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Harleen K. Brar Yunpeng Pan Babak Mahmoudi Evangelos A. Theodorou Autonomous Control and Decision Systems Laboratory at the Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA

ISBN: (纸本)9781457702198

This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller. With insufficient gains, the internal controller cannot provide enough feedback to the brain dynamics causing an increase in correlation between different brain sites. This increase in synchronization results in the destabilization of the brain dynamics, which is representative of an epileptic seizure. To provide compensation for an insufficient internal controller an external controller is designed using proportional-integral feedback control strategy. A cross-entropy optimization algorithm is applied to the chaotic oscillator network model to learn the optimal feedback gains for the external controller instead of hand-tuning the gains to provide sufficient control to the pathological brain and prevent seizure generation. The correlation between the dynamics of neural activity within different brain sites is calculated for experimental data to show similar dynamics of epileptic neural activity as simulated by the network of chaotic oscillators.

关键词： Oscillators Brain modeling Correlation Couplings Pathology Computational modeling Epilepsy

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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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Fuzzy control of a two-wheeled mobile pendulum system

Fuzzy control of a two-wheeled mobile pendulum system

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Ákos Odry Ervin Burkus István Kecskés János Fodor Péter Odry Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Óbuda University Institute of Intelligent Engineering Systems Budapest Hungary Department of Control Engineering and Information Technology University of Dunaújváros Dunaújváros Hungary

ISBN: (纸本)9781509023813

In this paper, the fuzzy control of a mechatronic system will be studied. The mechatronic system is a special mobile robot (called two-wheeled mobile pendulum) that has two contact points with the supporting surface and its center of mass is located under the wheel axis. Due to the mechanical structure, the inner body (which acts as a pendulum between the wheels) tends to oscillate during the translational motion of the robot, thus the application of complex control solutions is essential in order to stabilize the dynamical system. In the first part of the paper the mechatronic system and the corresponding mathematical model are introduced, while in the second part the design and implementation of different fuzzy controllers are elaborated for the plant. The achieved control performances are analyzed based on simulation and implementation results.

关键词： Mobile robots Oscillators Fuzzy control Wheels Mathematical model Voltage control

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