A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

scholarly journals Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Feature Identification ◽  
Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

Feature Extraction for Classification of Proteomic Profile

2013 ◽  
Vol 756-759 ◽  
pp. 4576-4580
Author(s):  
Xiao Li Yang ◽  
Qiong He ◽  
Fen Yang
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Proteomic Profile ◽  
Fisher Discriminant Analysis ◽  
Discrimination Ability ◽  
Classification Information

This work studies on feature extraction for classification of proteomic profile. We evaluated four methods, including principal component analysis (PCA), independent component analysis (ICA), locally linear embedding (LLE) and weighted maximum margin criterion (WMMC). PCA, ICA and LLE extract features based on traditional low-dimension map technique. Comparatively, WMMC extracts features according to classification goal. To study classification performance of PCA, ICA, LLE and WMMC in detail, we used two well known classification methods, support vector machine (SVM) and Fisher discriminant analysis (FDA), to classify profiles. The results show WMMC having relatively good performance due to its prediction accuracy, sensitivity and specificity for diagnosis; it can correctly identify features with high discrimination ability from high-dimensional proteomic profile. When feature set size was reduced less than 10, PCA, ICA and LLE lose a lot of classification information, and the prediction accuracies are less than 90%. However, WMMC can extract most classification information. Its prediction accuracies, sensitivities and specificities are more than 95%. Obviously, WMMC is more suitable to proteomic profile classification. For classifier, FDA is sensible to feature extraction.

scholarly journals Arrhythmia recognition and classification using kernel ICA and higher order spectra

2018 ◽  
Vol 7 (2) ◽  
pp. 256 ◽  
Author(s):  
Raghu Nanjundegowda ◽  
Vaibhav Meshram
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Linear Structure ◽  
Component Analysis ◽  
Higher Order ◽  
Support Vector ◽  
Free Data ◽  
Kernel Independent Component Analysis ◽  
Non Linear ◽  
Ecg Data

Electrocardiogram (ECG) is one of the monitoring methodology for the identification of arrhythmia disease. The conventional methodologies of arrhythmia identification are based on morphological features or certain transformation technique. These conventional techniques are partially successful in arrhythmia identification, because it treats heart as a linear structure. In this paper, ECG based arrhythmia identification is assessed by employing MIT-BIH arrhythmia dataset. The proposed approach contains two major steps: feature extraction and classification. Initially, a combination of non-linear and linear feature extraction is carried-out using Principal Component Analysis (PCA), Kernel Independent Component Analysis (KICA) and Higher Order Spectrum (HOS) for achieving optimal feature subsets. The linear experiments on ECG data achieves high performance in noise free data and the non-linear experiments distinguish the ECG data more effectively, extract hidden information and also helps to attain better performance under noisy conditions. After finding the feature information, a binary classifier Support Vector Machine (SVM) is employed for classifying the normality and abnormality of arrhythmia. In experimental analysis, the proposed approach distinguishes the normality and abnormality of arrhythmia ECG signals in terms of specificity, sensitivity and accuracy. Experimental outcome shows that the proposed approach improved accuracy in arrhythmia detection up to 0.5-1% compared to the existing methods: neural network and SVM based radial basis function.

scholarly journals The Classification of Primitive-Shaped Patterns by Using Principal Component Analysis Method

2019 ◽  
Vol 20 (2) ◽  
pp. 12
Author(s):  
IGA Widagda ◽  
Hery Suyanto
Keyword(s):  
Principal Component Analysis ◽  
Predictive Accuracy ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Moment Invariant ◽  
Pca Method ◽  
The Moment

Abstrak – The recognition or classification of patterns is a major problem in computer vision. Many methods have been applied such as: moment invariant, Artificial Neural Networks (ANN), K-mean, Support Vector Machine (SVM) and others. These methods have a few limitations. The moment invariant fashion is highly vulnerable to noise. ANN methods require a long computing time (especially multi-layer ANN) during the training process. On the other hand, the dimensions of the features generated from the methods are relatively high, which requires large storage space (memory). In addition, this leads to the long computing time when the testing process is carried out. Based on these facts, this research makes use of methods that being able to reduce the feature dimensions, namely the Principal Component Analysis (PCA). In the PCA method the dimensions of the sample image are converted to principal components (face space), whose dimensions are much smaller than the dimensions of the sample image itself. Our works exhibit that the PCA method is highly effective in carrying out the pattern classification process. This can be indicated by the relatively high values of Predictive Accuracy, Precision and Recall (close to 1) while the FP Rate is low (close to 0). Moreover, the location of the point coordinates (FP Rate, TP Rate) in ROC graphs is fallen in the upper left region (approaching the perfect classifier region).

Application of Kernel Trick to Fuzzy c-Means with Regularization by K-L Information

2004 ◽  
Vol 8 (6) ◽  
pp. 566-572 ◽  
Author(s):  
Hidetomo Ichihashi ◽  
◽  
Katsuhiro Honda
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Fisher Discriminant Analysis ◽  
Kernel Trick ◽  
Kernel Approach

Support vector machines (SVM), kernel principal component analysis (KPCA), and kernel Fisher discriminant analysis (KFD), are examples of successful kernel-based learning methods. By the addition of a regularizer and the kernel trick to a fuzzy counterpart of Gaussian mixture models (GMM), this paper proposes a clustering algorithm in an extended high dimensional feature space. Unlike the global nonlinear approaches, GMM or its fuzzy counterpart is to model nonlinear structure with a collection, or mixture, of local linear sub-models of PCA. When the number of feature vectors and clusters are n and c respectively, this kernel approach can find up to c × n nonzero eigenvalues. A way to control the number of parameters in the mixture of probabilistic principal component analysis (PPCA) is adopted to reduce the number of parameters. The algorithm provides a partitioning with flexible shape of clusters in the original input data space.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

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