• Journal of applied mathematics & informatics
• /
• 제38권5_6호
• /
• pp.519-532
• /
• 2020

In this paper, we first propose a new total variation-based regularization model for image restoration. We next propose a fixed-point-like method for solving the new image restoration model, and then we provide convergence analysis for the fixed-point-like method. To evaluate the feasibility and efficiency of the fixed-point-like method for the new proposed total variation-based regularization model, we provide numerical experiments for several test problems.

• 대한수학회지
• /
• 제54권2호
• /
• pp.613-626
• /
• 2017

For the overdetermined linear system, when both the data matrix and the observed data are contaminated by noise, Total Least Squares method is an appropriate approach. Since an ill-conditioned data matrix with noise causes a large perturbation in the solution, some kind of regularization technique is required to filter out such noise. In this paper, we consider a Dual regularized Total Least Squares problem. Unlike the Tikhonov regularization which constrains the size of the solution, a Dual regularized Total Least Squares problem considers two constraints; one constrains the size of the error in the data matrix, the other constrains the size of the error in the observed data. Our method derives two nonlinear equations to construct the iterative method. However, since the Jacobian matrix of two nonlinear equations is not guaranteed to be nonsingular, we adopt a trust-region based iteration method to obtain the solution.

• 대한기계학회논문집B
• /
• 제29권8호
• /
• pp.903-910
• /
• 2005

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and finite-difference Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach that adopts the hybrid genetic algorithm as an initial value selector and uses the finite-difference Newton method as an optimization procedure.

• 전자공학회논문지
• /
• 제52권12호
• /
• pp.158-164
• /
• 2015

차선인식은 차선 유지, 경로 계획 등을 가능하게 하는 기술로서 자율주행차를 구성하는 가장 중요한 요소 중 하나이다. 카메라 센서를 이용한 연구가 많이 진행되었으나 센서의 특성상 화각의 한계가 존재하며 조도 환경에 취약한 단점이 있다. 반면 Lidar 센서는 넓은 화각과 함께 표면의 반사율 정보를 이용하기에 조도의 영향을 받지 않는 장점이 있다. 기존 연구에선 Hough 변환, 히스토그램 등의 방법을 이용하였는데 도로 표시들이 혼재한 상황에서 올바른 차선 인식이 이루어지지 않거나 다수의 차선이 존재함에도 주행 차선만 인식 되는 문제점들이 존재한다. 본 논문에서는 RANSAC과 regularization을 적용해 도로 표시가 혼재된 고속도로 환경에서도 정확하고 안정적인 다중 차선 인식 알고리즘을 제안한다. 정확한 차선 후보군 추출을 위해 원 모델 RANSAC을 적용하였고 안정적인 다중 차선 검출을 위해 피팅에 regularization을 추가로 제안하였다. 직접 취득한 도로 주행 데이터에 적용하여 높은 정확도와 실시간성을 정량적으로 검증하였다.

• 전자공학회논문지
• /
• 제54권4호
• /
• pp.91-98
• /
• 2017

전기 임피던스 단층촬영 기법은 도메인의 표면에 부착된 전극들을 통해 주입된 전류와 측정된 전압 데이터를 기반으로, 미지의 도전율 분포를 복원하는 비파괴 기술이다. 이 논문에서는 전기 임피던스 단층촬영법에서 일반적 Tikhonov 조정을 갖는 역문제를 풀고 도전율 분포를 복원하기 위해 절단된 특이값 분해 기반의 역문제 해법을 제안하였다. 역문제 계산시간을 줄이기 위해 일반 조정행렬을 역행렬 항목에서 분리시키고 절단된 특이값 분해 방법을 적용하였다. 제안한 방법의 성능을 검증하기 위해 모의실험과 팬텀실험을 수행하고 복원결과를 비교하였다.

• 한국산학기술학회논문지
• /
• 제11권10호
• /
• pp.3928-3934
• /
• 2010

본 논문에서는 움직임에 의해 흐려지고 잡음으로 훼손된 영상에 대하여 영상전체에 일률적으로 복원처리를 행하는 기존의 적용방법들에서 나타나는 문제점을 해결하고자 영상의 국부적인 특성과 방향성을 고려한 복원방법을 제시한다. 이는 평면영역과 윤곽영역을 적응적으로 처리하기 위하여 윤곽의 방향성을 찾기 위하여 다해상도 신호분석인 Wavelet 계수를 적용하여 처리하는 방법을 제안한다.

• Communications for Statistical Applications and Methods
• /
• 제29권5호
• /
• pp.591-601
• /
• 2022

In gene set analysis with microarray expression data, a group of genes such as a gene regulatory pathway and a signaling pathway is often tested if there exists either differentially expressed (DE) or differentially co-expressed (DC) genes between two biological conditions. Recently, a statistical test based on covariance estimation have been proposed in order to identify DC genes. In particular, covariance regularization by hard thresholding indeed improved the power of the test when the proportion of DC genes within a biological pathway is relatively small. In this article, we compare covariance thresholding methods using four different regularization penalties such as lasso, hard, smoothly clipped absolute deviation (SCAD), and minimax concave plus (MCP) penalties. In our extensive simulation studies, we found that both SCAD and MCP thresholding methods can outperform the hard thresholding method when the proportion of DC genes is extremely small and the number of genes in a biological pathway is much greater than a sample size. We also applied four thresholding methods to 3 different microarray gene expression data sets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer to compare genetic pathways identified by each method.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2007년도 춘계학술대회B
• /
• pp.3027-3032
• /
• 2007

To understand the complex phenomena performed in steam explosion, the fast and global measurement of the steam distribution is imperative for this extremely rapid transient stimulation of the bubble breakup and coalescence due to turbulent eddies and shock waves. TROI, the experimental facility requests more robust sensor system to meet this requirement. In Europe, researchers are prefer a X-ray method but this method is very expensive and has limited measurement range. There is an alternative technology such as ECT. Because of TROI's geometry, however, we need axial tomography method. This paper reviews image reconstruction algorethms for axial tomography, including Tikhonov regularization and iterative Tikhonov regularization. Axial tomography method is examined by simulation and experiment for typical permittivity distributions. Future works in axial tomography technology is discussed.

• IEIE Transactions on Smart Processing and Computing
• /
• 제6권2호
• /
• pp.85-92
• /
• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• 대한수학회지
• /
• 제54권5호
• /
• pp.1557-1571
• /
• 2017

The L-curve method is a parametric plot of interrelation between the residual norm of the least squares problem and the solution norm. However, the L-curve method may be hard to apply to the total least squares problem due to its no closed form solution of the regularized total least squares problems. Thus the sequence of the solution norm under the fixed regularization parameter and its corresponding residual need to be found with an efficient manner. In this paper, we suggest an efficient algorithm to find the sequence of the solutions and its residual in order to plot the L-curve for the total least squares problems. In the numerical experiments, we present that the proposed algorithm successfully and efficiently plots fairly 'L' like shape for some practical regularized total least squares problems.