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Evaluating the effectiveness of a machine learning approach based on response time and reliability for islanding detection of distributed generation

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IET RENEWABLE POWER GENERATION 2017年第11期11卷 1392-1400页

作者： Alam, Mollah Rezaul Muttaqi, Kashem M. Bouzerdoum, Abdesselam Amer Int Univ Bangladesh Dept Elect & Elect Engn Dhaka 1212 Bangladesh Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2500 Australia Hamad Bin Khalifa Univ HBKU Coll Sci & Engn Education City Doha Qatar

Conventional relays, such as vector surge relay, frequency relay and rate-of-change-of-frequency relay, are usually employed for islanding detection;however, these conventional relays fail to detect islanding incidents in the presence of small power imbalance inside the islanded system. This study presents an islanding detection approach for synchronous type distributed generation using multiple features extracted from network variables and a support vector machine (SVM) classifier. Features are extracted from a sliding temporal window, whose width is selected so as to achieve the highest detection rate at a fixed false alarm rate. The SVM classifier is trained with linear, polynomial and Gaussian radial basis function kernels, and the parameters of the kernels are tuned to improve the classification performance. The application of the proposed method is illustrated for islanding cases associated with different power imbalance conditions, including small power imbalance conditions associated with the non-detection zone of conventional relays. Furthermore, variation of detection time as a function of power imbalance scenarios, which involve all probable combinations of deficit of active/reactive and excess of active/reactive power imbalance, is assessed in the testing phase. The performance of the proposed approach is evaluated and compared with those of conventional relays in terms of reliability and response time of islanding detection.

关键词： distributed power generation power generation reliability learning (artificial intelligence) relay protection feature extraction support vector machines pattern classification radial basis function networks power engineering computing machine learning approach effectiveness evaluation response time reliability relay failure islanding detection approach synchronous type distributed generation multiple feature extraction support vector machine classifier SVM classifier network variables sliding temporal window fixed false alarm rate Gaussian radial basis function kernels polynomial radial basis function kernels linear radial basis function kernels small power imbalance conditions testing phase

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Significance of Higher-Order Spectral Analysis in Infant Cry Classification

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2018年第1期37卷 232-254页

作者： Chittora, Anshu Patil, Hemant A. Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

In this paper, higher-order spectral analysis is applied to infant cry signals for classification of normal infant cries from pathological infant cries. From the family of higher-order spectra, bispectrum is considered for the proposed task. Bispectrum is the Fourier transform of the third-order cumulant function. To extract features from the bispectrum, application of higher-order singular value decomposition theorem is proposed. Experimental results show the average classification accuracy of 82.44 +/- 4.03% and Matthew's correlation coefficient (MCC) of 0.62 with proposed bispectrum features. In all of the experiments reported in this paper, support vector machine with radial basis function kernel is used as the pattern classifier. Performance of the proposed features is also compared with the state-of-the-art methods such as linear frequency cepstral coefficients, Mel frequency cepstral coefficients, perceptual linear prediction coefficients, linear prediction coefficients, linear prediction cepstral coefficients and perceptual linear prediction cepstral coefficients, and is found to be better than that given by these feature sets. The proposed bispectrum-based features are shown to be robust under signal degradation or noisy conditions at various SNR levels. Performance in the presence of noise is compared with the state-of-the-art spectral feature sets using MCC scores. In addition, effectiveness of cryunit segmentation in normal and pathological infant cry classification task is reported.

关键词： Bispectrum Cumulants Higher-order singular value decomposition theorem Higher-order spectral analysis support vector machine classifier

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An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium

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BMC BIOINFORMATICS 2012年第1期13卷 1-14页

作者： Schikora, Marek Neupane, Balram Madhogaria, Satish Koch, Wolfgang Cremers, Daniel Hirt, Heribert Kogel, Karl-Heinz Schikora, Adam Univ Giessen IFZ Inst Plant Pathol & Appl Zool D-35392 Giessen Germany Fraunhofer FKIE Dept Sensor Data & Informat Fus D-53343 Wachtberg Germany Tech Univ Munich Dept Comp Sci D-85748 Garching Germany Univ Evry CNRS INRA URGV Plant Genom F-97000 Evry France

Background: The enteric pathogen Salmonella is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by Salmonella is an active infection process. Salmonella changes the metabolism and adjust the plant host by suppressing the defense mechanisms. In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis. Results: The algorithm is designed to attribute image pixels into one of the two classes: healthy and unhealthy. The task is solved in three steps. First, we perform segmentation to divide the image into foreground and background. In the second step, a support vector machine (SVM) is applied to predict the class of each pixel belonging to the foreground. And finally, we do refinement by a neighborhood-check in order to omit all falsely classified pixels from the second step. The developed algorithm was tested on infection with the non-pathogenic E. coli and the plant pathogen Pseudomonas syringae and used to study the interaction between plants and Salmonella wild type and T3SS mutants. We proved that T3SS mutants of Salmonella are unable to suppress the plant defenses. Results obtained through the automatic analyses were further verified on biochemical and transcriptome levels. Conclusion: This report presents an automatic pixel-based classification method for detecting "unhealthy" regions in leaf images. The proposed method was compared to existing method and showed a higher accuracy. We used this algorithm to study the impact of the human pathogenic bacterium Salmonella Typhimurium on plants immune system. The comparison between wild type bacteria and T3SS mutants showed similarity in the infection process in animals and in plants. Plant epidemiology is only one possible application of the proposed algorithm, it can be easily extended to other detection tasks, which also rely on color information, or even extended to other features

