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Wavelet packet decomposition with motif patterns for rolling bearing fault diagnosis under variable working loads

JOURNAL OF VIBRATION AND CONTROL 2025年第3-4期31卷 516-527页

作者： Wang, Qiang Xu, Feiyun Ma, Tianchi Southeast Univ Sch Mech Engn 2 Southeast Univ Rd Nanjing 211189 Peoples R China

Bearing intelligent diagnosis based on signal processing has been a hot research topic. However, due to the different data distribution caused by the variable working loads, the model learned from source domain has poor performance in target domain. To solve this problem, a feature extraction method named Wavelet Packet Decomposition with Motif Patterns (WPDMP) is proposed. Firstly, multiscale signals are obtained using wavelet packet decomposition;then, the MP features of these multiscale signals and the original signal are extracted;finally, these MPs are combined as input vector of support vector classification (SVC) for fault identification. Compared with other methods, the proposed method has extraordinary superiority for unlabeled target domain fault diagnosis. In addition, the feature visualization results show that the proposed model can extract domain invariant features, so the proposed model has considerable research prospects.

关键词： wavelet packet decomposition motif patterns support vector classification variable working load intelligent fault diagnosis

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Misfire-misfuel classification using support vector machines

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2007年第D9期221卷 1183-1195页

作者： Gani, E. Manzie, C. Univ Melbourne Dept Mech & Mfg Engn Melbourne Vic 3010 Australia

This paper proposes the use of support vector machines to perform classification between different types of missed combustion event in a six-cylinder engine. On-board diagnostics regulations require the detection of missed combustion events, which is possible through interpretation of crankshaft speed information. However, current approaches provide no information on the actual cause of the event, in particular whether it was caused by a misfuel (absence of fuel) or a misfire (absence of spark) event. Whilst the impact on the environment and emission treatment systems due to misfuel is minimal, misfire events are detrimental to both. Consequently information regarding the causes of missing combustion events potentially allows the development of unique recovery strategies particular to the source of the problem. In this paper, an approach is proposed that will provide the potential for, firstly, detection of a missing combustion event and, secondly, real-time classification of the event into either misfuel or misfire events using feedback from a heated universal exhaust gas oxygen sensor. In order to evaluate the potential of such a system in an engine control unit, a computational complexity measure is also presented. [PUBLICATION ABSTRACT]

关键词： misfire-misfuel classification misfire misfuel internal combustion engine support vector machines support vector classification

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Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data

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REMOTE SENSING 2015年第8期7卷 10668-10688页

作者： Suess, Stefan van der Linden, Sebastian Okujeni, Akpona Leitao, Pedro J. Schwieder, Marcel Hostert, Patrick Humboldt Univ Dept Geog D-10099 Berlin Germany

Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis Program (EnMAP), frequent and area-wide mapping of natural environments by means of high quality hyperspectral data becomes possible. However, the amplified spectral mixing due to the sensor's ground sampling distance of 30 m on the one hand and the patterns of natural landscapes in the form of gradual transitions between different land cover types on the other require special attention. Based on simulated EnMAP data, this study focuses on mapping shrub vegetation along a landscape gradient of shrub encroachment in a semi-arid, natural environment in Portugal. We demonstrate how probability outputs from a support vector classification (SVC) model can be used to extend a hard classification by information on shrub cover fractions. This results in a more realistic representation of gradual transitions in shrub vegetation maps. We suggest a new, adapted approach for SVC parameter selection: During the grid search, parameter pairs are evaluated with regard to the prediction of synthetically mixed test data, representing shrub to non-shrub transitions, instead of the hard classification of original, discrete test data. Validation with an unbiased, equalized random sampling shows that the resulting shrub-class probabilities from adapted SVC more accurately represent shrub cover fractions (mean absolute error/root mean squared error of 16.3%/23.2%) compared to standard SVC (17.1%/29.5%). Simultaneously, the discrete classification output was considerably improved by incorporating synthetic mixtures into parameter selection (averaged F1 accuracies increased from 72.4% to 81.3%). Based on our findings, the integration of synthetic mixtures into SVC parameterization allows the use of

关键词： class probabilities EnMAP imaging spectroscopy Portugal shrub ecosystem sub-pixel mapping support vector classification

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Structure-activity relationship study of trifluoromethylketones: inhibitors of insect juvenile hormone esterase

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013年第6期24卷 747-765页

作者： Doucet, J. P. Doucet-Panaye, A. Devillers, J. Univ Paris 07 ITODYS UMR 7086 Paris France CTIS Rillieux La Pape France

