• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.13 no.1
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• pp.40-47
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• 2017

In this paper, we investigate the feasibility of a strategy of failure detection and identification. The point of proposed strategy includes a pattern extraction approach for failure identification using Auto-Associative Kernel Regression (AAKR). We consider a simulation data concerning 605 signals of a Generic Pressurized Water Reactor(GPWR). In the application, the reconstructions are provided by a set of AAKR models, whose input signals have been selected by Correlation Analysis(CA) for the identification of the groups. The failure pattern is extracted by analyzing the residuals of observations and reconstructions. We present the possibility of extraction of patterns for six failure.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.32-43
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• 2002

In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.304-309
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• 2003

In this study, natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.2
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• pp.102-106
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• 2003

This paper describes a new pattern classifier neural network to extract the feature from a letter. The proposed pattern classifier is based on relative distance, which is measure between an input datum and the center of cluster group. So, the proposed classifier neural network is called relative neural network(RNN). According to definitions of the distance and the learning rule, the structure of RNN is designed and the pseudo code of the algorithm is described. In feature extraction of letter, RNN, in spite of deletion of learning rate, resulted in the identical performance with those of winner-take-all(WTA), and self-organizing-map(SOM) neural network. Thus, it is shown that RNN is suitable to extract the feature of a letter.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2108-2111
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• 2003

This paper introduces a new local feature extraction method and image matching method for the localization and classification of targets. Proposed method is based on the block-by-block projection associated with directional pattern of blocks. Each pattern has its own eigen-vertors called as CEBs(Classified Eigen-Blocks). Also proposed block-based image matching method is robust to translation and occlusion. Performance of proposed feature extraction and matching method is verified by the face localization and FLIR-vehicle-image classification test.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1995.05a
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• pp.53-59
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• 1995

In a hot water extraction process, the flow pattern of upper region in a storage tank is a major reason of mixing between hot water and cold water. In this study, the temperature distribution in a storage tank was measured to predict the flow pattern of upper region, and the degree of stratification was analysed to the variables dominating a extraction process. And also, it was found that the degree of stratification improved expecially in a low flow rate in case of using modified distributor I(DMI) as a outlet port type.

• The korean journal of orthodontics
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• v.26 no.4
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• pp.349-358
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• 1996

To extract or not to extract permanent teeth for the correction of malocclusion has been a great debate in the history of orthodontics, and there is a variety of analytic methods and criteria to aid in the diagnosis. Extraction formulas that has been presented are many analytic methods that depend on arch length discrepancy, dental prominence, and skeletal pattern of the each patients. Of these analysis, the most important diagnostic factor is patient's skeletal pattern. Because the behavior of the dentition is closely dependent upon the skeletal pattern of each patient, dentition must be arranged within that person's skeletal frame. EI(Extraction Index) is composed of CF, interincisal angle, and lip position. CF is made of ODI and APDI that differentiate vertical and horizontal component of the skeletal pattern. So, EI not only represents patient's skeletal pattern, but also takes facial appearance into consideration. This study was undertaken to investigate EI and related cephalometric variables on the cephalogram of Korean adolescents which consisted of 153 persons with normal occlusion, harmonious skeleton and pleasing face. The following conclusions were obtained. 1. The mean value of the ODI is $73.5^{\circ}$, APDI $82.5^{\circ}$, CF $156.3^{\circ}$ 2. The mean value of the interincisal angle is $123.6^{\circ}$ 3. The mean distance of upper lip to E-line is 0.0mm, lower lip to E-line is 1.4mm. 4. The mean value of the EI is $153.8^{\circ}$.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.167-174
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• 1998

The ultrasonic test is recognized for its significance as a non-destructive testing method to detect volume defects such as porosity and incomplete penetration which reduce strength in the weld zone. This paper illustrates the defect detection in the weld zone of ferritic carbon steel using ultrasonic wave and the evaluation of pattern recognition by chaotic feature extraction using time series signal of detected defects as data. Shown in the time series data were that the time delay was 4 and the embedding dimension was 6 which indicate the geometric dimension of the subject system and the extent of information correlation. Based on fractal dimension and lyapunov exponent in quantitative chaotic feature extraction, feature value of 2.15, 0.47 is presented for porosity and 2.24, 0.51 for incomplete penetration The precision rate of the pattern recognition is enhanced with these values on the total waveform of defect signal in the weld zone. Therefore, we think that the ultrasonic pattern recognition method of weld zone defects of ferritic carbon steel by ultrasonic-chaotic feature extraction proposed in this paper can boost precision rate further than the existing method applying only partial waveform.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.209-215
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• 2005

In this paper, we describe the answer pattern for definitional question-answering system. The .answer extraction method of a definitional question-answering system is different from the general answer extraction method because it presents the descriptive answer for a definitional question. The definitional answer extraction using the definitional answer pattern can extract the definitional answer correctly without the semantic analysis. The definitional answer pattern is consist of answer pattern, conditional rule and priority to extract the correct definitional answer. We extract the answer pattern from the definitional training corpus and determine the optimum conditional rule using F-measure. Next, we determine the priority of answer patterns using precision and syntactic structure. Our experiments show that our approach results in the precision(0.8207), the recall(0.9268) and the F-measure(0.8705). It means that our approach can be used efficiently for a definitional question-answering system.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.349-357
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• 2007

Named entities (NEs) are important in many Natural Language Processing (NLP) applications, and discovering NE-related relations in texts may be beneficial for these applications. This paper proposes a method to extract the ISA relation between a "named entity" and its category, and an IS-RELATED-TO relation between the category and its related object. Based on the pattern extraction algorithm "Person Category Extraction" (PCE), we extend it for solving our problem. Our experiments on Wall Street Journal (WSJ) corpus show promising results. We also demonstrate a possible application of these relations by utilizing them for semantic search.