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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yu-Liang Hsu Yu-Tai Chen Po-Huan Chou Yu-Chen Kou Yi-Chung Chen Heng-Yi Su Department of Automatic Control Engineering Feng Chia University Taichung Taiwan R.O.C. Department of Mechatronics Control Industrial Technology Research Institute Hsinchu Taiwan R.O.C. Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan R.O.C. Department of Electrical Engineering Feng Chia University Taichung Taiwan R.O.C.

ISBN: (纸本)9781509020744

This paper presents an inertial-sensor-based portable instrument and its associated swing motion detection algorithm to detect the following several major stages of a full golf swing motion: address, back swing, top of swing, down swing, impact, follow through, and finish. The portable instrument consisting of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and a Bluetooth wireless transmission module can be mounted on the golf club to measure swing motion signals. A swing motion detection algorithm composed of the procedures of signal collection, signal preprocessing, and swing motion segmentation has been developed for recognizing golf swing motion stages. Experimental results show that the portable instrument with its swing motion detection algorithm is a promising tool for serving as a training assistant tool for golfers.

关键词： Instruments Motion detection Motion measurement Accelerometers Gyroscopes Sports equipment Sensors

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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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Neurofuzzy based temperature prediction of an industrial polymerization reactor in real time 54

Neurofuzzy based temperature prediction of an industrial pol...

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54th IEEE Conference on Decision and control, CDC 2015

作者： Aller, Fernando Blazquez, L. Felipe Miguel, Luis J. Department of Electrical and Automatic Control and Systems Engineering University of León E.II.I.I. Campus de Vegazana S/n León24071 Spain Department of Automatic Control and Systems Engineering University of Valladolid E.I.I. Paseo del Cauce s/n Valladolid47011 Spain

ISBN: (纸本)9781479978861

The ultimate purpose of this work is the real-time control of a semibatch emulsion polymerization reaction. The main variable under control is the temperature inside the reactor. The keypoint to get accurate control is the early detection of temperature deviations. A neurofuzzy network has been trained to predict the temperature from some of the previously calculated variables. This approach aims to extend the prediction horizon with which the temperature is predicted to an order of magnitude of minutes, based on variables which are not measured online but rather estimated using a reduced calorimetric model. This methodology has been applied to data from a real life emulsion polymerization reactor, and in order to allow its implementation in this factory, it has been ensured that all of the operations can be performed in real time by a common Programmable Logic controller (PLC). The resulting set of equations predicts the temperature several minutes in advance with good accuracy. © 2015 IEEE.

关键词： Emulsification

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Bayesian Multiple Target Tracking – Second edition L. D. Stone et al Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2014. 293pp. £119. (20% discount available to RAeS members via *** using RAES2014 promotion code). ISBN 978-1-608-07553-9.

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The Aeronautical Journal 2016年第1209期118卷 1382-1382页

作者： Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffeld

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Are bone marrow obesity and ischemia related to osteoporosis in older adults?

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Osteoporosis and Sarcopenia 2017年第3期3卷 S49-S50页

作者： Wing P. Chan Hou-Ting Yang Yi-Chien Lu Yi-Jui Liu Department of Radiology Wan Fang Hospital Taipei Medical University Taipei 116 Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Program of Electrical and Communications Engineering Feng Chia University Taichung 407 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung 407 Taiwan

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Estimation of fingertip force from surface EMG a multivariate Bayesian mixture of experts approach 8

Estimation of fingertip force from surface EMG a multivariat...

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8th International Conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2015

作者： Baldacchino, Tara Jacobs, William Anderson, Sean R. Worden, Keith Rowson, Jennifer Dynamics Research Group Department of Mechanical Engineering University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9789897580697

Improving the dexterity of active prostheses is a major research area amalgamating machine learning algorithms and biosignals. A recent research niche has emerged from this-providing proportional control to a prosthetic hand by modelling the force applied at the fingertips using surface electromyography (sEMG). The publicly released NinaPro database contains sEMG recording for 6 degree-of-freedom force activations for 40 intact subjects. In this preliminary study the authors successfully perform multivariate force regression using Bayesian mixture of experts (MoE). The accuracy of the model is compared to the benchmark set by the authors of NinaPro;comparable performance is achieved, however in this work a lower dimensional feature extraction representation obtains the best modelling accuracies, hence reducing training time. Inherent to the Bayesian framework is the inclusion of uncertainty in the model structure, providing a natural step in obtaining confidence bounds on the predictions. The MoE model used in this paper provides a powerful method for modelling force regression with application to actively controlling prosthetic and robotic arms for rehabilitation purposes, resulting in highly refined movements.

