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A hierarchical Approach for the Recognition of Induction Machine Failures

JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2018年第1期29卷 44-61页

作者： Maitre, Julien Bouzouane, Abdenour Gaboury, Sebastien Univ Quebec Chicoutimi LIARA 555 Blvd Univ Chicoutimi PQ G7H 2B1 Canada

Due to its sturdiness, low cost and ease of implementation, the induction machine is one of the most common electrical motors used in industry. However, this machine still concedes failures leading to unplanned shutdowns, sources of significant financial losses. For this reason, induction machine failure diagnosis has become an ordinary task, where some ameliorations have to be achieved in order to improve the efficiency of maintenance programs by minimizing the number and the time period of unexpected shutdowns. In this paper, we address the recognition of inter-turn short circuit (ITSC) in the stator windings and broken rotor bars (BRBs) in the three-phase squirrel cage induction machine by proposing a new approach. This approach relies on the three-phase current analysis method to extract features and a new hierarchical recognition algorithm based on an ensemble of three different classifiers. In addition, the present work studies the failures recognition of the induction machine operating with a torque and speed control. The proposed approach is a major advanced for the induction machine diagnosis research because it recognizes correctly (detects, localizes and estimates the degree of severity) ITSC and (detects and estimates) BRB faults with an accuracy of 93.36% and with a great robustness compared to a classical machine learning algorithm.

关键词： Induction machine failures Faults diagnosis Inter-turn short circuit Broken rotor bar Classification hierarchical algorithm

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Hidden Markov Models with mixtures as emission distributions

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STATISTICS AND COMPUTING 2014年第4期24卷 493-504页

作者： Volant, Stevenn Berard, Caroline Martin-Magniette, Marie-Laure Robin, Stephane INRA UMR MIA 518 F-75231 Paris France AgroParisTech UMR MIA F-75231 Paris France INRA URGV UMR1165 F-91057 Evry France UEVE UMR URGV F-91057 Evry France CNRS UMR URGV ERL8196 F-91057 Evry France

In unsupervised classification, Hidden Markov Models (HMM) are used to account for a neighborhood structure between observations. The emission distributions are often supposed to belong to some parametric family. In this paper, a semiparametric model where the emission distributions are a mixture of parametric distributions is proposed to get a higher flexibility. We show that the standard EM algorithm can be adapted to infer the model parameters. For the initialization step, starting from a large number of components, a hierarchical method to combine them into the hidden states is proposed. Three likelihood-based criteria to select the components to be combined are discussed. To estimate the number of hidden states, BIC-like criteria are derived. A simulation study is carried out both to determine the best combination between the combining criteria and the model selection criteria and to evaluate the accuracy of classification. The proposed method is also illustrated using a biological dataset from the model plant Arabidopsis thaliana. A R package HMMmix is freely available on the CRAN.

关键词： Hidden Markov models Model-based clustering Mixture model hierarchical algorithm

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A k-populations algorithm for clustering categorical data

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PATTERN RECOGNITION 2005年第7期38卷 1131-1134页

作者： Kim, DW Lee, K Lee, D Lee, KH Korea Adv Inst Sci & Technol Dept BioSyst Taejon 305701 South Korea Korea Adv Inst Sci & Technol Adv Informat Technol Res Ctr Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea

In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： clustering categorical data hierarchical algorithm k-modes algorithm fuzzy k-modes algorithm

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Pre-registration of latent fingerprints based on orientation field

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IET BIOMETRICS 2015年第2期4卷 42-52页

作者： Krish, Ram Prasad Fierrez, Julian Ramos, Daniel Ortega-Garcia, Javier Bigun, Josef Univ Autonoma Madrid Escuela Politecn Super Biometr Recognit Grp ATVS E-28049 Madrid Spain Halmstad Univ Intelligent Syst Lab SE-30118 Halmstad Sweden

In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment.

关键词： fingerprint identification image registration correlation methods search problems latent fingerprints preregistration hierarchical algorithm partial fingerprint registration full fingerprint orientation fields normalised correlation measure similarity measures score fusion similarity scores search space partial fingerprint identification rank identification improvements minutiae-based automated fingerprint identification systems NIST-special database hierarchical registration

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Adaptive Approach for Sensor Placement Combining a Quantitative Strategy with Engineering Practice 25th

Adaptive Approach for Sensor Placement Combining a Quantitat...

