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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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Identification of DNA-binding proteins using support vector machines and evolutionary profiles

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BMC BIOINFORMATICS 2007年第1期8卷 1-10页

作者： Kumar, Manish Gromiha, Michael M. Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh 160036 India Natl Inst Adv Ind Sci & Technol Comp Biol Res Ctr Koto Ku Tokyo 1350064 Japan

Background: Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation, as these proteins play a crucial role in gene-regulation. In this paper, we developed various SVM modules for predicting DNA-binding domains and proteins. All models were trained and tested on multiple datasets of non-redundant proteins. Results: SVM models have been developed on DNAaset, which consists of 1153 DNA-binding and equal number of non DNA-binding proteins, and achieved the maximum accuracy of 72.42% and 71.59% using amino acid and dipeptide compositions, respectively. The performance of SVM model improved from 72.42% to 74.22%, when evolutionary information in form of PSSM profiles was used as input instead of amino acid composition. In addition, SVM models have been developed on DNAset, which consists of 146 DNA-binding and 250 non-binding chains/domains, and achieved the maximum accuracy of 79.80% and 86.62% using amino acid composition and PSSM profiles. The SVM models developed in this study perform better than existing methods on a blind dataset. Conclusion: A highly accurate method has been developed for predicting DNA-binding proteins using SVM and PSSM profiles. This is the first study in which evolutionary information in form of PSSM profiles has been used successfully for predicting DNA-binding proteins. A web-server DNAbinder has been developed for identifying DNA-binding proteins and domains from query amino acid sequences http://***/raghava/dnabinder/.

关键词： support vector machine Amino Acid Composition Protein Data Bank support vector machine model Matthews Correlation Coefficient

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MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences

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BMC BIOINFORMATICS 2011年第1期12卷 1-14页

作者： Wu, Yonggan Wei, Bo Liu, Haizhou Li, Tianxian Rayner, Simon Chinese Acad Sci Wuhan Inst Virol Bioinformat Grp State Key Lab Virol Wuhan 430071 Hubei Peoples R China Chinese Acad Sci State Key Lab Virol Wuhan Inst Virol Wuhan 430071 Hubei Peoples R China Texas Tech Univ Dept Biol Sci Lubbock TX 79409 USA

Background: MicroRNAs are a family of similar to 22 nt small RNAs that can regulate gene expression at the post-transcriptional level. Identification of these molecules and their targets can aid understanding of regulatory processes. Recently, HTS has become a common identification method but there are two major limitations associated with the technique. Firstly, the method has low efficiency, with typically less than 1 in 10,000 sequences representing miRNA reads and secondly the method preferentially targets highly expressed miRNAs. If sequences are available, computational methods can provide a screening step to investigate the value of an HTS study and aid interpretation of results. However, current methods can only predict miRNAs for short fragments and have usually been trained against small datasets which don't always reflect the diversity of these molecules. Results: We have developed a software tool, miRPara, that predicts most probable mature miRNA coding regions from genome scale sequences in a species specific manner. We classified sequences from miRBase into animal, plant and overall categories and used a support vector machine to train three models based on an initial set of 77 parameters related to the physical properties of the pre-miRNA and its miRNAs. By applying parameter filtering we found a subset of similar to 25 parameters produced higher prediction ability compared to the full set. Our software achieves an accuracy of up to 80% against experimentally verified mature miRNAs, making it one of the most accurate methods available. Conclusions: miRPara is an effective tool for locating miRNAs coding regions in genome sequences and can be used as a screening step prior to HTS experiments. It is available at http://***/bioinformatics/mirpara

关键词： support vector machine support vector machine model support vector machine Training Negative Sequence Full Length Genome Sequence

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Prediction of RNA-binding amino acids from protein and RNA sequences

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BMC BIOINFORMATICS 2011年第s13期12卷 1-12页

作者： Choi, Sungwook Han, Kyungsook Inha Univ Sch Comp Sci & Engn Inchon 402751 South Korea

Background: Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules. Results: We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient o

关键词： Feature vector Training Dataset support vector machine model Protein Data Bank File Redundancy Reduction

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Investigation and identification of protein γ-glutamyl carboxylation sites

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BMC BIOINFORMATICS 2011年第Sup13期12卷 1-11页

