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Detection of transformer winding faults using FRA and image features

IET ELECTRIC POWER APPLICATIONS 2020年第6期14卷 972-980页

作者： Zhou, Lijun Jiang, Junfei Zhou, Xiangyu Wu, Zhenyu Lin, Tong Wang, Dongyang Southwest Jiaotong Univ Coll Elect Engn Chengdu Peoples R China Guangdong Power Grid co Ltd Elect Power Res Inst Guangzhou Peoples R China

Frequency response analysis (FRA) is a widely used approach for detecting winding faults in a transformer. Appropriate and quantitative FRA features will help to improve the accuracy of fault diagnosis. In this study, a novel FRA interpretation including new image features is proposed based on the image processing technique. First, winding faults of different windings are simulated in a test autotransformer and the FRA curves are measured under various faults. Then frequency region division method and image processing technique are first applied to the measured FRA curves. The area ratio and centroid deviation in different frequency regions are calculated through a novel algorithm. Finally, the image features are used as the inputs to support vector machine model. Additionally, three different parametric optimisation algorithm are compared during the training process. The results show that the particle swarm optimisation and image feature exhibit best performance for identifying winding faults.

关键词： power transformer testing support vector machines frequency response transformer windings fault diagnosis particle swarm optimisation image processing feature extraction power engineering computing image features quantitative FRA features fault diagnosis FRA interpretation image processing technique frequency region division method measured FRA curves particle swarm optimisation parametric optimisation algorithm transformer winding fault detection frequency response analysis area ratio centroid deviation support vector machine model test autotransformer

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Linking the Random Forests model and GIS to Assess Geo-Hazards Risk: A Case Study in Shifang County, China

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IEEE ACCESS 2020年 8卷 28033-28042页

作者： Huang, Pei Peng, Li Pan, Hongyi Sichuan Normal Univ Coll Geog & Resources Chengdu 610101 Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Chengdu 610041 Peoples R China

This study proposes an objective and accurate geo-hazards risk assessment method to address the challenge of increasingly severe hazards around the world. Previous studies mostly began from the perspectives of hazard and vulnerability, ignoring the role of survey data at disaster sites during risk assessments. The random forests (RF) model was applied in this study. Combined with detailed data from hazard sites, a geo-hazards risk assessment model was constructed, with the two dimensions of disaster hazard and vulnerability, was constructed. We analyzed the spatial pattern characteristics and the internal patterns of disaster risk and discussed the risk controlling factors and their contributions. The results showed the following. (1) The RF model, when combined with hazard, vulnerability conditions, and detailed data from disaster sites, can be used to zone and verify regional geo-hazards risks, providing a method for point-to-surface disaster risk mapping. (2) The RF-based geo-hazards risk assessment results were relatively consistent with the evaluation results from the support vector machine (SVM) model, but the accuracy and stability of the RF model were higher. (3) This method can be used to avoid the subjectivity in determining the weights and threshold values for indexes and can calculate the contribution of each index to geo-hazards risks.

关键词： Geo-hazards random forests model risk assessment Shifang county support vector machine model

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machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World's Largest Water Transfer Project-A Case Study in the Miyun Reservoir, North China

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WATER 2021年第17期13卷 2406页

作者： Liao, Zhenmei Zang, Nan Wang, Xuan Li, Chunhui Liu, Qiang Beijing Normal Univ Sch Environm State Key Lab Water Environm Simulat Beijing 100875 Peoples R China Beijing Normal Univ Sch Environm Key Lab Water & Sediment Sci Minist Educ Beijing 100875 Peoples R China Chinese Acad Environm Planning Beijing 100012 Peoples R China

Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world's largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir's water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

关键词： chlorophyll-a concentration prediction machine learning support vector machine model random forest model water quality management decision South-to-North water transfer project

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Reliability prediction of CNC machine tools based on GM(1,1)-SVM combined model 5

Reliability prediction of CNC machine tools based on GM(1,1)...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Li, Dong-yang Piao, Cheng-dao Wang, De-chao Zhao, De-jin Yanbian Univ Engn Coll Yanji Jilin Peoples R China

ISBN: (纸本)9781665423144

Aiming at the characteristics of low cost and complex influencing factors of CNC machine tool failure data sample collection, a combination prediction model based on gray prediction and support vector machine is proposed, use Python and MATLAB software to predict the time between failures of CNC machine tools in the SVM model and the GM(1,1) model, respectively, and obtain the predicted time between failures, use the standard deviation method to determine the weight of the combined model, and establish GM(1,1)-SVM combined prediction model, the accuracy and practicability of the combined forecasting model are verified through examples. The results show that the prediction of the combined model is higher than that of the single model and the results are reliable, the combined model provides a theoretical basis for the CNC machine tool users and maintenance personnel to maintain it in advance, and has guiding value for the reliability prediction.