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25th Workshop of the European-Group-for-Intelligent-Computing-in-Engineering (EG-ICE)

作者： Bertola, Numa Joy Smith, Ian F. C. Swiss Fed Inst Technol Singapore ETH Ctr Future Cities Lab 1 CREATE WayCREATE Tower Singapore 138602 Singapore Swiss Fed Inst Technol EPFL Appl Comp & Mech Lab IMAC Sch Architecture Civil & Environm Engn ENAC CH-1015 Lausanne Switzerland

ISBN: (纸本)9783319916385;9783319916378

Infrastructure-capacity challenges due to growing populations, increasing urbanization and ageing existing assets are widespread. The assessment of remaining life of existing infrastructure has the potential to improve decision-making on asset management. However, this task is challenging due to the difficulties in modelling of infrastructure behavior. Error-domain model falsification ( EDMF) is an easy-to-use model-based structural-identification methodology where field measurements are used to improve knowledge of the real behavior of structures. This methodology accommodates aleatory and systematic uncertainties induced by sources such as modelling assumptions, boundary conditions and numerical computation. Field-measurements, collected during load tests, lead to the identification of bridge characteristics such as geometric and material properties as well as support conditions. Benefits of structural-identification practice depend upon the methodology chosen but also upon the choice of sensor type and its location. In spite of such obvious importance, sensor types and positions are usually chosen using only qualitative rules of thumb coming from engineering experience. A more rational strategy to design optimal sensor configuration is justified and this is the aim of the study described in this paper. First, two quantitative methodologies for sensor-configuration optimization are compared with the solution designed by engineers using experience only on a full-scale case study in Singapore. The first quantitative sensor-placement methodology selects sensor locations with the largest signal-to-noise ratio in model prediction. The second strategy maximizes the joint entropy of the sensor configuration, using the hierarchical algorithm for sensor-placement. The joint entropy evaluates redundant information between possible sensor locations to select locations delivering the largest information gain when coupled. The performance of sensor configurations is evaluated

关键词： Sensor placement Structural identification hierarchical algorithm Model falsification

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Process Distance-aware Adaptive MPI Collective Communications

Process Distance-aware Adaptive MPI Collective Communication...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Ma, Teng Herault, Thomas Bosilca, George Dongarra, Jack J. Univ Tennessee EECS Knoxville TN USA

ISBN: (纸本)9780769545165

Message Passing Interface (MPI) implementations provide a great flexibility to allow users to arbitrarily bind processes to computing cores to fully exploit clusters of multicore/many-core nodes. An intelligent process placement can optimize application performance according to underlying hardware architecture and the application's communication pattern. However, such static process placement optimization can't help MPI collective communication, whose topology is dynamic with members in each communicator. Conversely, a mismatch between the collective communication topology, the underlying hardware architecture and the process placement often happens due to the MPI's limited capabilities of dealing with complex environments. This paper proposes an adaptive collective communication framework by combining process distance, underlying hardware topologies, and runtime communicator together. Based on this information, an optimal communication topology will be generated to guarantee maximum bandwidth for each MPI collective operation regardless of process placement. Based on this framework, two distance-aware adaptive intra-node collective operations (Broadcast and Allgather) are implemented as examples inside Open MPI's KNEM collective component. The awareness of process distance helps these two operations construct optimal runtime topologies and balance memory accesses across memory nodes. The experiments show these two distance-aware collective operations provide better and more stable performance than current collectives in Open MPI regardless of process placement.

关键词： MPI Collective Communication Process Distance hierarchical algorithm Ring algorithm

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Study and Implementation of Clustering algorithms in R 3

Study and Implementation of Clustering Algorithms in R

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International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

作者： Meena, Gaurav Chauhan, Pradeep Singh Choudhary, Ravi Raj Cent Univ Rajasthan Dept Comp Sci Ajmer 305917 Rajasthan India

ISBN: (纸本)9781538632437

clustering is a process in which we group the data by finding similarities between data based on their characteristics. These groups are called cluster. In clustering, there is a division of data into groups of similar objects. These groups are the clusters, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. Clustering is unsupervised learning technique, based on the concept of maximize infra-clustering and minimize inter- clustering. Nowadays, clustering of biological dataset is the widely researched topic among computer science. Bio- informatics has become area that receive most of the attention of data mining techniques. Generally, bio- informatics targets to solve complicated problems like gene categorization and its functionality, gene expression analysis of data obtained from micro- array experiments etc. These clustering techniques are addressed with R. Clustering techniques are used to analyze the structure of biological data. There are many different methods but we study k- means, hierarchical and Density- based clustering algorithm for Biological Data using R programming tool.

