Optimal Feature Vector Research Articles

Optimal feature selection for classification of the power quality events using wavelet transform and least squares support vector machines

In this paper, a new optimal feature selection based power quality event recognition system is proposed for the classification of power quality events. While Apriori algorithm is capable of processing categorical data, an effective feature vector, which represents distinctive features of digital power quality event data, has been obtained by means of the proposed k-means based Apriori algorithm feature selection approach. The proposed k-means based Apriori algorithm feature selection approach is presented with a power quality event recognition system. In the power quality event recognition system, normalization and segmentation processes have been applied to three-phase event voltage signals. Using 9-level multiresolution analysis, wavelet transform coefficients of the event signals have been obtained. By applying nine different feature extraction processes to these coefficients, a 90 dimensional feature vector belonging to three-phase event voltage signals has been extracted. Optimal feature vector has been obtained by applying the k-means based Apriori algorithm feature selection approach to the obtained feature vector, which has been applied as the last step to the input of the least squares support vector machine classifier and recognition performance results have been obtained. Real power quality event data have been used to evaluate the performance of the proposed feature selection approach and power quality event recognition system. According to the results, the proposed k-means based Apriori algorithm feature selection approach and power quality event recognition system are efficient, reliable and applicable and classify three-phase event types with a high degree of accuracy.

Feature Vector Selection Method Using Mahalanobis Distance for Diagnostics of Analog Circuits Based on LS-SVM

Multi-fault diagnosis for analog circuits based on support vector machine (SVM) usually used a single feature vector to train all binary SVM classifier. In fact, each binary SVM classifier has different classification accuracy for different feature vectors. However, no one has discussed the optimal or near-optimal feature vector selection problem. Based on Mahalanobis distance, a near-optimal feature vector selection method has been proposed for diagnostics of analog circuits using the least squares SVM (LS-SVM). The selection problems of wavelet types, wavelet decomposition level, and normalization methods have been also discussed. Two filters with parametric faults and a nonlinear half-wave rectifier with hard and parametric faults were used as circuits under test (CUTs). The simulation results showed the following: (1) the accuracies using the feature vector with the maximum MD were better than the average accuracies using all the feature vectors, and were better than most accuracies using a single feature vector. But the computation time using the MD method was an order of magnitude larger than that using a single feature vector; (2) Most the diagnostic accuracies using the maximum MD method were near to the optimal accuracies using the exhaustive method while the computation time was reduced about 20–50 % in comparision to the exhaustive method; (3) the Haar wavelet was the best choice among Daubechie’s wavelet family for all CUTs’ diagnosis; (4) only non-normalization, all-normalization, and part-normalization methods are necessary to be considered for feature vector normalization. The proposed method can obtain a near-optimal diagnostic accuracy in a reasonable time, which is beneficial for analog IC or circuits testing and diagnosis.

An analysis of content-based classification of audio signals using a fuzzy c-means algorithm

Content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FCM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FCM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

잡음 환경에서의 유도 전동기 고장 검출 및 분류를 위한 강인한 특징 벡터 추출에 관한 연구

유도 전동기는 항공 산업, 자동차 산업 등의 산업 현장에서 중요한 역할을 하고 있으며, 이러한 유도 전동기의 고장으로 인한 피해를 최소화하기 위해 유도 전동기의 고장 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 이에 본 논문에서는 정상 및 각종 비정상 상태의 유도 전동기 진동 신호에 대해 부분 자기 상관(partial autocorrelation, PARCOR) 계수, 로그 스펙트럼 파워(log spectrum powers, LSP), 캡스트럼 계수의 평균값(cepstrum coefficients mean, CCM), 멜 주파수 캡스트럼 계수(mel-frequency cepstrum coefficient, MFCC)의 네 가지 특징 벡터를 신경 회로망의 입력으로 사용하여 유도 전동기의 고장을 검출하고 분류하였다. 고장 분류를 위한 최적의 특징 벡터를 찾기 위해 추출하는 특징의 수를 2에서 20으로 바꾸어 가며 분류 성능을 평가한 결과 CCM을 제외한 나머지의 경우 5~6의 특징만으로 분류 정확도가 거의 100%에 가까운 결과를 보였다. 또한 본 논문에서는 실제 산업 현장에서 진동 신호 취득 시 포함될 수 있는 잡음을 고려하여 취득한 신호에 백색 잡음(white Gaussian noise)을 인위적으로 추가하여 실험한 결과 LSP, PARCOR, MFCC 순으로 잡음 환경에 강인한 특징 벡터임을 확인할 수 있었다. Induction motors play a vital role in aeronautical and automotive industries so that many researchers have studied on developing a fault detection and classification system of an induction motor to minimize economical damage caused by its fault. With this reason, this paper extracts robust feature vectors from the normal/abnormal vibration signals of the induction motor in noise circumstance: partial autocorrelation (PARCOR) coefficient, log spectrum powers (LSP), cepstrum coefficients mean (CCM), and mel-frequency cepstrum coefficient (MFCC). Then, we classified different types of faults of the induction motor by using the extracted feature vectors as inputs of a neural network. To find optimal feature vectors, this paper evaluated classification performance with 2 to 20 different feature vectors. Experimental results showed that five to six features were good enough to give almost 100% classification accuracy except features by CCM. Furthermore, we considered that vibration signals could include noise components caused by surroundings. Thus, we added white Gaussian noise to original vibration signals, and then evaluated classification performance. The evaluation results yielded that LSP was the most robust in noise circumstance, then PARCOR and MFCC followed by LSP, respectively.