关键词： Grey model support vector machine model GM (1,1)-SVM combined model Reliability prediction

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Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models

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GEOMATICS NATURAL HAZARDS & RISK 2018年第1期9卷 1053-1070页

作者： Oh, Hyun-Joo Kadavi, Prima Riza Lee, Chang-Wook Lee, Saro Korea Inst Geosci & Mineral Resources Deajeon South Korea Kangwon Natl Univ Div Sci Educ Chunchon Gangwon Do South Korea

The main purpose of this study was to produce landslide susceptibility maps using evidential belief function (EBF), logistic regression (LR) and support vector machine (SVM) models and to compare their results for the region surrounding Yongin, South Korea. We compiled a landslide inventory map of 82 landslides based on reports and aerial photographs and confirmed these data through extensive field surveys. All landslides were randomly separated into two data sets of 41 landslide data points each;half were selected to establish the model, and the remaining half were used for validation. We divided 18 landslide conditioning factors into the following four categories: topography factors, hydrology factors, soil map and forest map;these were considered for landslide susceptibility mapping. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the EBF, LR and SVM models. The three models were then validated using the area under the curve (AUC) method. According to the validation results, the prediction accuracy of the LR model (AUC = 94.59%) was higher than those of the EBF model (AUC = 92.25%) and the SVM model (AUC = 81.78%);the LR model also had the highest training accuracy.

关键词： Area under the curve evidential belief function model landslide susceptibility logistic regression support vector machine model

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Using support vector machine models for Size of Motorcycle Taxi Terminal in Bang Yai District Analysis

Using Support Vector Machine Models for Size of Motorcycle T...

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作者： Morakot Worachairungreung Kunyaphat Thanakunwutthirot Thammasak Songka Faculty of Humanities and Social Sciences Suan Sunandha Rajabhat University Fuculty of Industrail Technology Suan Sunandha Rajabhat University Academic Service Center Burapha University

This study aims to study the factors of building usage that influence the size of the motorcycle taxi ***,as there are many factors of building usage,this research aims to study whether the modern non-parametric model,such as,the support vector machine(SVM),is able to classify the size of the motorcycle taxi terminal,using the dataset of the building *** on random 50 grid cells,with the size of 500 *** ratio of the correct classification of the training data is 85 percents,whereas the SVM model is able to predict the size *** ratio of the correct classification of the testing data is 100 *** though,one of the SVM model's limitations is the Black Box *** Black Box prevents the model from classifying the factors that directly influence the motorcycle taxi terminal's ***,the author uses that dataset for the correlation test,in order to find the correlation *** this case,the factors that are highly correlated with the motorcycle taxi terminal's sizes are the number of ATMs and the bus stations.

关键词： support vector machine model Motorcycle taxi terminal Correlation Building usage

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Identification of Parkinson's disease-related pathways and potential risk factors

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JOURNAL OF INTERNATIONAL MEDICAL RESEARCH 2020年第10期48卷 300060520957197-300060520957197页

作者： Shen, Jun Chen, Xiao-Chang Li, Wang-Jun Han, Qiu Chen, Chun Lu, Jing-Min Zheng, Jin-Yu Xue, Shou-Ru Soochow Univ Dept Neurol Affiliated Hosp 1 188 Shizi Rd Suzhou 215006 Jiangsu Peoples R China Xuzhou Med Univ Dept Neurol Affiliated Huaian Hosp Huaian Jiangsu Peoples R China Second Peoples Hosp Huaian Huaian Jiangsu Peoples R China Hongze Huaian Dist Peoples Hosp Dept Neurol Huaian Jiangsu Peoples R China Yangzhou Univ Dept Neurol Changshu Peoples Hosp 2 Clin Med Coll 5 Changshu Jiangsu Peoples R China Nanjing Med Univ Dept Neurol Huaian Peoples Hosp 1 Affiliated Huaian Peoples Hosp 1 Huaian Jiangsu Peoples R China