作者： Lee, Tzong-Yi Lu, Cheng-Tsung Chen, Shu-An Bretana, Neil Arvin Cheng, Tzu-Hsiu Su, Min-Gang Huang, Kai-Yao Yuan Ze Univ Dept Comp Sci & Engn Chungli 320 Taiwan

Background: Carboxylation is a modification of glutamate (Glu) residues which occurs post-translation that is catalyzed by gamma-glutamyl carboxylase in the lumen of the endoplasmic reticulum. Vitamin K is a critical co-factor in the post-translational conversion of Glu residues to gamma-carboxyglutamate (Gla) residues. It has been shown that the process of carboxylation is involved in the blood clotting cascade, bone growth, and extraosseous calcification. However, studies in this field have been limited by the difficulty of experimentally studying substrate site specificity in gamma-glutamyl carboxylation. In silico investigations have the potential for characterizing carboxylated sites before experiments are carried out. Results: Because of the importance of gamma-glutamyl carboxylation in biological mechanisms, this study investigates the substrate site specificity in carboxylation sites. It considers not only the composition of amino acids that surround carboxylation sites, but also the structural characteristics of these sites, including secondary structure and solvent-accessible surface area (ASA). The explored features are used to establish a predictive model for differentiating between carboxylation sites and non-carboxylation sites. A support vector machine (SVM) is employed to establish a predictive model with various features. A five-fold cross-validation evaluation reveals that the SVM model, trained with the combined features of positional weighted matrix (PWM), amino acid composition (AAC), and ASA, yields the highest accuracy (0.892). Furthermore, an independent testing set is constructed to evaluate whether the predictive model is over-fitted to the training set. Conclusions: Independent testing data that did not undergo the cross-validation process shows that the proposed model can differentiate between carboxylation sites and non-carboxylation sites. This investigation is the first to study carboxylation sites and to develop a system for identifyi

关键词： Amino Acid Composition support vector machine model Accessible Surface Area Matthews Correlation Coefficient Carboxylation Site

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Measurement and prediction of the international reputation of Chinese women's apparel and accessories

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TEXTILE RESEARCH JOURNAL 2023年第1-2期93卷 194-205页

作者： Cheng, Yan Zhou, Feng Zhao, Yu Southwest Univ Coll Sericulture Text & Biomass Sci Chongqing Beibei Peoples R China

Chinese women's apparel and accessories have been often mentioned by other countries in history and nowadays, and the underlying culture along with these apparel and accessories contributes to this developing world to some extent. The assessment of the international reputation for these kinds of apparel and accessories is important for better understanding the worldwide cultural exchange, globalization, social development and so on;however, it remains unknown. This study presents a work with the overall objective to measure and predict the international reputation of Chinese women's apparel and accessories. To reach this objective, the study first proposes a data mining procedure to use online query volume data from the Google Ngram corpus to construct a reputation index in order to measure the international reputation of Chinese women's apparel and accessories. Then, by establishing a series of socio-economic and political determinants associated with the international reputation of Chinese women's apparel and accessories, several forms of support vector machine based models are developed, aiming to predict their international reputation. There are two contributions out of this work: (1) presenting the first ever large-scale quantitative examination of the trend and measurement of the international reputation of Chinese women's apparel and accessories, which provides a new method to identify the dynamic transition of sociocultural development;(2) constructing a quantitative model to predict the international reputation of Chinese women's apparel and accessories, which generalizes an implication to understand the other sociocultural object and its impact factors.

关键词： International reputation apparel accessories Chinese women support vector machine model

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Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

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BMC BIOINFORMATICS 2009年第1期10卷 1-10页

作者： Yang, Zheng Rong Univ Exeter Sch Biosci Exeter EX4 5DE Devon England

Background: Tyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins. Results: A new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. Peptides are formed by consecutive residues symmetrically flanking tyrosine sites. They are then encoded using an amino acid hydrophobicity scale. This new approach has increased the sensitivity by 22%, the specificity by 3%, and the total prediction accuracy by 10% compared with the previous predictor using the same blind data. Meanwhile, both negative and positive predictive powers have been increased by 9%. In addition, the random forest model has an excellent feature for ranking the residues flanking tyrosine sites, hence providing more information for further investigating the tyrosine sulfation mechanism. A web tool has been implemented at http://***/ sulfotyrosine for public use. Conclusion: The random forest algorithm is able to deliver a better model compared with the Hidden Markov model, the support vector machine, artificial neural networks, and others for predicting sulfotyrosine sites. The success shows that the random forest algorithm together with an amino acid hydrophobicity scale encoding can be a good candidate for peptide classification.

