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Active Thermography Data-Processing Algorithm for Nondestructive Testing of Materials

IEEE ACCESS 2020年 8卷 175054-175062页

作者： Da Silva, Wellington Francisco Melo, Renan A. C. Grosso, Marcella Pereira, Gabriela R. Riffel, Douglas Bressan Univ Fed Sergipe Dept Mech Engn BR-49100000 Sao Cristovao Brazil Univ Fed Rio de Janeiro Nondestruct Testing Corros & Welding Lab BR-21941901 Rio De Janeiro Brazil

This article presents the development of an active thermography algorithm capable of detecting defects in materials, based on the techniques of Thermographic Signal Reconstruction (TSR), Thermal Contrast (TC) and the physical principles of heat transfer. The results obtained from this algorithm are compared to the TSR technique and the raw thermogram obtained by stepped thermography inspection. Experimentally, a short thermal pulse is used and the surface temperature of the sample is monitored over time with an infrared camera. Due to the volume of data, the first step is data compression. Newton's law of cooling was used to store the normalized temperature data pixel-by-pixel over time and a compression ratio of 99% was obtained. The main contributions of the developed algorithm are: only four parameters for data compression and the concept of change in the direction of the heat flow to delimit the edges of the defects, where the borders are identified with a remarkable accuracy. Some well known image processing technique are also integrated to improve the thermal analysis: edge detection/interface between the sample and the image background;consolidation in a single image by aggregating the indicators referring to the concept of cooling/heating time constant, maximum thermal amplitude and contrast.

关键词： Heating systems Cooling Temperature measurement Image edge detection Temperature distribution Image coding Monitoring Active thermography thermal analysis infrared imaging data compression change detection algorithms image processing

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Tree Mining Application to Matching of Heterogeneous Knowledge Representations

Tree Mining Application to Matching of Heterogeneous Knowled...

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IEEE International Conference on Granular Computing (GRC)

作者： Fedja Hadzic Tharam S. Dillon Elizabeth Chang Digital Ecosystems and Business Intelligence Institute Curtin University of Technology Perth Australia

Matching of heterogeneous knowledge sources is of increasing importance in areas such as scientific knowledge management, e-commerce, enterprise application integration, and many emerging Semantic Web applications. With the desire of knowledge sharing and reuse in these fields, it is common that the knowledge coming from different organizations from the same domain is to be matched. We propose a knowledge matching method based on our previously developed tree mining algorithms for extracting frequently occurring subtrees from a tree structured database such as XML. Using the method the common structure among the different representations can be automatically extracted. Our focus is on knowledge matching at the structural level and we use a set of example XML schema documents from the same domain to evaluate the method. We discuss some important issues that arise when applying tree mining algorithms for detection of common document structures. The experiments demonstrate the usefulness of the approach.

关键词： Knowledge representation XML Ontologies Knowledge management Automation change detection algorithms Semantic Web Buildings Web services Application software

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A Framework for Evolving Multi-Shaped Detectors in Negative Selection

A Framework for Evolving Multi-Shaped Detectors in Negative ...

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IEEE Symposium on Foundations of Computational Intelligence, FOCI

作者： Sankalp Balachandran Dipankar Dasgupta Fernando Nino Deon Garrett Computer Science Department University of Memphis USA Universidad Nacional de Colombia USA

This paper presents a framework to generate multi-shaped detectors with valued negative selection algorithms (NSA). In particular, detectors can take the form of hyper-rectangles, hyper-spheres and hyper-ellipses in the non-self space. These novel pattern detectors (in the complement space) are evolved using a genetic search (the structured genetic algorithm), which uses hierarchical genomic structures and a gene activation mechanism to encode multiple detector shapes. This genetic search (the structured GA) allows in maintaining diverse shapes while contributing to the proliferation of best suited detector shapes in expressed phenotype. The results showed that a significant coverage of the non-self space could be achieved with fewer detectors compared to other NSA approaches (using only single-shaped detectors). The uniform representation scheme and the evolutionary mechanism used in this work can serve as a baseline for further extension to use several shapes, providing an efficient coverage of non-self space.

关键词： Detectors change detection algorithms Genomics Bioinformatics Computational intelligence Genetic algorithms Evolutionary computation Shape measurement Biological cells Computer science

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Nonparametric algorithm for a detection of random process disorder in the signals of radar remote sensing

Nonparametric algorithm for a detection of random process di...

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International Conference on Mathematical Methods in Electromagnetic Theory (MMET)

作者： I. Prokopenko K. Prokopenko National Aviation University Ukraine

A new nonparametric algorithm of radar signal disorder detection is considered. The algorithm is based on spectral estimation of a few signal samples of some close windows and application of a nonparametric Wilkockson's test to compare them. The algorithm can be used for radar signal detection specifically in the tasks of turbulence detection in clouds and precipitation as well as for moving target detection. The efficiency of the new algorithm is analyzed.

