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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Andres O. Salazar Diego A. M. Fonsêca André L. Martelli Francisco Fontes Filipe de Oliveira Quintaes Department of Computing and Automation Engineering Federal University of Rio Grande do Norte Natal RN Brazil Universidade Federal do Rio Grande do Norte Natal RN BR Informatics Department Federal Institute of Rio Grande do Norte Natal RN Brazil

The purpose of this paper is to presents a design of a magnetic sensor capable of detecting contamination of insulating oil used in the artificial lift method of oil-type electrical submersible pump (ESP). The sensor generates an alarm signal at the moment when the contamination oil is present. This signal is transmitted to the surface by communication system which uses the tree-phase motor power. This diagnostic information enables the implementation of predictive maintenance (condition monitoring) functions. For plant operators, this therefore makes it possible to detect faults in advance and implement measures to improve operational reliability and keep the downtimes of plants to a minimum. The prototype was designed to work in harsh conditions to reach a depth of 2000 m and temperatures up to 200 C. It used simulator software to define the mechanical and electromagnetic variables. Results of field experiments were performed to validate the prototype. The final results performed in an ESP system with a 62-HP motor showed a good reliability and fast response of the prototype.

关键词： Magnetic flux Magnetic cores Induction motors Contamination Coils Oil insulation Ferrites

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Corrections to “Exploiting Web Images for Semantic Video Indexing Via Robust Sample-Specific Loss” [Oct 14 1677-1689]

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IEEE Transactions on Multimedia 2015年第2期17卷 256-256页

作者： Yang Yang Zheng-Jun Zha Yue Gao Xiaofeng Zhu Tat-Seng Chua School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Institute of Intelligent Machines Chinese Academy of Sciences Hefei China Department of Automation Tsinghua University China Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States School of Computing National University of Singapore Singapore

In the above paper [ibid, vol. 16, no. 6, p. 1679, Oct. 2014], the sentence "Yang et al. [?] integrated semisupervised learning and transfer learning techniques to exploit manually-labeled images for video tagging" should have appeared as "Yang et al. [34] integrated semisupervised learning and transfer learning techniques to exploit manually-labeled images for video tagging."

关键词： Iterative methods Indexing Video signal processing Learning (artificial intelligence) Image classification Tagging

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Operating limits of underexcited synchronous generator

Operating limits of underexcited synchronous generator

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2013 4th International Conference on Power engineering, Energy and Electrical Drives, POWERENG 2013

作者： Maljkovic, Zlatko Gasparac, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Department of Electrical Machines Drives and Automation Zagreb Croatia

ISBN: (纸本)9781467363921

Standard power chart of synchronous generator has its limit defined by practical stability limit and by field current minimum in the underexcitation quadrant. However, during the time, some generators have shown certain sensitivity during the permanent underexcited operation even in the stabile operating area due to local heating of magnetic conductive parts in the end region of the machine. Underexcited operation is usually required in case of raised network voltage while the active power demands are lowered. Precise knowledge of limits of underexcited operation ability enables precise planning of system reactive power trading. Some world experiences and axial magnetic field measurements in the end region of synchronous hydro and turbine generator are presented. Based on mentioned measurements, it is possible to define real power chart which almost always deviates in capacitive operating area compared to manufacturer's diagram. © 2013 IEEE.

关键词： Turbogenerators

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Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

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Frontiers of Computer Science 2013年第6期7卷 852-863页

作者： Zengchang QIN Tao WAN Intelligent Computing and Machine Learning Lab School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China School of Biological Science and Medical Engineering Beihang University Beijing 100191 China Department of Biomedical Engineering Case Western Reserve University Cleveland OH 44106 USA

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees （PETs） give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree （LDT） is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.

关键词： fuzzy labels label semantics random set probability estimation tree mass assignment linguistic decision tree naive Bayes

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Selecting redundant nodes when addressing availability in wireless visual sensor networks

Selecting redundant nodes when addressing availability in wi...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Daniel G. Costa Ivanovitch Silva Luiz Affonso Guedes Paulo Portugal Francisco Vasques Department of Technology State University of Feira de Santana Feira de Santana Brazil Department of Computing and Automation Federal University of Rio Grande do Norte Natal Brazil Universidade Federal do Rio Grande do Norte Natal RN BR Faculty of Engineering University of Porto Porto Portugal

As Wireless Sensor Networks have been employed to support critical monitoring applications, network availability has become a major design concern. In these networks, redundancy can be exploited to enhance the attainable availability level, where redundant sensors can replace faulty nodes. When camera-enabled sensors are deployed to retrieve visual information, the perception of redundancy changes considerably, since the redundancy of visual sensors depends on the monitoring requirements of the applications. In such context, characteristics as deployment density, viewing angle and sensing range are relevant when planning wireless sensor network applications, directly impacting in the number of redundant nodes. We propose an algorithm to select redundant nodes in Wireless Visual Sensor Networks, according to the application requirements. Moreover, we discuss how parameters of the deployed network can influence on the number of redundant nodes.

关键词： Visualization Redundancy Monitoring Sensors Availability Wireless sensor networks Wireless communication

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Parallel architecture and optimization for discrete-event simulation of spike neural networks

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Science China(Technological Sciences) 2013年第2期56卷 509-517页

作者： TANG YuHua ZHANG BaiDa WU JunJie HU TianJiang ZHOU Jing LIU FuDong Department of Computer Science and Technology School of ComputerNational University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology College of Mechatronic Engineering and Automation National University of Defense Technology

Spike neural networks are inspired by animal brains,and outperform traditional neural networks on complicated ***,spike neural networks are usually used on a large scale,and they cannot be computed on commercial,off-the-shelf computers.A parallel architecture is proposed and developed for discrete-event simulations of spike neural ***,mechanisms for both parallelism degree estimation and dynamic load balance are emphasized with theoretical and computational *** results show the effectiveness of the proposed parallelized spike neural network system and its corresponding support components.

