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International Conference on Cybernetics and Informatics

作者： Teofana Puleva Jakub Osusky Department of Systems and Control Faculty of Automatics Technical University of Sofia Institute of Automotive Mechatronics Faculty of Electrical Engineering and Information Technology Slovak University of Technology

ISBN: (纸本)9781509018352

In this paper a two-region power control system of wind turbine generator by aero dynamical torque estimation is presented. It is based on adaptive observer design augmented with an extra integral state. This observer exhibits adaptive features with respect to the external forces applied to the plant (such as aero dynamical torque variation). The simulation results show the control system good efficiency combined with a simple implementation.

关键词： Wind generator Two-regime control system Adaptive observer Aerodynamical torque estimation

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Partial labeled gastric tumor segmentation via patch⇂based reiterative learning

arXiv

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arXiv 2017年

作者： Nan, Yang Coppola, Gianmarc Liang, Qiaokang Zou, Kunglin Sun, Wei Zhang, Dan Wang, Yaonan Yu, Guanzhen College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Vision Perception and Control Hunan University Changsha Hunan410082 China Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Hunan418000 China Faculty of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 7K4 Canada Department of Mechanical Engineering York University TorontoONM3J 1P3 Canada Department of Oncology Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai201203 China

Gastric cancer is the second leading cause of cancer‐related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent. This assignment has high clinical relevance and would generally require vast microscopic assessment by pathologists. Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation. This requires plenty of labor costs, for the ground truth must be annotated meticulously by pathologists. In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre‐trained or further manual annotation. We eliminate the boundary error of patch‐based model through our overlapped region forecast algorithm. Through these advisable methods, a mean intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09% on the partial labeled dataset was achieved, which made us win the 2017 China Big Data & Artificial Intelligence Innovation and Entrepreneurship Competitions1. Copyright © 2017, The Authors. All rights reserved.

关键词： Diseases

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Robust Multimodel control Applied on Hydraulic Turbine Model

Robust Multimodel Control Applied on Hydraulic Turbine Model

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International Conference on Cybernetics and Informatics

作者： Jakub Osusky Maria Hypiusova Teofana Puleva Institute of Automotive Mechatronics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Department of Systems and Control Faculty of Automatics Technical University oof Sofia

ISBN: (纸本)9781509018352

The paper deals with the departition of nonlinear system into several linear models and application of multimodel control, robust control and their combination on hydraulic turbine model. controller design will be done in the frequency domain using the Edge Theorem and the Neymark D-partition method using the affine model. The proposed method guarantees stability and performance in form of the required degree of stability. Hydraulic turbine model is analyzed as system with unstructured uncertainty and robust stability condition is included in controller design. Multimodel and robust control approaches are presented in details, compared and used for derivation new approaches based on Edge Theorem combining advantages of both ones.

关键词： Multimodel control Robust control Hybrid control Hydraulic turbine

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Gain-Scheduled MPC Design for Nonlinear Systems with Input Constraints

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IFAC-PapersOnLine 2015年第11 11期48卷 912-917页

作者： Ilka, Adrian Veselý, Vojtech Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Ilkovicova 3 Bratislava812 19 Slovakia

A novel methodology is proposed for discrete model predictive gain-scheduled controller design for nonlinear systems with input(hard)/output(soft) constraints for finite and infinite prediction horizons. The proposed design procedure is based on the linear parametervarying (LPV) paradigm, afine parameter-dependent quadratic stability and on the notion of the parameter-varying guaranteed cost. The obtained design procedure is in the form of BMI. Numerical examples show the benefit of the proposed approach. © 2015

关键词： Design controllers Cost functions Gain control Lyapunov functions Nonlinear systems Predictive control systems Gain scheduled controllers Linear parameter varying LPV systems Parameter dependent Lyapunov function Parameter dependents Prediction horizon Predictive controller Quadratic stability

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Exploiting Ankle Torque for Orbital Stabilization in Biped Robots;A Hybrid Zero Dynamics Approach

Exploiting Ankle Torque for Orbital Stabilization in Biped R...