The juvenile hormone esterase (JHE) regulates juvenile hormone titre in insect hemolymph during its larval development. It has been suggested that JHE could be targeted for use in insect control. This enzyme can also be considered as involved in the phenomenon of endocrine disruption by xenobiotics in beneficial insects. Consequently, there is a need to know the characteristics of the molecules able to act on the JHE. Trifluoromethylketones (TFKs) are the most potent JHE inhibitors found to date and different quantitative structure-activity relationships (QSARs) have been derived for this group of chemicals. In this context, a set of 181 TFKs (118 active and 63 inactive compounds), tested on Trichoplusia ni for their JHE inhibition activity and described by physico-chemical descriptors, was split into different training and test sets to derive structure-activity relationship (SAR) models from support vector classification (SVC). A SVC model including 88 descriptors and derived from a Gaussian kernel was selected for its predictive performances. Another model computed only with 13 descriptors was also selected due to its mechanistic interpretability. This study clearly illustrates the difficulty in capturing the essential structural characteristics of the TFKs explaining their JHE inhibitory activity.

关键词： juvenile hormone esterase trifluoromethylketones SAR model support vector classification inhibitory activity

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Modeling blast furnace productivity using support vector machines

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第9-12期52卷 989-1003页

作者： Ghosh, Abhijit Majumdar, Sujit K. Indian Stat Inst Kolkata India

Productivity of a modern generation blast furnace was modeled with the help of a leading supervised learning tool viz. support vector Machines in the form of (1) minimum error, maximum margin classification function in binary setting of productivity classes (low/high) and (2) the class-specific regression functions for real values of productivity based on epsilon sensitive loss function and minimum regulated risk. The SVMs were trained with large number data-points each of which consisted of a setting of 21 critical input parameters of blast furnace, corresponding productivity value observed, and the productivity class (low/high) attributed. During the training session of the SVMs, the vectors of critical input parameters were required to be mapped into high-dimensional feature space via Radial basis kernel as function and the optimum SVM-RBF classifying function with chosen setting of its hyperparameters that had good generalization property was found using quadratic optimization. The SVM-RBF classifying function could be used to predict the class of productivity (low/high) for any given setting of the critical input parameters. Class-specific SVM-RBF regression models were also developed for both low as well as high-productivity classes and these models could be used to predict real value of productivity for any given setting of the critical input parameters. The SVM-RBF regression model fitted to the high-productivity class was subjected to constrained nonlinear optimization treatment to find the optimum setting of the critical input parameters that gave maximum productivity. The optimum setting of the critical parameters could be used as the target setting obtaining high productivity in the blast furnace.

关键词： Blast furnace productivity Supervised learning support vector classification support vector regression Nonlinear optimization Maximum productivity

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Combined feature extraction method for classification of EEG signals

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NEURAL COMPUTING & APPLICATIONS 2017年第11期28卷 3153-3161页

作者： Zhang, Yong Ji, Xiaomin Liu, Bo Huang, Dan Xie, Fuding Zhang, Yuting Liaoning Normal Univ Sch Comp & Informat Technol 1Liushu South St Dalian 116081 Liaoning Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Liaoning Normal Univ Sch Urban & Environm Sci Dalian 116029 Peoples R China

classification of electroencephalogram (EEG) signals is an important task in brain-computer interfaces applications. This paper combines autoregressive (AR) model and sample entropy and presents a combination strategy of feature extraction. Each feature vector obtained from the combination strategy contains two parts: AR coefficients and sample entropy values. In the classification phase, this paper employs support vector machine (SVM) with RBF kernel as the classifier. The proposed method is used in the five mental task experiments. Experimental results show that the SVM classifier performs very well in classifying EEG signals using the combination strategy of feature extraction. It obtains a better accuracy in comparison with AR-based method. The results also indicate that the combination strategy of AR model and sample entropy can effectively improve the classification performance of EEG signals.

关键词： EEG signal Feature extraction Autoregressive model Sample entropy support vector classification Brain-computer interfaces

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classification models for predicting the antimalarial activity against Plasmodium falciparum

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020年第4期31卷 313-324页

作者： Liu, Q. Deng, J. Liu, M. Hunan Inst Engn Hunan Prov Key Lab Environm Catalysis & Waste Reg Coll Mat & Chem Engn Xiangtan Peoples R China Huizhou Univ Sch Chem & Mat Engn Huizhou Peoples R China

support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds). For the SVM model, its prediction accuracies are 89.5% for the training set and 87.3% for the test set. For the GRNN model, the prediction accuracies for the two sets are 99.7% and 88.9%, respectively. Both SVC and GRNN models have better prediction ability than the classification model based on binary logistic regression (BLR) analysis. Compared with previously published classification models both SVC and GRNN models are satisfactory in predicting antimalarial activities of compounds with in addition of fewer descriptors.

