• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1996년도 학술대회
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• pp.217-220
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• 1996

This paper describes a new hierarchial block matching algorithm especially appropriate for a large search area. The proposed algorithm consists of higher level search for an initial motion vector estimate by using a new matching criterion over the evenly subsampled search points, and lower level search for the final motion vector refinement. In the higher level matching criterion, mean absolute differences at the search points (or motion vector candidates) similar to motion vectors of causally neighboring blocks, are weighted properly so that these points can have a higher chance to being selected. The proposed algorithm outperforms existing hierarchical block matching algorithms, and its computational regularity makes hardware implementation simple.

• 소음진동
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• 제10권2호
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제26회 춘계학술대회논문집
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• pp.209-212
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• 2006

본 논문에서는 항공기용 터보 축 엔진의 결함 진단 알고리즘을 개발하지 위해 Support Vector Machine(SVM)과 인공신경망(ANN)을 이용하였다. SVM을 이용하여 결함 위치를 판별한 후 인공신경망이 선택적으로 학습하는 분할 학습 알고리즘(SLA)을 제안하였으며 이를 고도 변화에 따른 가스 터빈 엔진의 결함 진단에 적용하여 분류 속도 및 예측 정확률 개선 가능성을 확인하였다.

• ETRI Journal
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• 제28권5호
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• pp.574-582
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• 2006

In this paper, a new error concealment algorithm is proposed for the H.264 standard. The algorithm consists of two processes. The first process uses a fuzzy logic method to select the size type of lost blocks. The motion vector of a lost block is calculated from the current frame, if the motion vectors of the neighboring blocks surrounding the lost block are discontinuous. Otherwise, the size type of the lost block can be determined from the preceding frame. The second process is an error concealment algorithm via a proposed adapted multiple-reference-frames selection for finding the lost motion vector. The adapted multiple-reference-frames selection is based on the motion estimation analysis of H.264 coding so that the number of searched frames can be reduced. Therefore the most accurate mode of the lost block can be determined with much less computation time in the selection of the lost motion vector. Experimental results show that the proposed algorithm achieves from 0.5 to 4.52 dB improvement when compared to the method in VM 9.0.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.213-218
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• 2009

This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.475-481
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• 1999

This paper presents a recursive least square estimation algorithm to estimate parameters of the vector controlled induction machine based on measurements of the stator voltage, curents and slip frequency. Due to its recursive structure, this algorithm has the potential to be used for on-line estimation and adaptive control. The algorithm is designed using regression model derived from the motor electrical equation. This model is valid when there is a tittle-scale separation between vector control system and adaptive system. Vector control performed at fast stage and slow stage is in charge of parameters estimation. The performance of tile algorithm is illustrated by means of simulation results and experiment.

• 정보처리학회논문지D
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• 제16D권5호
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• pp.729-736
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• 2009

소프트웨어 공학에서 정확한 개발 비용 예측은 성공적인 개발 프로젝트를 위한 필수적인 요소로, 현재까지 많은 소프트웨어 비용산정을 위한 모델들이 개발되어 왔다. 전통적인 통계적 기법부터 기계학습을 적용한 알고리즘까지 다양한 분야의 아이디어를 접목하고 있다. 본 논문에서는 소프트웨어 개발 비용 예측을 위한 방법으로 유전 알고리즘과 서포트 벡터 머신의 회귀모델인 서포트 벡터 회귀를 결합한 GA-SVR 모델을 제안한다. 제안된 모델은 기존의 연구에 비해 향상된 결과를 보이고 있다.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1986년도 추계학술발표회 논문집
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• pp.182-185
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• 1986

In this paper, the four algorithm which obtain 2D displacement vector are proposed. In corwocutive difference pictures, the characteristics of up DP boundary and region are discussed and we estimate displacement vector using the DP boundary and region, Finally, the performance of proposed algorithm for gaussian noisy image which generated by computer are discussed.

• 한국컴퓨터정보학회논문지
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• 제15권7호
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• pp.11-17
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• 2010

본 논문에서는 클래스 불균형 학습을 위한 이차 최적화 문제의 해를 구하는 개선된 SMO 학습 알고리즘을 제안한다. 클래스에 서로 다른 정규화 값이 부여되는 지지벡터기계의 최적화 문제의 구현에 SMO 알고리즘이 적합하며, 제안된 알고리즘은 서로 다른 클래스에서 선택된 두 라그랑지 변수의 현재 해를 구하는 학습 단계를 반복한다. 제안된 학습 알고리즘은 UCI 벤치마킹 문제에서 테스트되어 클래스 불균형 분포를 반영하는 g-mean 평가를 이용한 일반화 성능이 SMO 알고리즘과 비교되었다. 실험 결과에서 제안된 알고리즘은 SMO에 비해 적은 클래스 데이터의 예측율을 높이고 학습시간을 단축시킬 수 있다.

• 한국CDE학회논문집
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• 제13권1호
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• pp.1-7
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• 2008

Data compression becomes increasingly an important issue for reducing data storage spaces as well as transmis-sion time in network environments. In 3D geometric models, the normal vectors of faces or meshes take a major portion of the data so that the compression of the vectors, which involves the trade off between the distortion of the images and compression ratios, plays a key role in reducing the size of the models. So, raising the compression ratio when the normal vector is compressed and minimizing the visual distortion of shape model's shading after compression are important. According to the recent papers, normal vector compression is useful to heighten com-pression ratio and to improve memory efficiency. But, the study about distortion of shading when the normal vector is compressed is rare relatively. In this paper, new normal vector compression method which is clustering normal vectors and assigning Representative Normal Vector (RNV) to each cluster and using the angular deviation from actual normal vector is proposed. And, using this new method, Visually Undistinguishable Lossy Compression (VULC) algorithm which distortion of shape model's shading by angular deviation of normal vector cannot be identified visually has been developed. And, being applied to the complicated shape models, this algorithm gave a good effectiveness.