关键词： Learning algorithms

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Handling risk of uncertainty in model-based production optimization: A robust hierarchical approach 2

Handling risk of uncertainty in model-based production optim...

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2nd IFAC Workshop on automatic control in Offshore Oil and Gas Production, OOGP 2015

作者： Siraj, M. Mohsin Van Den Hof, Paul M. J. Jansen, Jan Dirk Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Geoscience and Engineering Delft University of Technology Netherlands

Model-based economic optimization of oil production suffers from high levels of uncertainty. The limited knowledge of reservoir model parameters and varying economic conditions are the main contributors of uncertainty. The negative impact of these uncertainties on production strategy increases and becomes profound with time. In this work, a multi-objective optimization problem is formulated which considers both economic and model uncertainties and aims to mitigate the negative effects i.e., risk of these uncertainties on the production strategy. The improved robustness is achieved without heavily compromising the primary objective of economic life-cycle performance. An ensemble of varying oil price scenarios and geological model realizations are used to characterize the economic and geological uncertainty space respectively. The primary objective is an average NPV over these ensembles. As the risk of uncertainty increases with time, the secondary objective is aimed at maximizing the speed of oil production to mitigate risk. This multi-objective optimization is implemented separately with both forms of uncertainty in a hierarchical or lexicographic way. © 2015, IF AG.

关键词： Multiobjective optimization

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Robust H∞ control for a class of liner neutral delay systems

Robust H∞ control for a class of liner neutral delay system...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Xinlan Guo Hongxia Zhao Mechanical and Electrical Engineering Department Nanjing Communications Institute of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Huawei Nanjing Institute

This paper design controller for the uncertain neutral delay system. The uncertain neutral delay system includes norm bounded uncertain system parameters and unknown constant delay. The paper uses uncertain neutral systems with structural features, and constructs appropriate Lyapunov function to obtain feedback control law. Compared with the previous methods for solving the Riccati equation, this method overcomes the shortcoming of Riccati equation emphasizing the center solution. Simulation results show that, the conservatism of the controller designed in this paper is reduced, and the controller easy to operate and application.

关键词： Robust H∞ control neutral system uncertain time delay feedback control

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Heterogeneity of time delays determines synchronization of coupled oscillators

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Physical Review E 2016年第1期94卷 012209-012209页

作者： Spase Petkoski Andreas Spiegler Timothée Proix Parham Aram Jean-Jacques Temprado Viktor K. Jirsa Aix-Marseille Université Inserm INS UMR_S 1106 13005 Marseille France Aix-Marseille Université CNRS ISM UMR 7287 13288 Marseille France Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S10 2TN United Kingdom

Network couplings of oscillatory large-scale systems, such as the brain, have a space-time structure composed of connection strengths and signal transmission delays. We provide a theoretical framework, which allows treating the spatial distribution of time delays with regard to synchronization, by decomposing it into patterns and therefore reducing the stability analysis into the tractable problem of a finite set of delay-coupled differential equations. We analyze delay-structured networks of phase oscillators and we find that, depending on the heterogeneity of the delays, the oscillators group in phase-shifted, anti-phase, steady, and non-stationary clusters, and analytically compute their stability boundaries. These results find direct application in the study of brain oscillations.

关键词： Neuroscience Synchronization Coupled oscillators

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Onboard system identification for improved flight control of UAS 8

Onboard system identification for improved flight control of...

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8th IFAC Symposium on Robust control Design, ROCOND 2015

作者： Tantrairatn, S. Veres, S.M. Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

A new technique is proposed to select and estimate the significant aerodynamic parameters of micro unmanned aerial systems from flight data to improve the dynamical qualities of an indirect adaptive flight control system. The aerodynamic variables are estimated in the frequency domain using the angle of attack and sideslip air-flow angles which in turn are estimated using an extended Kalman filter. Parameter estimation and selection procedures of significant aerodynamic parameters are based on linear regression model structures with forward orthogonal least square (OLS) and error reduction ratio (ERR) methods. When combined, the methods can be applied to create an indirect adaptive flight control system. This new approach is verified by comparing the results with those obtained from conventional sensor data, including air flow angle measurements. Performance comparison of the system identification methods show that the proposed technique can obtain the same quality of flight performance as if the airflow measurements were available. The new methods are demonstrated in simulation of a benchmark flight performance experiment on an Aerosonde UAV. © 2015, IFAC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Angle of attack

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