• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.358-360
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• 2007

In this paper, an auto-diagnosis method of the stator winding fault for small induction motor is suggested. 3-phase stator currents are sampled, filtered, and transformed with Park's vector transformation. After then Park's vector patterns are obtained. To detect the stator winding fault automatically, a distortion ratio (id/iq) is newly defined and compared with the one of healthy motor, and the threshold levels are suggested. The 2-turn, 4-turn, 8-turn winding fault are tested with no load, 25%, 50%, 75%, and 100% rated load. The distortion ratio of the Park's vector patterns are increased as the increase of the faulted turns, but are same as the increase of the load.

• Spatial Information Research
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• 제15권4호
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• pp.385-395
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• 2007

컴퓨터의 사용과 인터넷이 발달로 인해 데이터의 사용과 공유가 매우 증가하고 있으며, 그에 따라 불법적인 데이터의 보급도 발생하고 있다. 본 연구에서는 이러한 불법적인 데이터의 복제 문제를 해결하기 위해 디지털 워터마킹 기법을 제안한다. 특히, GIS에서 많이 사용하고 있는 데이터인 벡터데이터에 워터마크를 삽입하고, 소유권을 주장할 수 있는 워터마킹 방법을 제안한다. 연구에서 제안한 방법은 벡터데이터의 좌표에 이진 연산을 이용하여 워터마크를 직접 삽입을 하고, 워터마크를 삽입하는 역과정을 통해 워터마크를 추출하는 것이다. 실험 결과를 통해 제안한 방법이 벡터데이터에 대한 다양한 공격에 대해 강인함을 알 수 있었다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.435-440
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• 2005

This study describes a new method of constructing Reconfigurable machine tools configurations from a set of modules or components. This proposed method defines combinability vector for each module and mutual combinability coefficient matrix for adjacent two modules. All of machine configurations possible to be generated from any two adjacent modules can be determined by quadratic form of two associated combinability vectors. Furthermore, all of possible RMT configurations generating from a series of multiple modules also can be obtained by multiplying quadratic form of two adjacent conbinability vectors recursively. Our proposed RMT configuration generating method can be successfully applied to determining all of possible machine configurations from several modules or components at conceptual- or preliminary- design stage.

• Journal of information and communication convergence engineering
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• 제3권2호
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• pp.105-109
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• 2005

This paper proposed a fault diagnosis technique of induction motors winding fault based on an artificial neural network (ANN). This method used Park's vector pattern as input data of ANN. The ANN are firstly learned using this pattern, and then classify between 'healthy' and 'winding fault' (with 2, 10, and 20 shorted turn) induction motor under 0, 50, and $100\%$ load condition. Also the possibility of classification of untrained turn-fault and load condition are tested. The proposed method has been experimentally tested on a 3-phase, 1 HP squirrel-cage induction motor. The obtained results provided a high level of accuracy especially in small turn fault, and showed that it is a reliable method for industrial application

• 한국소음진동공학회논문집
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• 제21권6호
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• pp.521-528
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• 2011

A variety of noise control methods have been developed as an interest on noise issues increases. Among them, noise control methods using masking effect, a phenomenon to reduce the ability to notice the unwanted sound by proper sound, to implement a pleasant sound environment have been studied under the name of soundscape. We proposed a novel vector base amplitude panning(VBAP) based noise control method to apply to the building environment. The proposed method could improve the amenity inside the building to reproduce the sounds with excellent masking effect on the incoming path of noise using the control speakers, considering the direction of noise source. The directional masking sounds can be generated by using VBPA technique. To verify the performance of the proposed method, we carried out the subjective test for the degree of amenity according to direction of the masking sound. Subjective test results showed that it is possible to improve the amenity inside the building by controlling the direction of masking sound considering the human's auditory characteristic.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.175-182
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• 2007

The support vector machine has been successful in many applications because of its flexibility and high accuracy. However, when a training data set is large or imbalanced, the support vector machine may suffer from significant computational problem or loss of accuracy in predicting minority classes. We propose a modified version of the support vector machine using the K-means clustering that exploits the information in class labels during the clustering process. For large data sets, our method can save the computation time by reducing the number of data points without significant loss of accuracy. Moreover, our method can deal with imbalanced data sets effectively by alleviating the influence of dominant class.

• 천문학회보
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• 제22권
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• pp.12.1-12.1
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• 1997

• 한국정보통신학회논문지
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• 제12권9호
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• pp.1547-1553
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• 2008

본 논문은 간소화된 주성분 벡터를 이용한 벡터 그래픽 캐릭터의 다양한 얼굴 표정 생성 방법을 제안한다. 먼저 Russell의 내적 정서 상태에 기반하여 재정의된 벡터 그래픽 캐릭터들의 9가지 표정에 대해 주성분 분석을 수행한다. 이를 통해 캐릭터의 얼굴 특성과 표정에 주된 영향을 미치는 주성분 벡터를 찾아내고, 간소화된 주성분 벡터로부터 얼굴 표정을 생성한다. 또한 캐릭터의 특성과 표정의 가중치 값을 보간함으로써 자연스러운 중간 캐릭터 및 표정을 생성한다. 이는 얼굴 애니메이션에서 종래의 키프레임 저장 공간을 상당히 줄일 수 있으며, 적은 계산량으로 중간 표정을 생성할 수 있다. 이에 실시간 제어를 요구하는 웹/모바일 서비스, 게임 등에서 캐릭터 생성 시스템의 성능을 상당히 개선할 수 있다.

• Asia pacific journal of information systems
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• 제15권2호
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• Spatial Information Research
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• 제17권1호
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• pp.51-66
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• 2009

정보통신 기술의 발달과 더불어 여러 데이터를 디지털화함에 따라 데이터 소유자의 권리를 보호하고자 하는 저작권 보호에 대한 관심이 많아졌다. 디지털 워터마킹은 저작권 보호를 위한 강력한 방법 중에 하나이다. GIS에서 많이 사용되는 벡터 맵 데이터에 대한 저작권 보호를 위해 본 연구에서는 새로운 디지털 워터마킹 기법을 제안하고자 한다. 제안하는 방법은 CRC의 원리를 이용하여 워터마크를 삽입하고 검출하는 방법으로 실험 결과 여러 공격에서도 삽입한 워터마크를 검출하여 데이터 소유권을 보호할 수 있음을 알 수 있었다. 따라서 제안하는 방법은 벡터 맵 데이터의 소유권을 보호하기 위한 방법으로 활용 가능할 것으로 기대된다.