关键词： spike neural network discrete event simulation intelligent parallelization framework

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DSP based Fuzzy controller applied to a DC-DC Boost converter

DSP based Fuzzy controller applied to a DC-DC Boost converte...

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2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013

作者： De Carvalho Neto, João T. Salazar, Andrés O. De Araújo, Fábio M. U. De O. Cavalcanti, Anderson L. Department of Computing Engineering and Automation Federal University of Rio Grande Do Norte Natal/ Rio Grande do Norte Brazil

ISBN: (纸本)9781467345439

This paper presents a Fuzzy controller applied to a DC-DC Boost converter in order to minimize the system's power loss effects by adjusting the duty cycle of the converter. The variation in the input voltage and the losses in the electronic components of the converter (load variations) are the main causes of these power losses. The main objective of the controller is to maintain a constant voltage at the load regardless of these losses. The Fuzzy controller was embedded on a Digital Signal Processor in order to have a great reliability and a faster system response. The results show that the Fuzzy control technique can be utilized to minimize the effects of the system's uncertainties and disturbances while keeping the system's output response in its operating point. © 2013 IEEE.

关键词： Fuzzy control

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The first ICB* competition on iris recognition

The first ICB* competition on iris recognition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Man Zhang Jing Liu Zhenan Sun Tieniu Tan Wu Su Fernando Alonso-Fernandez Valérian Némesin Nadia Othman Koichi Noda Peihua Li Edmundo Hoyle Akanksha Joshi Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences (CN) Department of Automation University of Science and Technology of China (CN) Zhuhai YiSheng Electronics Technology Co Ltd (CN) Halmstad University (SE) Centrale Marseille Institut Fresnel (UMR 7249) (FR) CNRS Télécom SudParis Nihon System Laboratory. Ltd (JP) School of Information and Communication Engineering Dalian University of Technology (CN) University Federal of Rio de Janeiro (BR) Centre for Development of Advanced Computing (IN)

Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition, we organized the first ICB* Competition on Iris Recognition in 2013 (or ICIR2013 shortly). In this competition, 8 participants from 6 countries submitted 13 algorithms totally. All the algorithms were trained on a public database (e.g. CASIA-Iris-Thousand [3]) and evaluated on an unpublished database. The testing results in terms of False Non-match Rate (FNMR) when False Match Rate (FMR) is 0.0001 are taken to rank the submitted algorithms.

关键词： Iris recognition Databases Image segmentation Magnetic resonance Testing Image edge detection Noise

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Containment for double-integrator multi-agent systems in heterogeneous networks

Containment for double-integrator multi-agent systems in het...

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IEEE Annual Conference on Decision and Control

作者： Jiahu Qin Wei Xing Zheng Huijun Gao Qichao Ma University of Science and Technology of China Hefei China School of Computing Engineering and Mathematics University of Western Sydney Penrith NSW 2751 Australia Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150080 Heilongjiang Province China Department of Automation University of Science and Technology of China Hefei China

ISBN: (纸本)9781467360890

This paper is concerned with the containment control for a group of double-integrator agents with multiple dynamic leaders communicating over heterogeneous networks, i.e., the position and velocity network topologies are modeled by different graphs. Different containment control algorithms are proposed and analyzed for the two cases that the leaders move with the same constant velocity and the same time-varying velocity, respectively. For the former case the sufficient conditions provided for guaranteeing the containment control are structural and thus easy to verify. Though the conditions proposed for the latter case that leaders move with time-varying velocity are algebraic, such algebraic conditions can be satisfied if the position and velocity network topologies among the follower agents are undirected.

关键词： Network topology Eigenvalues and eigenfunctions Educational institutions Symmetric matrices Heuristic algorithms Algorithm design and analysis Vectors

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A heuristic fuzzy algorithm bio-inspired by Evolution Strategies for energy forecasting problems

A heuristic fuzzy algorithm bio-inspired by Evolution Strate...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Vitor N. Coelho Frederico G. Guimarães Agnaldo J. R. Reis Igor M. Coelho Bruno N. Coelho Marcone J. F. Souza Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Departament of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Control Engineering and Automation Universidade Federal de Ouro Preto Ouro Preto MG Brazil Institute of Computing Universidade Federal Fluminense Niteri RJ Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto MG Brazil

Improving the use of energy resources has been a great challenge in the last years. A new complex scenario involving a decentralized bidirectional communication between energy suppliers, distribution system and consumption is nowadays becoming reality. Sometimes cited as the largest and most complex machine ever built, Electric Grids (EG) are been transformed into Smart Grids (SG). Hence, the load forecasting problem has become more difficulty and more autonomous load predictors are needed in this new conjecture. In this paper a novel method, so-called MSES, bio-inspired by Evolution Strategies (ES) combined with Multi-Start (MS) procedure is described. This procedure is mainly based on a self-adaptive algorithm to calibrate the parameters of the fuzzy rules. MSES was implemented in C++ via OptFrame framework. Our main goal is to evaluate the performance of this algorithm in a grid environment. Real data from an electric utility have been used in order to test the proposed methodology. The obtained results are fully described and analyzed.

关键词： Forecasting Vectors Prediction algorithms Predictive models Training Standards Adaptation models

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