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IEEE-RAS International Conference on Humanoid Robots

作者： Hamid Sadeghian Christian Ott Gordon Cheng Engineering Department University of Isfahan Isfahan Iran Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Institute for Cognitive Systems Faculty of Electrical Engineering and Information Technology Technical University of Munich (TUM)-Germany

ISBN: (纸本)9781509047192

This paper investigates the rule of ankle torque in stabilization of periodic motions in planar biped with feet. In order to illustrate the main concepts, the walking is considered to be consist of two phases;A fully actuated phase, where the stance foot is flat on the ground, and an instantaneous double support phase. It is shown how the ankle torque can be used effectively within hybrid zero dynamics framework to stabilize the system around a target periodic orbit. This control torque which is designed as an internal command to the swing phase zero dynamics is further exploited to switch among different orbits in one transition step. For the former goal, the Lagrangian property of the swing phase zero dynamics is employed and the ankle torque is applied to regulate the corresponding energy level of the target orbit. For the switching problem, this command is used to steer the trajectory of the system within connecting zero dynamics manifold. The proposed methods are verified through several simulations on a seven-link planar biped robot.

关键词： Torque Foot Orbits Legged locomotion Manifolds Dynamics Torque Orbits Biped locomotion Foot Foot Eye Sockets Manifolds Foot British dynamics biped robot Ankle Zero

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Hand gesture-based interface with multichannel sEMG band enabling unknown gesture discrimination 10

Hand gesture-based interface with multichannel sEMG band ena...

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

作者： Tomczyński, Jakub Kaczmarek, Piotr Mańkowski, Tomasz Institute of Control and Information Engineering Faculty of Electrical Engineering Poznan University of Technology Poznań Poland

ISBN: (纸本)9781479970438

A forearm band consisting of 7 EMG sensors was developed. The band is dedicated to serve as a human-machine interface and its applicability as a gesture-based interface was presented. The gesture recognition was performed by ANN with softmax output function. The classifier uses output entropy function to discriminate between known command gestures and unknown gestures. 15 features of low computational complexity were selected. The preliminary test was performed for 4 healthy volunteers. Continuous time series were used, including unknown gestures and transitions. Special attention was paid to analyze system properties of detecting and rejecting unknown gestures (not commands movements), even if they were not included in training set. Obtained preliminary results show the sensitivity for command gestures (including transition phases) of 97% and fall out of 1.9%. In case of the system trained to reject unknown gestures the sensitivity and fall out were 82% and 9%, respectively. The calculated unknown gestures rejection rate was at 96%. It was shown that in most cases the interface is robust to false positive detection of command gestures while performing other gestures, even if the same muscles groups were recruited for movement. © 2015 IEEE.

关键词： Continuous time systems

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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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A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

A motor imagery based brain-computer interface system via sw...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Shang-Lin Wu Yu-Ting Liu Kuang-Pen Chou Yang-Yin Lin Jie Lu Guangquan Zhang Chun-Hsiang Chuang Wen-Chieh Lin Chin-Teng Lin Brain Research Center Institute of Electrical Control Engineering Institute of Computer Science and Engineering Electronic Systems Research Division National Chung-Shan Institute of Science and Technology (NCSIST) Taoyuan Taiwan Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Department of Computer Science National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781509006274

A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.

关键词： Electroencephalography Feature extraction Wireless sensor networks Wireless communication Electrodes Particle swarm optimization Microcontrollers

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Gain-Scheduled MPC Design for Nonlinear Systems with Input Constraints

Gain-Scheduled MPC Design for Nonlinear Systems with Input C...

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关键词： Cost functions

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Robust gain-scheduled controller design for T1DM individualised model 8

Robust gain-scheduled controller design for T1DM individuali...

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8th IFAC Symposium on Robust control Design, ROCOND 2015

作者： Ilka, Adrian Ludwig, Tomáš Ottinger, Ivan Tárník, Marián Miklovičová, Eva Veselý, Vojtech Murgaš, Ján Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Ilkovičova 3 Bratislava812 19 Slovakia

Novel original robust gain-scheduled controller design for individualized type 1 diabetes mellitus (T1DM) subject model is presented in this paper. For controller design an LPV model is created from insulin-glucose subsystem based on Bergman's minimal model with subcutaneous insulin absorption and absorption of digested carbohydrates. Identification was performed on pharmacokinetics and pharmacodynamics characteristics of administered insulin and data collected from continuous glucose monitoring (CGM) system. The controller design approach guarantees the closed-loop stability and cost for all scheduled parameter changes. Simulation results show the benefits of the proposed approach. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Insulin

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