关键词： k- means algorithm hierarchical algorithm Density-based algorithm

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IMPROVED SIDE MATCHING FOR MATCHED-TEXTURE CODING 5

IMPROVED SIDE MATCHING FOR MATCHED-TEXTURE CODING

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5th European Workshop on Visual Information Processing (EUVIP)

作者： Jin, Guoxin Pappas, Thrasyvoulos N. Neuhoff, David L. Northwestern Univ Dept EECS Evanston IL 60208 USA Univ Michigan Dept EECS Ann Arbor MI 48109 USA

ISBN: (纸本)9781479945726

Matched-texture coding (MTC) exploits the redundancy of textured regions in natural images in order to achieve low-encoding-rate structurally lossless compression. A key element of MTC identifying large image blocks that can be replaced with previously encoded blocks that have similar structure. The side matching (SM) approach attempts to do this by matching the upper and left boundary (side) of a target block with the corresponding boundary of the candidate block, and then, among the best side matches, selecting the one that best matches the target block. We explore the effectiveness of, and interplay between, three SM criteria in order to increase the number and quality of matches and to reduce the computational complexity. The criteria are mean-squared-error, log variance ratio, and partial implementations of STSIM-2, a recently proposed structural texture similarity metric. We propose a hierarchical algorithm for side matching, with three layers that utilize the three metrics, that improves performance and reduces the computation complexity. To set thresholds for the first and second layers of the hierarchical algorithm, we rely on Bayesian hypothesis testing. To estimate the necessary local probability densities, we introduce an adaptive estimation technique that depends on the side matching search region. Experimental results demonstrate an improvement of quality for a given encoding rate over previous realizations of MTC.

关键词： Side matching Matched-Texture Coding Bayesian Hypothesis Test hierarchical algorithm

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Efficient 3D Curve Skeleton Extraction from Large Objects

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16th Iberoamerican Congress on Pattern Recognition

作者： Szilagyi, Laszlo Szilagyi, Sandor Miklos Iclanzan, David Szabo, Lehel Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783642250842

Curve skeletons are used for linear representation of 3D objects in a wide variety of engineering and medical applications. The outstandingly robust and flexible curve skeleton extraction algorithm, based on generalized potential fields, suffers from seriously heavy computational burden. In this paper we propose and evaluate a hierarchical formulation of the algorithm, which reduces the space where the skeleton is searched, by excluding areas that are unlikely to contain relevant skeleton branches. The algorithm was evaluated using dozens of object volumes. Tests revealed that the computational load of the skeleton extraction can be reduced up to 100 times, while the accuracy doesn't suffer relevant damage.

关键词： 3D curve skeleton potential fields hierarchical algorithm parallel computation graphical processing units

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Iterative circular conebeam CT reconstruction using fast hierarchical backprojection/reprojection operators

Iterative circular conebeam CT reconstruction using fast hie...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Brokish, Jeffrey Keesing, Daniel B. Bresler, Yoram InstaRecon Inc Champaign IL 61820 USA

ISBN: (纸本)9780819480231

This is the first report on a new fast statistical iterative reconstruction algorithm for conebeam with a circular source trajectory, accelerated by InstaRecon's fast O(N(3)logN) hierarchical cone beam backprojection(1) and reprojection algorithms. We report on the results of image quality and run-time comparisons with iterative algorithms based on conventional backprojection and reprojection. We demonstrate that the iterative algorithm introduced here can provide image quality indistinguishable from an iterative algorithm using conventional BP/RP operators, while providing almost a 10x speedup in reconstruction rates. Combining the 10x algorithmic acceleration with additional hardware acceleration by FPGA, Cell, or GPU implementation, this work indicates the feasibility of iterative reconstruction algorithms for dose reduction and image quality improvement in routine CT practice, at competitive speeds and affordable cost.

关键词： Tomography Cone-Beam Iterative Reconstruction hierarchical algorithm Fast algorithm

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