关键词： support vector machine Gaussian Mixture Model Arabidopsis Plant Hypersensitive Response support vector machine classifier

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A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 112卷 460-475页

作者： Ahmadipour, Masoud Hizam, Hashim Othman, Mohammad Lutfi Radzi, Mohd Amran Mohd Chireh, Nikta Univ Putra Malaysia Dept Elect & Elect Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia ALPER Serdang 43400 Malaysia Alaodoleh Semnani Inst Higher Educ Dept Architecture Engn Semnan Iran

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn.

关键词： Islanding detection Slantlet transform Ridgelet Probabilistic Neural Network support vector machine classifier Harmony search method

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Evaluation of Radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation

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International Journal of Digital Earth 2019年第10期12卷 1177-1197页

作者： Amit Kumar Sharma Laurence Hubert-Moy Buvaneshwari Sriramulu M.Sekhar Laurent Ruiz S.Bandyopadhyay Shiv Mohan Samuel Corgne Univ Rennes UMR LETG CNRSRennesFrance Indo-French Cell for Water Science Indian Institute of ScienceBangaloreIndia Department of Civil Engineering Indian Institute of ScienceBangaloreIndia UMR 1069 SAS INRAAgro-campus OuestRennesFrance Géosciences Environnement Toulouse Universitéde ToulouseCNRSIRDToulouseFrance EOS Indian Space Research OrganizationBangaloreIndia PLANEX Physical Research LaboratoryAhmedabadIndia

Groundwater assists farmers to irrigate crops for fulfilling the crop-water *** agriculture system is characterized by three cropping seasons known as Kharif(monsoon),Rabi(post-monsoon)and summer(pre-monsoon).In tropical countries like India,monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover,which can be well addressed by microwave remote *** the proposed research,the strength of C-band polarimetric Synthetic Aperture Radar(SAR)time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year *** present study was performed in the Berambadi experimental watershed of Kabini river basin,southern peninsular India.A total of fifteen polarimetric variables were estimated includes four backscattering coefficients(HH,HV,VH,VV)and eleven polarimetric indices for all Radarsat-2 SAR *** cumulative temporal sum(seasonal and dual-season)of these parameters was supervised classified using support vector machine(SVM)classifier with intensive ground observation *** results using the best equation(highest accuracy and kappa)shows that the Kharif,Rabi and irrigated double croplands are respectively 9.58 km2(20.6%),16.14 km2(34.7%)and 6.22 km2(13.4%)with a kappa coefficient respectively 0.84,0.74 and 0.94.

关键词： Radarsat-2 Synthetic Aperture Radar Polarimetric Indices Irrigated Cropland support vector machine classifier Kabini Critical Zone Observatory

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Mining protein networks for synthetic genetic interactions

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BMC BIOINFORMATICS 2008年第1期9卷 1-14页

作者： Paladugu, Sri R. Zhao, Shan Ray, Animesh Raval, Alpan Keck Grad Inst Appl Life Sci Claremont CA 91711 USA NIH Virtual Endoscopy & Comp Aided Diag Lab Dept Radiol Ctr Clin Bethesda MD 20892 USA Claremont Grad Univ Sch Math Sci Claremont CA 91711 USA

Background: The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/ lethal gene pairs have been extensively identified. Results: We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/ lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. Conclusion: Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein interaction networks could plausi

关键词： Gene Pair support vector machine classifier Betweenness Centrality Protein Interaction Network Closeness Centrality

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Identification of germ cell-specific genes in mammalian meiotic prophase

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BMC BIOINFORMATICS 2013年第1期14卷 1-13页

作者： Li, Yunfei Ray, Debjit Ye, Ping Washington State Univ Sch Mol Biosci Pullman WA 99164 USA Washington State Univ Pullman WA 99164 USA Washington State Univ Ctr Reprod Biol Pullman WA 99164 USA

Background: Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies. Results: We developed a machine-learning method for identifying germ cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically during meiotic initiation and prophase. At 10% recall, the method detected spermatocyte genes and oocyte genes with 90% and 94% precision, respectively. Our method outperformed gonadal expression levels and gonadal expression correlations in predicting germ cell-specific expression. Top-predicted spermatocyte and oocyte genes were both preferentially localized to the X chromosome and significantly enriched for essential genes. Also identified were transcription factors and microRNAs that might regulate germ cell-specific expression. Finally, we experimentally validated Rps6ka3, a top-predicted X-linked spermatocyte gene. Protein localization studies in the mouse testis revealed germ cell-specific expression of RPS6KA3, mainly detected in the cytoplasm of spermatogonia and prophase spermatocytes. Conclusions: We have demonstrated that, through the use of machine-learning methods, it is possible to detect germ cell-specific expression from gonadal microarray data. Results from this study improve our understanding of the transition from germ cells to meiocytes in the mammalian gonad. Further, this appr