关键词： Random Forest Linear Discriminant Analysis Receiver Operating Characteristic Curve support vector machine model Random Forest model

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Analysis and prediction of antibacterial peptides

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BMC BIOINFORMATICS 2007年第1期8卷 1-10页

作者： Lata, Sneh Sharma, B. K. Raghava, G. P. S. Inst Microbial Technol Chandigarh India

Background: Antibacterial peptides are important components of the innate immune system, used by the host to protect itself from different types of pathogenic bacteria. Over the last few decades, the search for new drugs and drug targets has prompted an interest in these antibacterial peptides. We analyzed 486 antibacterial peptides, obtained from antimicrobial peptide database APD, in order to understand the preference of amino acid residues at specific positions in these peptides. Results: It was observed that certain types of residues are preferred over others in antibacterial peptides, particularly at the N and C terminus. These observations encouraged us to develop a method for predicting antibacterial peptides in proteins from their amino acid sequence. First, the N-terminal residues were used for predicting antibacterial peptides using Artificial Neural Network (ANN), Quantitative Matrices (QM) and support vector machine (SVM), which resulted in an accuracy of 83.63%, 84.78% and 87.85%, respectively. Then, the C-terminal residues were used for developing prediction methods, which resulted in an accuracy of 77.34%, 82.03% and 85.16% using ANN, QM and SVM, respectively. Finally, ANN, QM and SVM models were developed using N and C terminal residues, which achieved an accuracy of 88.17%, 90.37% and 92.11%, respectively. All the models developed in this study were evaluated using five-fold cross validation technique. These models were also tested on an independent or blind dataset. Conclusion: Among antibacterial peptides, there is preference for certain residues at N and C termini, which helps to demarcate them from non-antibacterial peptides. Both the termini play a crucial role in imparting the antibacterial property to these peptides. Among the methods developed, SVM shows the best performance in predicting antibacterial peptides followed by QM and ANN, in that order. AntiBP (Antibacterial peptides) will help in discovering efficacious antibacterial peptides,

关键词： support vector machine Artificial Neural Network Antimicrobial Peptide support vector machine model Independent Dataset

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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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Utilizing support vector machine in real-time crash risk evaluation

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ACCIDENT ANALYSIS AND PREVENTION 2013年第Mar.期51卷 252-259页

作者： Yu, Rongjie Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32826 USA

Real-time crash risk evaluation models will likely play a key role in Active Traffic Management (ATM). models have been developed to predict crash occurrence in order to proactively improve traffic safety. Previous real-time crash risk evaluation studies mainly employed logistic regression and neural network models which have a linear functional form and over-fitting drawbacks, respectively. Moreover, these studies mostly focused on estimating the models but barely investigated the models' predictive abilities. In this study, support vector machine (SVM), a recently proposed statistical learning model was introduced to evaluate real-time crash risk. The data has been split into a training dataset (used for developing the models) and scoring datasets (meant for assessing the models' predictive power). Classification and regression tree (CART) model has been developed to select the most important explanatory variables and based on the results, three candidates Bayesian logistic regression models have been estimated with accounting for different levels unobserved heterogeneity. Then SVM models with different kernel functions have been developed and compared to the Bayesian logistic regression model. model comparisons based on areas under the ROC curve (AUC) demonstrated that the SVM model with Radial-basis kernel function outperformed the others. Moreover, several extension analyses have been conducted to evaluate the effect of sample size on SVM models' predictive capability;the importance of variable selection before developing SVM models;and the effect of the explanatory variables in the SVM models. Results indicate that (1) smaller sample size would enhance the SVM model's classification accuracy, (2) variable selection procedure is needed prior to the SVM model estimation, and (3) explanatory variables have identical effects on crash occurrence for the SVM models and logistic regression models. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： support vector machine model Bayesian logistic regression Real-time crash risk evaluation Mountainous freeway safety

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