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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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Social Manufacturing Development with Sino-Finnish Innovation Cooperation

Social Manufacturing Development with Sino-Finnish Innovatio...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Jian Liu Gang Xiong Xiuqin Shang Timo R.Nyberg Yiming Wang Tongkai Ji The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Information Science and Technology Beijing University of Chemical Technology Cloud Computing Center Chinese Academy of Sciences Department of Industrial Engineering and Management School of ScienceAalto University Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences

Innovation is changing the world every day by offering new technical solutions for the society, and the technology development powered by innovation has been changing the operation and structure of traditional manufacturing. Social manufacturing is the new trend of manufacturing development, especially in the field of apparel industry. Social manufacturing can create opportunities for customers to participate in the produce process of tailoring, and to make more personalizing clothes, which will also benefit for the related companies' brand making process. With the assistance of internet technology and industry globalization, we can establish several modes about the trend of social manufacturing and new related technology, social manufacturing can also prompt the international division of labor by reducing the producing costs and enlarging the market capacity, we will analyze the advance of Sino-Finland cooperation in the field of innovation and apparel industry, the international cooperation in manufacturing industry will benefit both customers and industry revolution with the assistance of social manufacturing.

关键词： social manufacturing innovation apparel industry international cooperation

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Disturbance observer for estimation of oxygen uptake rate in an activated sludge reactor

Disturbance observer for estimation of oxygen uptake rate in...

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IEEE Instrumentation and Measurement Technology Conference

作者： Francisco Jadilson dos Santos Silva Euler C. T. de Macêdo Sebastian Y. C. Catunda Carlos E. T. Dorea Adrianus C. Van Haandel Federal Institute of Maranhão Alcântara Brazil Department of Electrical Engineering Federal University of Paraiba João Pessoa Brazil Department of Computing Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil Natural Resources Technology Center Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9781467392211

The oxygen uptake rate (OUR) is an important quantity to the aerobic activated sludge processes. It can be used to model and to optimize the system, for monitoring the process efficiency, and as a toxicity indicator in wastewater treatment plants, among other applications. The lack of online and reliable sensors to measure this significant quantity has motivated the development of software sensors for its estimation. This paper shows the design of a discrete-time disturbance observer to be used as a software sensor to estimate the oxygen uptake rate in an activated sludge reactor. A linear model of the dissolved oxygen (DO) process is used to design a compensator to maintain the DO concentration around a reference operating point. Numerical simulations are carried out and the obtained results demonstrate the applicability of the proposed method.

关键词： Inductors Sensors Biological system modeling Observers Sludge treatment Pulse width modulation

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Corrigendum to “Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

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Robotics and Autonomous Systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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Non-GPS positioning sensor network in social manufacturing

Non-GPS positioning sensor network in social manufacturing

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

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

ISBN: (纸本)9781509029280

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 strategies. In 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： Databases Heating Trajectory Receivers Global Positioning System Manufacturing Servers

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A compressive sensing based method for cross-terms suppression in the time-frequency plane 15

A compressive sensing based method for cross-terms suppressi...

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15th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2015

作者： Volaric, Ivan Sucic, Victor Car, Zlatan Department of Automation and Electronics Faculty of Engineering University of Rijeka Rijeka51000 Croatia Center for Advanced Computing and Modeling University of Rijeka Rijeka51000 Croatia

ISBN: (纸本)9781467379830

Signals with time-varying frequency content are generally best represented in the time frequency (TF) domain, with the components instantaneous frequency laws being their key nonstationary features. There are a number of methods for TF distribution calculation (TFD), however, most of them add unwanted artifacts, making the TFD interpretation more difficult. In this paper, we use a compressive sensing approach to suppress or completely eliminate those artifacts. Furthermore, a basis pursuit algorithm is employed for signal reconstruction in order to reduce resolution loss, which is a mayor obstacle of existing TFD design methods. © 2015 IEEE.

关键词： Signal reconstruction

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System analysis and isothermal separate effect experiments of the accident behavior in PWR spent fuel storage pools 16

System analysis and isothermal separate effect experiments o...

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16th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2015

作者： Chahi, H. Kästner, W. Alt, S. Department of Nuclear Engineering/Soft Computing Institute of Process Technology Process automation and measuring technology University of Applied Sciences Zittau/Gorlitz Zittau Germany

ISBN: (纸本)9781510811843

This research project supported by the German Federal Ministry of Education and Research named SINABEL (Safety of spent fuel pools: Experimental analysis, modeling and validation of system and CFD codes) delivers with a combination of experiments and simulations, knowledge's of the heat transfer processes for the case of a partially or completely evaporated spent fuel storage pool in light water reactor nuclear power plants both within the fuel rod bundle of a fuel assembly (FA) and in the spaces between the FA in order to be able to predict via the modeling and simulation the development of the axial and radial temperature profiles of rods by different accident scenarios. SINABEL is a joint research project which three research institutions (TU-Dresden, HZDR and HSZG) work together. One of the main focus in this project is the investigation of the flow behavior by separate effect experiments in a single 16x16 PWR-FA-dummy (pressurized water reactor fuel assembly) to study the vertical density-driven exchange movements of gases within and above the FA-bundle. By means of these small scale experiments the flow behavior will be investigated using optical observations facilities for air, replacement atmospheres for superheated steam (e.g. through model fluids). The operational capability is also to be tested as part of this work. The measuring results in this project include the dynamic behavior of gas concentrations and local velocity vectors, turbulent structures of oppositely directed gas flows. In this paper the experimental design and results of preliminary isothermal experiments at a shortened PWR-FA-dummy are presented which are based on acquired knowledge's from experiments at a heated BWR-FA-dummy-rig and descriptions of fluid dynamic process in FA. The paper illustrates for example the Re-number dependent pressure drop in FA-dummy and the velocity distribution, as well as determines which model gas is well suited to model the overheated steam by application o

关键词： Lakes

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