• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1876-1878
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• 2003

The recognition of PD(Partial Discharge) phenomenon is useful for classification of defects. The distribution of stochastic parameters which consisted of those PD pulses data and pulses train can show discriminable characteristics of PD sources. But it is not sufficient to discriminate among to PD sources. In this paper, we suggests that classification method of PD source by NN(Neural Networks) are good tools for differentiate of those. The learning scheme of NN is (Back Propagation learning algorithm(BP).

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1812-1814
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• 2003

$SF_6$ 가스로 절연된 GIS(Gas Insulation Switchgears)는 매우 신뢰성이 높은 것으로 평가되어왔다. 그러나 GIS 내부에서 발생하는 결함에 대하여 완전하게 배제시키지 못하고 있으며, 이러한 부분방전 활동에 의한 대부분의 결함들이 GIS의 사고를 이끈다고 알려져 있다[1]. 따라서, GIS 내부에서 발생하는 부분방전 현상의 위치와 측정은 1940년대 초반부터 관심을 가져왔으며, 현재에는 부분방전 형태의 패턴이 사용된 부분방전 검출회로 및 신호의 전파와는 무관하다는 것을 알아낸 시점에 이르렀다. 이에 따라, 본 논문에서는 $SF_6$ 가스가 봉입된 GIS 내부에서 발생하는 부분방전 형태의 패턴인식을 위한 방법으로 NN(Neural Network)의 알고리즘 중 BP(Back-Propagation) 알고리즘을 이용하였다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.566-567
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• 2005

Until now variable pattern classification methods have been introduced. So, variable methods in PD source classification were applied. NN(neural network) the most used scheme as a PD(partial discharge) source classification. But in recent year another method were developed. These methods is present superior to NN in the field of image and signal process function of classification. In this paper, it is show classification result in PD source using three methods; that is, BP(back-propagation), ANFIS(adaptive neuro-fuzzy inference system), PCA-LDA(principle component analysis-linear discriminant analysis).

• Journal of the Korea Fashion and Costume Design Association
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• v.6 no.1
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• pp.55-63
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• 2004

The dress culture of Korea and Turkey should be necessary to study in view of a similar culture of Korea and Turkey due to similarity of oriental culture. Thus, as two nation's dress is investigated, cultural characteristics and dress form of two nations are analyzed with cultural background in detail. Also, comprehension is determinated for the dress color and texture as well as dress culture. In this paper, It is investigated theoretically for the function, type, and class of dress. Also, The form of Korean Po are compared to background of Turkish dress and Sulltans' dress form. To unify a cultural propagation and dress form, technical, inductive contents analytical method as well as literature review are used in positivism. Subject is limited to the Korean Po and Sultans' dress of Turkey. As a results, as the nation's dress reflect to culture and society of the nation, Korean dress have some similarity and differences comparing Turkish dress as followings. In dress form of two nation, rectangular Gil's shape was very similar. Mu's shape was similar too. However, Sultans' dress had outside shape of caftan type and Po had full or partial wrinkles by transforming Mu. Also, Sultans' dress did not have Sup. Korean Po have side slits or back side slits and Jun-Sam but Sultans' dress had only some side slits.

• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Proceedings of the KIEE Conference
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• 2005.11a
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• pp.9-11
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• 2005

본 연구는 GIS 진단신뢰성 향상기술 개발을 목적으로, 16개의 인위적 결함을 이용하여 부분방전 신호를 발생시키고 검출하여 그 패턴인식 확률을 높이기 위하여 신경망에 Genetic Algorithm (GA) 을 적용하였다. 이를 위하여 다음과 같은 5가지 서로 다른 신경망 모델을 선택하였다: Back Propagation (BP), Jordan-Elman Network (JEN), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM) 및 Support Vector Machine (SVM). 이와 같이 선택된 모델에 동일한 데이터를 학습 시키고 패턴인식 확률을 비교 및 분석하였다. 실험 결과에 의하면, BP의 인식률이 가장 높고 다음으로 JEN의 인식률이 높이 나타났으며, 후자의 경우 모든 결함에 대하여 정확한 패턴분류를 한 반면에 전자의 경우 1.8% 의 분류 오차가 발생하였다. 따라서 인식률이 높은 신경망이 더 정확한 패턴분류를 보장하지 못한다는 실험적 결과를 고려 할 때, 인식률이 높은 두 개의 모델을 선정하여 각각의 출력에 일정한 가중치를 주고 합산하여 새로운 출력을 얻는 방법을 제안한다.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1880-1884
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• 2000

The discrete wavelet transform is utilized as preprocessing of Neural Network(NN) to identify aging state of internal partial discharge in transformer. The discrete traveler transform is used to produce wavelet coefficients which are used for Classification. The statistical parameters (maximum of wavelet coefficients, average value, dispersion, skewness, kurtosis) using the wavelet coefficients are input into an back-propagation neural network. The neurons whose weights have obtained through Result of Cross-Validation. The Neural Network learning stops either when the error rate achieves an appropriate minimum or when the learning time overcomes a constant value. The networks, after training, can decide if the test signal is Early Aging State or Last Aging State or normal state.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.63-71
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• 2013

In order to support mobility for sink groups, it is important to get the current location of a mobile sink group and then to offer the location to a source. Typically, previous works calculate a region including all member sinks by flooding; then, it notifies this region information to a source. However, flooding and location updates are periodically performed regardless of the group movement so that it causes considerable control overhead. In this paper, we propose an energy-efficient scheme supporting mobile sink groups. The proposed scheme obtains a location of a group without flooding. It exploits the inherent property of mobile sink groups which could approximate entire group movement by only partial member sinks movement. Also, the scheme learns group location by back-propagation learning method through exploiting overhearing feature in wireless communication environment. Our simulation studies show that the proposed scheme significantly improves in terms of energy consumption compared to the previous work.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1243-1253
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• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.