• 대한전기학회논문지
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• 제43권7호
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• pp.1061-1066
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• 1994

The eddy currents of 3-dimensional conductin medium are calculated using vectorial Boundary element Method with vector variables E and H. Boundary edge basis vectors are used to expand the tangential components of E and H fields. Especially for axisymmetric conductor the computer storage and the computation time are greatly saved with the help of block-circulant boundary meshes. to verify this method eddy currents of conducting sphere are computed and show good agreements with the analytic solutions.

• 대한수학회지
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• 제38권2호
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• pp.297-309
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• 2001

White noise distribution theory over the complex Gaussian space is established on the basis of the recently developed white noise operator theory. Unitarity condition for a white noise operator is discussed by means of the operator symbol and complex Gaussian integration. Concerning the overcompleteness of the exponential vectors, a coherent sate representation of a white noise function is uniquely specified from the diagonal coherent state representation of the associated multiplication operator.

• 한국산학기술학회논문지
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• 제14권5호
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• pp.2393-2399
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• 2013

본 논문에서는 특이값 분해(SVD: singular value decomposition)에 의한 기저 스펙트럼의 합성을 통해 혈소판 라만 스펙트럼에서 알츠하이머병(AD: Alzheimer's disease)을 검출하는 방법을 제안하였다. AD가 유도된 형질 전환 실험용 쥐의 혈소판에서 측정한 라만 스펙트럼은 가산 잡음과 배경 잡음의 제거와 정규화로 구성된 전처리 과정을 수행한다. 각 데이터 행렬의 열벡터는 AD와 정상(NR: normal)의 라만 스펙트럼으로 구성한다. 이 데이터 행렬을 SVD로 분해한 다음 각 행렬의 열벡터 12개를 AD와 NR의 기저 스펙트럼으로 결정한다. 분류 과정은 각 클래스의 기저 스펙트럼을 선형 합성한 스펙트럼과 분류 스펙트럼의 평균제곱근오차(root mean square error)가 최소인 클래스를 선택하는 것으로 완료된다. 278개의 혈소판 라만 스펙트럼을 사용한 실험에 따르면 제안한 방법의 평균 분류율은 약 97.6%로 주성분 분석(principle components analysis)으로 추출한 특징에 MLP(multi-layer perceptron)를 이용한 경우보다 약 6.1% 정도의 우수한 성능을 보였다. 이 결과에서 SVD에 의한 기저 스펙트럼이 혈소판 라만 스펙트럼에서 AD의 검출에 적합하게 사용될 수 있음을 확인하였다.

• 산업기술연구
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• 제31권B호
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• pp.3-7
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• 2011

This paper addresses a computationally simple adaptive beamforming method to cancel interferences arriving onto a sensor array. In the proposed method, an estimate of the interference subspace is extracted from a submatrix of the sample covariance matrix and an orthonormal basis for the estimated subspace is efficiently found, one basis vector being updated every sample. Its computational burden is just $O(M{\eta})$ in an M-sensor array when ${\eta}$ directional signals are present. The new method does not make any premises of the geometrical structure of arrays, and can be applied to arbitrary arrays.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.943-948
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• 1994

The vector sum excited linear prediction(VSELP) coding gives high quality of synthetic speech at bit rates as low as 4.8kbps, but its computational complexity is prohibitive for real time applications. In this paper, we propose a method to reduce the computations of the VSELP codebook search procedure. The proposed method reduces the search space efficiently, before applying every linear combination of the basis vectors to the codebook search procedure. It decides whether is can fix the combination coefficient of each basis vector using heuristics so that the number of combinations decreases. It has been shown that the proposed method retains good quality of synthetic speech and reduces the computations of codebook search procedure by more than 40% of the origin.

• ETRI Journal
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• 제39권6호
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• pp.841-850
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• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2259-2261
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• 1998

Auditory Models that are capable of achieving human performance would provide a basis for realizing effective speech processing systems. Perceptual invariance to adverse signal conditions (noise, microphone and channel distortions, room reverberations) may provide a basis for robust speech recognition and speech coder with high efficiency. Auditory model that simulates the part of auditory periphery up through the auditory nerve level and new distance measure that is defined as angle between vectors are described.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (B)
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• pp.769-771
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• 2005

In this paper we present a method of separating musical instrument sound sources from their monaural mixture, where we take the harmonic structure of music into account and use the sparseness and the overlapping NMF [1] to select representative spectral basis vectors which are used to reconstruct unmixed sound. A method of spectral basis selection is illustrated and experimental results with monaural mixture of voice/cello and trumpet/viola are shown to confirm the validity of our proposed method.

• Structural Engineering and Mechanics
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• 제84권4호
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• pp.453-464
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• 2022

Thin plates are the most common spatially stressed members in engineering structures that bear out-of-plane loads. Therefore, it is of great significance to study the deformation performance characteristics of thin plates for structural design. By constructing 12 basic displacement and deformation basis vectors of the four-node square thin plate element, a deformation decomposition method based on the complete orthogonal mechanical basis matrix is proposed in this paper. Based on the deformation decomposition method, the deformation properties of the thin plate can be quantitatively analyzed, and the areas dominated by each basic deformation can be visualized. In addition, the method can not only obtain more deformation information of the structure, but also identify macroscopic basic deformations, such as bending, shear and warping deformations. Finally, the deformation properties of the bidirectional thin plates with different sizes of central holes are analyzed, and the changing rules are obtained.

• 한국지능시스템학회논문지
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• 제16권6호
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• pp.759-765
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• 2006

본 논문에서는 퍼지 소속 함수와 웨이브렛 기저를 이용한 효과적인 얼굴 표정 인식 LDA 융합모델을 제안하였다. 제안된 알고리즘은 최적의 영상을 얻기 위해 퍼지 웨이브렛 알고리즘을 수행하고, 표정 검출은 얼굴 특징 추출단계와 얼굴표절인식 단계로 구성된다. 본 논문에서 얼굴 표정이 담긴 영상을 PCA를 적용하여 고차원에서 저차원의 공간으로 변환 후, LDA 특성을 이용하여 클래스 별호 특징벡터를 분류한다. LDA 융합 모델은 얼굴 표정인식단계는 제안된 LDA융합모델의 특징 벡터에 NNPC를 적응함으로서 얼굴 표정을 인식한다. 제안된 알고리즘은 6가지 기본 감정(기쁨, 화남, 놀람, 공포, 슬픔, 혐오)으로 구성된 데이터베이스를 이용해 실험한 결과, 기존알고리즘에 비해 향상된 인식률과 특정 표정에 관계없이 고른 인식률을 보임을 확인하였다.