Objective To identify Parkinson's disease (PD)-associated deregulated pathways and genes, to further elucidate the pathogenesis of PD. Methods Dataset GSE100054 was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) in PD samples were identified. Functional enrichment analyses were conducted for the DEGs. The top 10 hub genes in the protein-protein interaction (PPI) network were screened out and used to construct a support vector machine (SVM) model. The expression of the top 10 genes was then validated in another dataset, GSE46129, and a clinical patient cohort. Results A total of 333 DEGs were identified. The DEGs were clustered into two gene sets that were significantly enriched in 12 pathways, of which 8 were significantly deregulated in PD, including cytokine-cytokine receptor interaction, gap junction, and actin cytoskeleton regulation. The signature of the top 10 hub genes in the PPI network was used to construct the SVM model, which had high performance for predicting PD. Of the 10 genes,GP1BA,GP6,ITGB5, andP2RY12were independent risk factors of PD. Conclusion Genes such asGP1BA,GP6,P2RY12,andITGB5play critical roles in PD pathology through pathways including cytokine-cytokine receptor interaction, gap junctions, and actin cytoskeleton regulation.

关键词： Parkinson's disease deregulated pathway differentially expressed gene clustering analysis protein-protein interactions support vector machine model genetic risk factors

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Advanced method for short-term wind power prediction with multiple observation points using extreme learning machines

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JOURNAL OF ENGINEERING-JOE 2018年第1期2018卷 29-38页

作者： Mahmoud, Tawfek Dong, Zhao Yang Ma, Jin Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Univ NSW Sch Elect Engn & Telecommun Sydney NSW Australia

This research paper presents an advanced approach to enhance the short-term wind power prediction based on artificial intelligence techniques. A high-quality wind power prediction is essential for power system planning, operation, and control. Thus, a new novel approach has been developed to improve the quality and reliability of the calculated results by integrating advanced time series processing method and the extreme learning machine technique. Moreover, historical records are utilised from numerical weather information and multiple observations points close to real wind farm sites within Australia regions. The wind speed is assessed by using the developed model in the first stage, and then the wind power and capacity factor is calculated using wind power-speed curve for each observation site. Artificial neural network, fuzzy logic (adaptive neuro-fuzzy inference system), and support vector machine models are used for model verifications, validations, and practical applications. The developed model is tested using real wind measurements by Bureau of Meteorology, 15 selected weather stations corresponded to the locations of nearby real wind farm sites in Australia. The demonstrated results and performance indicators, e.g. root mean square error and mean absolute error are compared with Khalid, persistence, and Grey predictor models for validations and verifications reasons. As the potential gains over other techniques, the proposed model has found more efficient and superior for wind power estimation and prediction than other developed conventional methods and models, which in turn improves the power system performance, and reduces the economic impacts.

关键词： power engineering computing grey systems mean square error methods support vector machines fuzzy reasoning fuzzy neural nets time series learning (artificial intelligence) wind power plants mean absolute error Grey predictor model wind power estimation root mean square error short-term wind power prediction artificial intelligence technique high-quality wind power prediction power system planning power system operation power system control advanced time series processing method extreme learning machine technique historical records numerical weather information real wind farm sites Australia region wind speed capacity factor wind power-speed curve artificial neural network fuzzy logic model adaptive neuro-fuzzy inference system support vector machine model model verification real wind measurements weather station Australia

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LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features

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BMC CANCER 2019年第1期19卷 263-263页

作者： Yu, Jiaxian Hu, Yueming Xu, Yafei Wang, Jue Kuang, Jiajie Zhang, Wei Shao, Jianlin Guo, Dianjing Wang, Yejun Shenzhen Univ Hlth Sci Ctr Dept Cell Biol & Genet Shenzhen 518060 Peoples R China Chinese Univ Hong Kong State Key Lab Agrobiotechnol Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Sch Life Sci Shatin Hong Kong Peoples R China Sehnzhen GenRead Technol Co Ltd Shenzhen 518000 Peoples R China Zhejiang Hosp 12 Lingyin Rd Hangzhou 310003 Zhejiang Peoples R China

BackgroundLung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis *** obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different *** were found with higher mutation rates in good ( 3-year survival) than in poor (<3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. support vector machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC *** prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.

关键词： Lung adenocarcinomas Somatic mutational Personal medicine support vector machine model machine learning

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Investigating driver injury severity patterns in rollover crashes using support vector machine models

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ACCIDENT ANALYSIS AND PREVENTION 2016年 90卷 128-139页

作者： Chen, Cong Zhang, Guohui Qian, Zhen Tarefder, Rafiqul A. Tian, Zong Univ New Mexico Dept Civil Engn Albuquerque NM 87131 USA Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Heinz Coll Pittsburgh PA 15213 USA Univ Nevada Dept Civil & Environm Engn Reno NV 89557 USA

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Driver injury severity Rollover crash support vector machine model Kernel function Traffic safety

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