关键词： Germ Cell support vector machine classifier Essential Gene Meiotic Prophase Pachytene Spermatocyte

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PCA-SVM based CAD System for Focal Liver Lesions using B-Mode Ultrasound Images

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DEFENCE SCIENCE JOURNAL 2013年第5期63卷 478-486页

作者： Virmani, Jitendra Kumar, Vinod Kalra, Naveen Khandelwal, Niranjan Indian Inst Technol Roorkee Roorkee 247667 Uttar Pradesh India Postgrad Inst Med Educ & Res Chandigarh 160012 India

The contribution made by texture of regions inside and outside of the lesions in classification of focal liver lesions (FLLs) is investigated in the present work. In order to design an efficient computer-aided diagnostic (CAD) system for FLLs, a representative database consisting of images with (1) typical and atypical cases of cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small as well as large hepatocellular carcinoma (HCC) lesions and (3) normal (NOR) liver tissue is used. Texture features are computed from regions inside and outside of the lesions. Feature set consisting of 208 texture features, (i.e. 104 texture features and 104 texture ratio features) is subjected to principal component analysis (PCA) for finding the optimal number of principal components to train a support vector machine (SVM) classifier for the classification task. The proposed PCA-SVM based CAD system yielded classification accuracy of 87.2% with the individual class accuracy of 85%, 96%, 90%, 87.5% and 82.2% for NOR, Cyst, HEM, HCC and MET cases respectively. The accuracy for typical, atypical, small HCC and large HCC cases is 87.5%, 86.8%, 88.8%, and 87% respectively. The promising results indicate usefulness of the CAD system for assisting radiologists in diagnosis of FLLs.

关键词： Focal liver:lesions B-mode ultrasound principal component analysis support vector machine classifier computer-aided diagnostic system

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Multitemporal classification of TerraSAR- X data for wetland vegetation mapping

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Betbeder, Julie Rapinel, Sebastien Corpetti, Thomas Pottier, Eric Corgne, Samuel Hubert-Moy, Laurence Univ Rennes 2 LETG Rennes OSUR COSTELUMR CNRS 6554 F-35043 Rennes France Univ Rennes 1 IETR UMR CNRS 6164 F-35042 Rennes France

This paper is concerned with wetland vegetation mapping using multitemporal synthetic aperture radar imagery. Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to precisely map the distribution of vegetation formations considering flood duration. A series of six dual-polarization TerraSAR-X images (HH-VV) was acquired in 2012 during dry and wet seasons. One polarimetric parameter, the Shannon entropy (SE), and two intensity parameters (s HH and s VV), which vary with wetland flooding status and vegetation roughness, were first extracted. These parameters were then classified using support vector machine techniques based on a specific kernel adapted to the comparison of time-series data, K-nearest neighbors, and decision tree (DT) algorithms. The results show that the vegetation formations can be identified very accurately (kappa index 0.85) from the classification of SE temporal profiles derived from the TerraSAR-X images. They also reveal the importance of the use of polarimetric parameters instead of backscattering coefficients alone (HH or VV) or combined (HH and VV). (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： wetlands synthetic aperture radar TerraSAR-X imagery multitemporal classifications support vector machine classifier

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Recent developments in detection, imaging and classification for airborne maritime surveillance

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IET SIGNAL PROCESSING 2008年第3期2卷 192-203页

作者： Bon, N. Hajduch, G. Khenchaf, A. Garello, R. Quellec, J. -M. Thales Airborne Syst Brest France CLS Radar Applicat Div Brest France ENSIETA Lab E312 EA 3876 Brest France GET ENST Bretagne Brest France

The role of maritime patrol missions is to monitor large oceanic areas. The authors focus on a complete signal-processing sequence from the primary detection of the targets to their classification or recognition from an airborne radar. Contrary to the classical approach in which detection, tracking, imaging and classification are considered separately, here the authors propose an integrated strategy based on a close cooperation among all of them. Recent developments in high range resolution target detection are presented and their integration in the complete system is discussed to limit false alarms. High-resolution ISAR imaging of ships is then tackled and associated with a feature extraction process and a support vector machine classifier. A set of real data is used to illustrate the imaging and classification results.

关键词： airborne maritime surveillance detection maritime patrol missions target detection signal-processing sequence Military radar, detection and tracking systems Optical, image and video signal processing high-resolution ISAR ship imaging Radar equipment, systems and applications target tracking synthetic aperture radar feature extraction airborne maritime surveillance imaging airborne radar support vector machine classifier search radar support vector machines oceanic area monitoring airborne maritime surveillance classification

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