关键词： Antimalarial binary logistic regression classification model general regression neural network support vector classification

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classification of nasopharyngeal cell lines (C666-1, CNE2, NP69) via Raman spectroscopy and decision tree

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VIBRATIONAL SPECTROSCOPY 2015年 80卷 24-29页

作者： Chen, Yang Su, Ying Ou, Lin Zou, Changyan Chen, Zhong Xiamen Univ Dept Elect Sci Fujian Prov Key Lab Plasma & Magnet Resonance Xiamen 361005 Peoples R China Fujian Prov Tumor Hosp Radiobiol Lab Fuzhou 350014 Peoples R China Fujian Normal Univ Minist Educ Key Lab Optoelect Sci & Technol Med Fuzhou 350007 Peoples R China

Researchers have demonstrated that Raman spectroscopy can be used for characterization of tumor cells with excellent spatial resolution. However, performance evaluation of different algorithms in classifying multiclass of Raman spectra has not been reported yet. In this work, we present Raman spectra of nasopharyngeal carcinoma and nasopharyngeal normal cell lines. Combined with student's t-test and several multivariate approaches, including decision tree, support vector classification, and linear discriminant analysis, our work shows that the relative content of two histological abnormality sensitive bands at 1449 and 1658 cm(-1) in tumor cells is significantly different from that of normal cells (p = 0.0132), and can be a biomarker to classify these cells. This difference is confirmed by importance analyses in the decision tree model. Furthermore, performances of statistical methods are compared with one another to explore the ability in classification. Results show that the decision tree can be more capable for classification between tumorous and normal cell lines with sensitivity and specificity of 99.0% and 96.9%, respectively. Findings of this work further support our previous work and indicate that the decision tree performs more robustly in cell classification. Our work will prove helpful to the early diagnosis of nasopharyngeal carcinoma, and will indicate the decision tree to be the primary algorithm in tumor-cell classification. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Raman spectroscopy Nasopharyngeal carcinoma Cell Decision tree support vector classification

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Adaptive 3D facial action intensity estimation and emotion recognition

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1446-1464页

作者： Zhang, Yang Zhang, Li Hossain, M. A. Northumbria Univ Fac Engn & Environm Dept Comp Sci & Digital Technol Computat Intelligence Res Grp Newcastle Upon Tyne NE1 8ST Tyne & Wear England Anglia Ruskin Univ Fac Sci & Technol Anglia Ruskin IT Res Inst Cambridge CB1 1PF England

Automatic recognition of facial emotion has been widely studied for various computer vision tasks (e.g. health monitoring, driver state surveillance and personalized learning). Most existing facial emotion recognition systems, however, either have not fully considered subject-independent dynamic features or were limited to 2D models, thus are not robust enough for real-life recognition tasks with subject variation, head movement and illumination change. Moreover, there is also lack of systematic research on effective newly arrived novel emotion class detection. To address these challenges, we present a real-time 3D facial Action Unit (AU) intensity estimation and emotion recognition system. It automatically selects 16 motion-based facial feature sets using minimal-redundancy maximal-relevance criterion based optimization and estimates the intensities of 16 diagnostic AUs using feedforward Neural Networks and support vector Regressors. We also propose a set of six novel adaptive ensemble classifiers for robust classification of the six basic emotions and the detection of newly arrived unseen novel emotion classes (emotions that are not included in the training set). A distance-based clustering and uncertainty measures of the base classifiers within each ensemble model are used to inform the novel class detection. Evaluated with the Bosphorus 3D database, the system has achieved the best performance of 0.071 overall Mean Squared Error (MSE) for AU intensity estimation using support vector Regressors, and 92.2% average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers. In comparison with other related work, our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database. Moreover, in on-line real-time evaluation with real human subjects, the proposed system also shows superior real-time performance with 84% recognition accuracy and great flexibility and adaptation for newl

关键词： Facial emotion recognition Action unit intensity estimation Adaptive ensemble classifiers Complementary Neural Networks support vector Regression support vector classification

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Soft Computing Based Discriminator Model for Glaucoma Diagnosis

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Computer Systems Science & Engineering 2022年第9期42卷 867-880页

作者： Anisha Rebinth S.Mohan Kumar Department of Information Science and Engineering NHCEVisvesvaraya Technological UniversityBangaloreIndia Department of Computer Science and Engineering Nagarjuna College of Engineering and TechnologyBangaloreIndia

In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based support vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.

关键词： Glaucoma support vector classification clustering technique spatial domain and frequency domain features

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