Optimal EEG Feature Extraction using DWT for Classification of Imagination of Hands Movement

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student’s-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

Adaptive Road Crack Detection System by Pavement Classification

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

Face recognition using difference vector plus KPCA

In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram–Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging.

F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ 2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ 2(F)/F 2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

A Hybrid KPCA-PNN Approach to Condenser Fault Diagnosis with a Particle Swarm Optimizer

Diagnosis of condenser abnormality is very important for turbine generator reliability. This paper presents a novel approach for condenser fault diagnosis based on kernel principle component analysis (KPCA) and probabilistic neural network (PNN). KPCA is applied to PNN for feature extraction. It firstly maps data from the original input space into high dimensional feature space via nonlinear kernel function and then extract optimal feature vector as the inputs of PNN to solve condenser fault classification problems. A global optimizer, particle swarm optimizer (PSO), is employed to optimize the parameters of PNN to improve fault classification accuracy. The experimental results show that the proposed approach has a better ability in terms of diagnosis accuracy and computational efficiency compared with a number of popular fault diagnosis techniques.

Highly Sensitive Computer Aided Diagnosis System for Breast Tumor Based on Color Doppler Flow Images

A computer-aided diagnosis (CAD) system for breast tumor based on color Doppler flow images is proposed. Our system consists of automatic segmentation, feature extraction, and classification of breast tumors. First, the B-mode grayscale image containing anatomical information was separated from a color Doppler flow image (CDFI). Second, the boundary of the breast tumor was automatically defined in the B-mode image and then morphologic and gray features were extracted. Third, an optimal feature vector was created using K-means cluster algorithm. Then a back-propagation (BP) artificial neural network (ANN) was used to classify breast tumors as benign, malignant or uncertain. Finally, the blood flow feature was extracted selectively from the CDFI, and was used to classify the uncertain tumor as benign or malignant. Experiments on 500 cases show that the proposed system yields an accuracy of 100% for the malignant and 80.8% for the benign classification. Comparing with other systems, the advantage of our system is that it has a much lower percentage of malignant tumor misdiagnosis.

S1601-1-3 ニューラルネットワークを用いた食事音の分析(生物医学工学における計測と制御(1),社会変革を技術で廻す機械工学)

In recent years, an increasing number of people suffer from lifestyle diseases, such as metabolic syndrome. Eating habits monitoring is an important parameter for life-style diseases prevention. Since, we have been developing a system to monitor meal time activities. Our system consists of two bone conduction microphones connected to a portable IC recorder that collects internal body sound data. In the meal time activities differentiation process, we adopted a wavelet function for the feature extraction. We extracted 70 feature vectors from coefficients of discrete wavelet transformation, then we selected the optimal feature vectors set using minimal-redundancy-maximal-relevance criterion (mRMR), and finally we used probabilistic neural network (PNN) to classify meal-related activities. Experiments were carried on sound data from six persons. Our model proved to achieve better classification accuracy, and selected features to be independent from individual differences.