关键词： Radar detection Signal processing Radar remote sensing Random processes change detection algorithms Radar signal processing Radar cross section Signal synthesis Signal analysis Probability

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Glaucoma progression detection using variational expectation maximization algorithm

Glaucoma progression detection using variational expectation...

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IEEE International Symposium on Biomedical Imaging

作者： Akram Belghith Madhusudhanan Balasubramanian Christopher Bowd Robert N Weinreb Linda M. Zangwill Hamilton Glaucoma Center University of California San Diego La Jolla CA USA

ISBN: (纸本)9781467364560

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropthy characterized by distinctive changes in the optic nerve head (ONH) and visual field. In this context, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatour progression using the HRT images. In contrast to previous works that do not integrate a priori knowledge available on the images and particularly the spatial pixel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To our knowledge, the task of inferring the glaucomatous changes with a Variational Expectation Maximization VEM algorithm will be used for the first time in the glaucoma diagnosis framework. We then compared the diagnostic performance of the proposed framework to existing methods of progression detection.

关键词： Markov processes Optical imaging Visualization Noise Optical sensors change detection algorithms Approximation methods

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CORSISCA: classification of remotely sensed images-a soft computing approach

CORSISCA: classification of remotely sensed images-a soft co...

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IEEE India Conference (INDICON)

作者： A. Saurabh B.V. Raghu A. Agrawal India Indian Inst. of Inf. Technol. Allahabad India

The classification of multispectral satellite images is a challenging problem and has a number of applications such as feature identification, change detection, etc. We apply modified neural network algorithms: GA-BP (genetic algorithm as precursor to the back propagation) and modular artificial neural network (MNN) to classify the LISS-3 image of Allahabad area. We also classify the resolution merged image (USS-3 with PAN) using the same algorithms. By using genetic algorithm as a precursor to ANN, we increase the probability of reaching to the global minimum, thus reducing the problem of a stuck neural network in the local minimum. MNN models the human brain more closely to apply task decomposition to the satellite images as well. The output of the above techniques are generated and analyzed.

关键词： Artificial neural networks Satellites Biological neural networks change detection algorithms Genetic algorithms Multi-layer neural network Computer vision Image resolution Brain modeling Humans

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Real-time adaptive background segmentation

Real-time adaptive background segmentation

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IEEE International Conference on Multimedia and Expo (ICME)

作者： D. Butler S. Sridharan V.M. Bove Queensland University of Technology Brisbane QLD Australia MIT Media Laboratory Cambridge MA USA

Automatic analysis of digital video scenes often requires the segmentation of moving objects from the background. Historically, algorithms developed for this purpose have been restricted to small frame sizes, low frame rates or offline processing. The simplest approach involves subtracting the current frame from the known background. However, as the background is unknown, the key is how to learn and model it. This paper proposes a new algorithm that represents each pixel in the frame by a group of clusters. The clusters are ordered according the likelihood they model the background and are adapted to deal with background and lighting variations. Incoming pixels are matched against the corresponding cluster group and are classified according to whether the matching cluster is considered part of the background. The algorithm has been subjectively evaluated against three other techniques. It demonstrated equal or better segmentation than the other techniques and proved capable of processing 320 /spl times/ 240 video at 28 fps, excluding post-processing.

关键词： Layout Australia Cameras Surveillance Object detection Degradation change detection algorithms Laboratories Large-scale systems Hardware

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Erratum to “Semantic Unsupervised change detection of Natural Land Cover With Multitemporal Object-Based Analysis on SAR Images” [Jul 21 5494-5514]

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IEEE Transactions on Geoscience and Remote Sensing 2021年第8期59卷 7160-7160页

作者： Donato Amitrano Raffaella Guida Pasquale Iervolino Surrey Space Centre University of Surrey Guildford U.K. Airbus Defense and Space Guildford U.K.

In the above article [1] , the author affiliations were incorrectly listed. The correct affiliations are as follows:

关键词： Synthetic aperture radar Semantics Image analysis Accuracy Vegetation mapping change detection algorithms

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A New FFT-Based Digital Frequency Relay for Load Shedding

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IEEE Power Engineering Review 1982年第2期PER-2卷 29-30页

作者： Adly A. Girgis Fredric M. Ham Electrical Engineering Department Iowa State University Ames IA USA Harris Corporation Melbourne FL USA

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A new power-based digital algorithm for generator protection

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IEEE Power Engineering Review 2000年第2期20卷 49-52页

作者： O. Usta M. Bayrak Electrical Engineering Istanbul Technical University Istanbul Turkey

In this letter a new power-based digital algorithm is introduced to provide protection for the generators against unbalanced faults. The algorithm measures the generator's three-phase power output and monitors the sinusoidal component of the instantaneous power. When the magnitude of the sinusoidal component exceeds a predefined value, the algorithm checks the direction of the negative sequence-reactive power flow to discriminate between internal and external faults. The proposed algorithm initiates fast tripping for internal asymmetrical faults, and provides back up protection for external unbalanced conditions.

关键词： Power generation Voltage Power system protection Load flow Stator windings Equations Fault detection Frequency Synchronous generators change detection algorithms

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