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38th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2015

作者： Novak, Mateja Šunde, Viktor Jakopović, Željko Faculty of Electrical Engineering and Computing Department of Electrical Machines Drives and Automation Zagreb Croatia

ISBN: (纸本)9789532330854

Photovoltaic modules as direct converters of solar energy into electrical energy are typically connected to the power grid trough standard two level converters. A photovoltaics system can be connected to the power grid if it can satisfy the IEEE standardized conditions set by the power grid operator. The conditions include limitations of THD factor and current harmonics. Multilevel converters which have a lower THD factor than two level converters can satisfy these conditions and in the same time work at higher switching frequencies lowering the losses and the size of the output filter. The model implemented in Simulink® MATLAB consists of a photovoltaic array, boost DC-DC converter with MPPT control and three level neutral point clamped converter connected to the power grid. The simulation results are presented, analyzed and compared to the standard model with a two level converter to validate the benefits of the usage of multilevel converters for grid connected photovoltaic systems. © 2015 MIPRO.

关键词： Solar energy

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Non-GPS Positioning Sensor Network in Social Manufacturing

Non-GPS Positioning Sensor Network in Social Manufacturing

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Akatsuki Ryu Arja Mehtala Timo Nyberg Mark Nelson Gang Xiong Department of Industrial Engineering and Management Aalto University Nokia Technologies Cloud Computing Center Chinese Academy of sciences Peace Innovation Lab Stanford University Institute of Automation Chinese Academy of sciences

The Global Positioning System(GPS) technology has been widely used in many applications. However, GPS operation is very limited indoors. While people are spending most of the time indoor. Different solutions are used in indoor positioning and location based service. Indoor positioning based systems are increasingly used in manufacturing. Four instance indoor positioning systems are used for the assets tracking and human trajectory. Tracking mass amount of people Will generate huge big data, manufacturing companies as well as individuals can take advantage of this traffic flow data to pursue environmental, economic and social sustainable manufacturing *** this paper, we describe one method of tracking massive amount of people and provide them and visual tracking information by using one of the indoor positioning Technologies HAIP provided by Nokia. We present empirical results of experiments of using the HAIP system.

关键词： Indoor sensing technology location based services big data

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Minimum loss control of interior permanent magnet traction motor

Minimum loss control of interior permanent magnet traction m...

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IEEE International Electric Machines and Drives Conference, IEMDC 2015

作者： Jercic, Tino Zarko, Damir Matusko, Jadranko Martinovic, Marijan University of Zagreb Faculty of Electrical Engineering and Computing Department of Electrical Machines Drives and Automation Zagreb Croatia

ISBN: (纸本)9781479979417

This paper presents a new formulation for minimization of copper and iron losses in interior permanent magnet for the purpose of minimum loss control. Motor losses are formulated based on electromagnetic torque to total torque ratio resulting in simplified expression of minimal loss condition which can be easily solved online. The flux-weakening method which adjusts electromagnetic to total torque ratio in order to achieve field weakening operation is also proposed. © 2015 IEEE.

关键词： electric vehicles energy efficiency function approximation minimization Newton method optimization permanent magnet machines temperature dependence variable speed drives

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Effects of dead time and semiconductor device voltage drops of output voltage of multilevel converters 38

Effects of dead time and semiconductor device voltage drops ...

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38th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2015

作者： Mrčela, Ivan Šunde, Viktor Kovačić, Marinko University of Zagreb Faculty of Electrical Engineering and Computing Department of Electrical Machines Drives and Automation Zagreb Croatia

ISBN: (纸本)9789532330854

This paper presents the results of a study of the effect of dead time and finite semiconductor device voltage drops on the output voltage of three-level neutral point clamped power converters. Results showed that finite voltage drops and dead time caused a DC voltage shift, and lowered the base harmonic amplitude of output voltage. These effects were analysed and the results confirmed via simulation models. Voltage drops effect was confirmed using Ansys Simplorer, while the dead time effect was confirmed using Matlab. The results showed that the output voltage of the three-level neutral point clamped converters was dependant on the listed parameters. A method is proposed to suppress the negative effects these parameters have on converter voltage. © 2015 MIPRO.