Optimal feature selection for the assessment of vocal fold disorders

Unilateral vocal fold paralysis, vocal fold polyp, and vocal fold nodules are the most common types of neurogenic and organic vocal disorders. This article aims to distinguish these types of vocal diseases into four different classes for the purpose of automatic screening. Firstly, the reconstructed signal at each wavelet packet decomposition sub-band in five levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, to find a discriminant feature vector, three different methods have been applied: Davies–Bouldin (DB) criteria, genetic algorithm (GA) with the fitness functions of support vector machine's (SVM) and k-nearest neighbor's (KNN) recognition rates. Finally, obtained feature vectors have been passed on to SVM and KNN classifiers. The results show that a feature vector of length 12 obtained by the optimization method of GA with the fitness function of SVM's recognition rate fed to SVM classifier achieves the highest classification accuracy of 91%. Furthermore, nonlinear features play an important role in pathological voice classification by participating rate of approximately 67% in the optimal feature vector.

Direct kernel neighborhood discriminant analysis for face recognition

A nonlinear face recognition technique based on neighborhood preserving discriminant analysis (NPDA) is proposed. The kernel trick is adopted to allow the efficient computation of local Fisher discriminant in high-dimensional feature space. Moreover, a direct solution for obtaining the optimal feature vectors in feature space is presented which can preserve the most discriminative information. The proposed algorithm is evaluated on the UMIST database, the ORL database and the FERET database by using six different methods. Experiments show that consistent and promising results are obtained.

Pattern classification by using improved wavelet Compressed Zernike Moments

In this paper, an improved Feature Extraction Method (FEM), which selects discriminative feature sets able to lead to high classification rates in pattern recognition tasks, is presented. The resulted features are the wavelet coefficients of an improved compressed signal, consisting of the Zernike moments amplitudes. By applying a straightforward methodology, it is aimed to construct optimal feature vectors in the sense of vector dimensionality and information content for classification purposes. The resulting surrogate feature vector is of lower dimensionality than the original Zernike moment feature vector and thus more appropriate for pattern recognition tasks. Appropriate validation tests have been arranged, in order to investigate the performance of the proposed algorithm by measuring the discriminative power of the new feature vectors despite the information loss.

A neural network based approach for protein structural class prediction

The paper provides an alternative approach to protein structural class prediction employing artificial neural network. Existing works on protein structural class prediction are computationally intensive. The method employs SOFM for extraction of representative feature vectors, for the four different structural classes and then uses Principal Component Analysis for finding optimum feature vector dimension. Nearest neighborhood classification technique is finally utilised; to classify these protein datapoints to their respective classes. The proposed work presented in this paper, maintains the same level of classification accuracy in minimum computation time, as it employs most prominent and reduced number of feature set for classification.

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.

Combined Statistical and Multiscale View on Ultrasonic Liver Images for Characterization

Combined Statistical and Multiscale View on Ultrasonic Liver Images for Characterization

FRIP: a region-based image retrieval tool using automatic image segmentation and stepwise Boolean AND matching

We present our region-based image retrieval tool, finding region in the picture (FRIP), that is able to accommodate, to the extent possible, region scaling, rotation, and translation. Our goal is to develop an effective retrieval system to overcome a few limitations associated with existing systems. To do this, we propose adaptive circular filters used for semantic image segmentation, which are based on both Bayes' theorem and texture distribution of image. In addition, to decrease the computational complexity without losing the accuracy of the search results, we extract optimal feature vectors from segmented regions and apply them to our stepwise Boolean AND matching scheme. The experimental results using real world images show that our system can indeed improve retrieval performance compared to other global property-based or region-of-interest-based image retrieval methods.

Multiresolution S-Transform-Based Fuzzy Recognition System for Power Quality Events

The paper proposes a novel fuzzy pattern recognition system for power quality disturbances. It is a two-stage system in which a mulitersolution S-transform is used to generate a set of optimal feature vectors in the first stage. The multiresolution S-transform is based on a variable width analysis window, which changes with frequency according to a user-defined function. Thus, the resolution in time or the related resolution in frequency is a general function of the frequency and two parameters, which can be chosen according to signal characteristics. The multiresolution S-transform can be seen either as a phase-corrected version of the wavelet transform or a variable window short time Fourier transform that simultaneously localizes both real and imaginary spectra of the signal. The features obtained from S-transform analysis of the power quality disturbance signals are much more amenable for pattern recognition purposes unlike the currently available wavelet transform techniques. In stage two, a fuzzy logic-based pattern recognition system is used to classify the various disturbance waveforms generated due to power quality violations. The fuzzy approach is found to be very simple and classification accuracy is more than 98% in most cases of power quality disturbances.

Proposed wavelet-neurofuzzy combined system for power quality violations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis.