• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.221-222
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• 2005

In this paper, application of NN (Neural Network) as a method of pattern discrimination of PD(partial discharge) which occurs at the stator coil of traction motor was studied. For PD data acquisition, three defective models are manufactured such as internal discharge model, slot discharge model and surface discharge model. PD data for recognition were acquired from PD detector and DAQ board which is able to analysis the PD signal and perform the pattern discrimination. Statistical distributions and parameters are calculated to discriminate PD sources. And also these statistical distribution parameters are applied to classify PD sources by BP and has good recognition rate on the discharge sources.

• Journal of Power Electronics
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• v.12 no.1
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• pp.1-9
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• 2012

This study investigates an application of the Hamming network-dual extended Kalman filter (DEKF) based on pattern recognition for high accuracy state-of-charge (SOC)/capacity estimation and state-of-health (SOH) prediction at various temperatures. The averaged nine discharging/charging voltage-temperature (DCVT) patterns for ten fresh Li-Ion cells at experimental temperatures are measured as representative patterns, together with cell model parameters. Through statistical analysis, the Hamming network is applied to identify the representative pattern that matches most closely with the pattern of an arbitrary cell measured at any temperature. Based on temperature-checking process, model parameters for a representative DCVT pattern can then be applied to estimate SOC/capacity and to predict SOH of an arbitrary cell using the DEKF. This avoids the need for repeated parameter measuremet.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1231-1234
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• 2006

In this study, we detected electromagnetic waves generated in an enclosed switchgear and applied various statistical methods for detecting signals. We calculated the various statistical factors via the appropriate statistical methods. Further, we used these statistics to recognize the characteristics for each pattern by identifying the partial discharge in each case for normal, proceeding and abnormal states. The characteristics of electromagnetic wave patterns occurred in various states at electric power facilities and were used as an output variable for more efficient diagnosis. In this paper, we confirmed that the pattern of partial discharge signal can be used as one of the factors used to analyze the insulation state and to consider while estimating diagnosis of insulation states by recognizing the signal pattern to intelligence. We will utilize the proposed diagnosis method to determine insulation degradation states.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• Journal of Korean society of Dental Hygiene
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• v.10 no.2
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• pp.335-344
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• 2010

Objectives : Targeting Korean adults, the food pattern are grasped. And, its correlation with oral disease is analyzed. In order to offer basic data to developing the nutritional policy and nutritional program for the future prevention from oral disease, a research was conducted by utilizing the Korean National Health and Nutrition Examination Survey 2005(the 3rd term). Methods : The subjects in this study were 6,526 adults in more than fully 19 years among 9,047 persons who participated in the food intake survey out of those who completed the health interview survey. The statistical analysis was analyzed by using SPSS 12.0 program. Results : 1. As a result of Group Analyzing was indicated to dangerous-type food pattern and protection-type food pattern. 2. As a result of analyzing the answers for having dental caries in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had not the statistically significant difference. 3. As a result of analyzing the answers for having periodontal disease in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had the statistically significant difference(p<0.05). 4. As a result of analyzing the food pattern factors that have influence upon both major oral illnesses in the annually personal recognition, the person, who has the dangerous-type food pattern, had high risk level of the periodontal disease in the annually personal recognition. Conclusions : In the above results, as a result of surveying and analyzing importance of the food pattern in the incidence of both major oral illnesses, it is considered that there will be necessity of continuing to research into developing the nutritional policy and nutritional program in order to prevent oral illness in the future.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.719-724
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• 1998

This paper presents a systematic developement of a formal approach to inference in approximate reasoning. We introduce some measures of similarity and discuss their properties. Using the concept of similarity index we formulate two methods for inferring from vague knowledge. In order to illustrate the effectiveness of the proposed technique we use it to develop a vowel recognition system.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1735-1741
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• 2011

The safety of station structures among railway infrastructures should be considered as a top priority because hundreds of thousands passengers a day take a subway. The station structures, which have been being operated since the 1970s, are especially vulnerable to the earthquake and long-term vibrations such as ambient train vibrations as well. This is why the structural-health monitoring system of station structures should be required. For these reason, Korean government has made an effort to develop the structural health-monitoring system of them, which can evaluate the health-state of station structures as well as can monitor the vulnerable structural members in real-time. Then, through the monitoring system, the vulnerable structural members could be retrofitted. For the development of health-state evaluation method for station structures with the real-time sensing data measured in the fields, authors carried out the numerical simulations to develop evaluation algorithms based on statistical pattern recognition techniques. In this study, the dynamic behavior of Chungmuro station in Seoul was numerically analyzed and then critical members were chosen. Damages were artificially simulated at the selected critical members of the numerical model. And, the supervised and unsupervised learning based pattern recognition algorithms were applied to quantify and localize the structural defects.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.1
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• pp.31-36
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• 2003

This paper describes the quantitative analysis and the pattern recognition of partial discharge signals generated by particles in GIS. Four states of particles were simulated in this paper. Partial discharge signals from each state was measured and the Ф-Q-N distribution of partial discharge signals was displayed and then the Ф-Q, the Ф-Qm, the Ф-N and the Q-N distribution were displayed. Each distribution can be quantitatively represented by statistical parameters and the parameters were used for input data of pattern recognition. As the results, it was found that the forms of each distribution were different according to the particle states. Recognition rate using neural network was about 92〔%〕 and the more input data number, the more accurate results.