关键词： Power converters

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A multi-label classification approach for the detection of broken bars and mixed eccentricity faults using the start-up transient

A multi-label classification approach for the detection of b...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： George Georgoulas Vicente Climente-Alarcon Jose A. Antonino-Daviu Chrysostomos D. Stylios Antero Arkkio George Nikolakopoulos Control Engineering Group University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Aalto Finland Instituto Tecnologico de la Energia Universitat Politècnica de València Valencia Spain Department of Computer Engineering Laboratory of Knowledge and Intelligent Computing Kostakioi Greece

ISBN: (纸本)9781509028719

In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

关键词： Bars Time-frequency analysis Induction motors Transient analysis Principal component analysis Fault diagnosis Classification algorithms

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A survey on the feasibility of surface EMG in facial pacing

A survey on the feasibility of surface EMG in facial pacing

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

作者： Ville Rantanen Mirja Ilves Antti Vehkaoja Anton Kontunen Jani Lylykangas Eeva Mäkelä Markus Rautiainen Veikko Surakka Jukka Lekkala Department of Automation Science and Engineering Tampere University of Technology Tampere Finland Research Group for Emotions Sociality and Computing University of Tampere Tampere Finland Department of Clinical Neurophysiology Medical Imaging Centre Tampere Finland Department of Otorhinolaryngology University of Tampere Tampere Finland

A survey on the feasibility of surface electromyography (EMG) measurements in facial pacing is presented. Pacing for unilateral facial paralysis consists of the measurement of activity from the healthy side of the face and functional electrical stimulation to reanimate the paralyzed one. The goal of this study is to evaluate the feasibility of surface EMG as a measurement method to detect muscle activations and to determine their intensities. Prior work is discussed, and results from experiments where 12 participants carried out a set of facial movements are presented. EMG was registered from zygomaticus major (smile), orbicularis oris (lip pucker), orbicularis oculi (eye blink), corrugator supercilii (frown), and masseter (chew). Most important facial functions that are limited due to the paralysis are blinking, smiling, and puckering. With majority of the participants, crosstalk between the measured EMG channels was found to be acceptably small to be able to pace smiling and puckering based on detecting their contraction intensities from the healthy side. However, pacing blinking based on orbicularis oculi EMG measurement does not seem possible due to crosstalk from other muscles, but the electro-oculographic (EOG) signals that couple to the same measurement channel could help to detect eye blinks and trigger stimuli. Futhermore, masseter greatly disturbs EMG measurement of most facial muscles, which needs to be addressed in the pacing system to avoid falsely interpreting its activity as the activity of another muscle.

关键词： Electromyography Muscles Atmospheric measurements Particle measurements Crosstalk Electrodes Extraterrestrial measurements

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Unsupervised classification of data streams based on typicality and eccentricity data analytics

Unsupervised classification of data streams based on typical...

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2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

作者： Costa, Bruno Sielly Jales Bezerra, Clauber Gomes Guedes, Luiz Affonso Angelov, Plamen Parvanov Federal Institute of Rio Grande do Norte - IFRN Campus Natal - Zona Norte Natal Brazil Federal Institute of Rio Grande do Norte - IFRN Campus EaD Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Data Science Group School of Computing and Communications Lancaster University LancasterLA14WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509006250

In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns "on-The-fly". It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtime constraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handle concept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from the data. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilot plant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to different problems and with much higher dimensional inputs. The results obtained from the real data are very significant. © 2016 IEEE.

关键词： Classification (of information)

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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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 new evolving clustering algorithm for online data streams 10

A new evolving clustering algorithm for online data streams

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10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016

作者： Bezerra, Clauber Gomes Costa, Bruno Sielly Jales Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LancasterLA1 4WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509025831

In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular unities called data clouds, which are structures with no pre-defined shape or set boundaries. TEDA-Cloud is a fully autonomous and self-evolving algorithm that can be used for data clustering of online data streams and applications that require real-time response. Since it is fully autonomous, TEDA-Cloud is able to "start from scratch" (from an empty knowledge basis), create, update and merge data clouds, in a fully autonomous manner, without requiring any user-defined parameters (e.g. number of clusters, size, radius) or previous training. Moreover, TEDA-Cloud, unlike most of the traditional statistical approaches, does not rely on a specific data distribution or on the assumption of independence of data samples. The results, obtained from multiple data sets that are very well known in literature, are very encouraging. ©2016 IEEE.

关键词： Clustering algorithms

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