• Journal of the Korea Society for Simulation
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• v.21 no.2
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• pp.79-90
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• 2012

In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's $T^2$ Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.97-104
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• 2007

This paper presents the problem of the feature extraction techniques that has been used a content-based analysis, classification and retrieval in audio data and proposes a course of the preprocessing for a new contents-based retrieval methods. Because the feature vector according to sampling value changes, the existing audio data analysis is problem that same music is appraised by other music. Therefore, we propose waveform information extraction method of PCM data for retrieval audio data of various format to contents-based. If this method is used. we can find that audio datas that get into sampling in various format are same data. And it may be applied in contents-based music retrieval system. To verity the performance of the method, an experiment was done feature extraction using STFT and waveform information extraction using PCM data. As a result, we could know that the method to propose is effective more.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.44-52
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• 2002

In this paper, we present an analytical method for decision boundary feature extraction for neural networks. It has been shown that all the features necessary to achieve the same classification accuracy xxxas in the original space can be obtained from the vectors normal to decision boundaries. However, the vector normal to the decision boundary of a neural network has been calculated numerically using a gradient approximation. This process is time-consuming and the normal vector may be inaccurately estimated. In this paper, we propose a method to improve the performance of the previous decision boundary feature extraction for neural networks by accurately calculating the normal vector When the normal vectors are computed analytically, it is possible to reduce the processing time significantly and improve the performance of the previous implementation that employs numerical approximation.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.21-26
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• 2003

A new method to realize 3-dimensional object pattern recognition system using Fourier-based feature extractor has been proposed. The procedure to obtain the invariant feature vector is as follows ; A closed surface is generated by tracing the surface of object using the 3-dimensional polar coordinate. The centroidal distances between object's geometrical center and each closed surface points are calculated. The distance vector is translation invariant. The distance vector is normalized, so the result is scale invariant. The Fourier spectrum of each normalized distance vector is calculated, and the spectrum is rotation invariant. The Fourier-based feature generating from above procedure completely eliminates the effect of variations in translation, scale, and rotation of 3-dimensional object with closed-surface. The experimental results show that the proposed method has a high accuracy.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.122-126
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• 2019

In this paper, we propose a simple and efficient retrieval technique using the feature value of the corner region, which is one of the shape information attributes of images. The proposed algorithm extracts the edges and corner points of the image and rearranges the feature values of the corner regions doubly, and then measures the similarity with the image in the database using the correlation of these feature values as the feature vector. The proposed algorithm is confirmed to be more robust to rotation and size change than the conventional image retrieval method using the corner point.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.111-120
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• 2017

In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.461-472
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• 2005

In this paper, we propose face feature extraction algorithm through stereo image's matching value. The proposed algorithm detected face region by change the RGB color space of skin color information to the YCbCr color space. Applying eye-template from extracted face region geometrical feature vector of feature about distance and lean, nose and mouth between eye extracted. And, Proposed method could do feature of eyes, nose and mouth through stereo image's matching as well as 2D feature information extract. In the experiment, the proposed algorithm shows the consistency rate of 73% in distance within about 1m and the consistency rate of 52%in distance since about 1m.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.786-791
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• 2011

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.

• Speech Sciences
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• v.15 no.3
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• pp.7-15
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• 2008

This paper proposes the method to extract new feature vectors using the difference between the cepstrum for static characteristics and delta cepstrum for dynamic characteristics in speaker recognition (SR). The difference vector (DV) which it proposes from this paper is containing the static and the dynamic characteristics simultaneously at the intermediate characteristic vector which uses the deference between the static and the dynamic characteristics and as the characteristic vector which is new there is a possibility of doing. Compared to the conventional method, the proposed method can achieve new feature vector without increasing of new parameter, but only need the calculation process for the difference between the cepstrum and delta cepstrum. Experimental results show that the proposed method has a good performance more than 2.03%, on average, compared with conventional method in speaker identification (SI).

• Journal of Power Electronics
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• v.19 no.2
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• pp.443-453
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• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.