• 한국컴퓨터정보학회논문지
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• 제16권12호
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• pp.187-196
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• 2011

유도 전동기는 항공 산업, 자동차 산업 등의 산업 현장에서 중요한 역할을 하고 있으며, 이러한 유도 전동기의 고장으로 인한 피해를 최소화하기 위해 유도 전동기의 고장 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 이에 본 논문에서는 정상 및 각종 비정상 상태의 유도 전동기 진동 신호에 대해 부분 자기 상관(partial autocorrelation, PARCOR) 계수, 로그 스펙트럼 파워(log spectrum powers, LSP), 캡스트럼 계수의 평균값(cepstrum coefficients mean, CCM), 멜 주파수 캡스트럼 계수(mel-frequency cepstrum coefficient, MFCC)의 네 가지 특징 벡터를 신경 회로망의 입력으로 사용하여 유도 전동기의 고장을 검출하고 분류하였다. 고장 분류를 위한 최적의 특징 벡터를 찾기 위해 추출하는 특징의 수를 2에서 20으로 바꾸어 가며 분류 성능을 평가한 결과 CCM을 제외한 나머지의 경우 5~6의 특징만으로 분류 정확도가 거의 100%에 가까운 결과를 보였다. 또한 본 논문에서는 실제 산업 현장에서 진동 신호 취득 시 포함될 수 있는 잡음을 고려하여 취득한 신호에 백색 잡음(white Gaussian noise)을 인위적으로 추가하여 실험한 결과 LSP, PARCOR, MFCC 순으로 잡음 환경에 강인한 특징 벡터임을 확인할 수 있었다.

• Journal of Computational Design and Engineering
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• 제1권2호
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• pp.88-95
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• 2014

Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.

• 한국정밀공학회지
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• 제25권4호
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• pp.69-76
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• 2008

The self-organizing feature map(SOFM) among a number of neural network uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 1-dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the method using string and the modified neural network is useful tool to mobile robot for the global path planning.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1408-1411
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• 2002

In this paper, we propose a method that converts motion vectors on MPEG coded domain as a uniform set, independent of the frame type and the direction of prediction, and directly utilizes these normalized motion vectors for understanding video contents. This frame-type-independent motion vectors are utilized as feature information for image retrieval or moving object tracking on compressed domain. By simulation, we evaluate the effectiveness of the proposed method and compare its performance to the conventional method.

• 인터넷정보학회논문지
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• 제10권4호
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• pp.13-23
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• 2009

본 논문에서 우리는 위성 영상에 대하여 객체를 지역화한 접근을 제안한다. 우리의 방법은 서술 벡터에 기반한 특징 정합 방법이다. 객체를 지역화하는 방법은 SIFT(Scale Invariant Feature Transform)를 적용시킨다. 먼저, 위성영상의 키포인트를 찾고, 키포인트의 서술 벡터를 일반화한다. 그리고 서술 벡터간에 유사성을 측정하여 키포인트를 매칭시킨다. 마지막으로, 키포인트의 인접 픽셀값에 가중치를 주어 객체에서 위치를 결정한다. SIFT를 이용한 이 실험은 다양한 스케일과 어파인 변환에 대해 좋은 결과를 산출하였다. 본 논문에서 제안된 방법은 구글 어스의 위성영상을 사용하였다.

• 한국전자통신학회논문지
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• 제8권9호
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• pp.1299-1305
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• 2013

이 논문에서 우리는 MFCC 특징벡터의 차원 저감을 통해 음성 인식에서의 계산량을 줄이는 방법을 조사한다. 특징벡터의 특성분해는 벡터의 성분을 분산의 크기에 따라 배치되도록 선형 변환 시켜준다. 첫 번째 성분은 가장 큰 분산을 가져서 패턴 분류에서 가장 중요한 역할을 한다. 따라서, 분산이 작은 성분들을 제외시키는 차원 저감을 통하여, 계산량을 줄이면서 동시에 음성 인식 성능을 저하시키지 않는 방법을 생각할 수 있다. 실험 결과, MFCC 특징벡터의 성분을 절반 정도로 줄여도 음성인식 오류율에 큰 악영향이 없음이 확인되었다.

• 제어로봇시스템학회논문지
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• 제5권7호
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• pp.821-826
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• 1999

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.313-320
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• 1999

In this paper, a translation, rotation and scale invariant system for the classification of closed 2D images using the bispectrum of a contour sequence and the weighted fuzzy mean method is derived and compared with the classification process using one of the competitive neural algorithm, called a LVQ(Learning Vector Quantization). The bispectrun based on third order cumulants is applied to the contour sequences of the images to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images and are fed into an classifier using weighted fuzzy mean method. The experimental processes with eight different shapes of aircraft images are presented to illustrate the high performance of the proposed classifier.

• 제어로봇시스템학회논문지
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• 제9권11호
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• pp.937-942
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• 2003

We present a self-localization method for mobile robots using vertical line features of indoor environment. When a 2D map including feature points and color information is given, a mobile robot moves to the destination, and acquires images from the surroundings having vertical line edges by one camera. From the image, vertical line edges are detected, and pattern vectors meaning averaged color values of the left and right regions of the each line are computed by using the properties of the line and a region growing method. The pattern vectors are matched with the feature points of the map by comparing the color information and the geometrical relationship. From the perspective transformation and rigid transformation of the corresponded points, nonlinear equations are derived. Localization is carried out from solving the equations by using Newton's method. Experimental results show that the proposed method using mono view is simple and applicable to indoor environment.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.793-797
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• 2003

In this paper, we present a self-localization method for mobile robots using vertical line features of indoor environment. When a 2D map including feature points and color information is given, a mobile robot moves to the destination, and acquires images by one camera from the surroundings having vertical line edges. From the image, vertical line edges are detected, and pattern vectors meaning averaged color values of the left and right region of each line segment are computed. The pattern vectors are matched with the feature points of the map using the color information and the geometrical relationship of the points. From the perspective transformation of the corresponded points, nonlinear equations are derived. Localization is carried out from solving the equations by using Newton's method. Experimental results show that the proposed method using mono view is simple and applicable to indoor environment.