• 한국수자원학회논문집
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• 제31권4호
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• pp.439-448
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• 1998

대규모의 배수관망 시스템에서 유량해석을 위한 기법들이 많이 있지만 가장 널리 사용되고 있는 기법은 선형화 기법이다. 이 방법은 연속방정식과 에너지 방정식을 연립하여 해석하므로 이론적으로는 간단하나 실제 시스템에 적용을 위해서는 연립방정식 해석시 생성되는 계수매트릭스의 대각행력에 '0'이 발생하는 등 매우 큰 이산화된 계수 매트릭스의 처리가 문제가 되었다. 본 연구에서는 ill-condition 계수매트릭스의 발생을 배제하기 위해 도학이론으로부터 선형독립적인 폐합회로를 찾는 기법을 상수관망해석에 적용하여 선형화기법의 positive-definite 계수매트릭스를 만드는 기법을 개발하였다. 개발된 알고리듬의 적용성을 시험하고자 22개 가상관로 및 142개 관로를 가진 대구 인근의 실제 관망자료를 이용하여 유량해석을 실시하였다. 유량해석 결과 본 알고리듬이 적용된 모형에서는 가상관망 및 실제관로에서 수렴의 실패없이 원활하게 계산이 이루어지고 있었다. 본 연구결과는 관로내 정상상태 유량해석을 위해 효율적으로 이용될 것이 기대된다.

• Journal of Mechanical Science and Technology
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• 제20권1호
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• pp.110-124
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• 2006

The multi scale wavelet-Galerkin method implemented in an adaptive manner has an advantage of obtaining accurate solutions with a substantially reduced number of interpolation points. The method is becoming popular, but its numerical efficiency still needs improvement. The objectives of this investigation are to present a new numerical scheme to improve the performance of the multi scale adaptive wavelet-Galerkin method and to give detailed implementation procedure. Specifically, the subdomain technique suitable for multiscale methods is developed and implemented. When the standard wavelet-Galerkin method is implemented without domain subdivision, the interaction between very long scale wavelets and very short scale wavelets leads to a poorly-sparse system matrix, which considerably worsens numerical efficiency for large-sized problems. The performance of the developed strategy is checked in terms of numerical costs such as the CPU time and memory size. Since the detailed implementation procedure including preprocessing and stiffness matrix construction is given, researchers having experiences in standard finite element implementation may be able to extend the multi scale method further or utilize some features of the multiscale method in their own applications.

• 대한기계학회논문집B
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• 제29권9호
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• pp.1049-1056
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• 2005

A conservative pressure-based finite-volume numerical method has been developed for computing flow and heat transfer by using an unstructured grid system. The method admits arbitrary convex polyhedra. Care is taken in the discretization and solution procedures to avoid formulations that are cell-shape-specific. A collocated variable arrangement formulation is developed, i.e. all dependent variables such as pressure and velocity are stored at cell centers. Gradients required for the evaluation of diffusion fluxes and for second-order-accurate convective operators are found by a novel second-order accurate spatial discretization. Momentum interpolation is used to prevent pressure checkerboarding and the SIMPLE algorithm is used for pressure-velocity coupling. The resulting set of coupled nonlinear algebraic equations is solved by employing a segregated approach, leading to a decoupled set of linear algebraic equations fer each dependent variable, with a sparse diagonally dominant coefficient matrix. These equations are solved by an iterative preconditioned conjugate gradient solver which retains the sparsity of the coefficient matrix, thus achieving a very efficient use of computer resources.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.202-206
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• 1989

A new algorithm is suggested to solve the optimal reactive power control(optimal VAR control) problem. An efficient computer program based on the latest achievements in the sparse matrix/vector techniques has been developed for this purpose. The model minimizes the real power losses in the system. The constraints include the reactive power limits of the generators, limits on the bus voltages and the operating limits of control variables- the transformer tap positions, generator terminal voltages and switchable reactive power sources. The method developed herein employs linearized sensitivity relationships of power systems to establish both the objective function for minimizing the system losses and the system performance sensitivities relating dependent and control variables. The algorithm consists of two modules, i.e. the Q-V module for reactive power-voltage control, Load flow module for computational error adjustments. In particular, the acceleration factor technique is introduced to enhance the convergence property in Q-module, The combined use of the afore-mentioned two modules ensures more effective and efficient solutions for optimal reactive power dispatch problems. Results of the application of the method to the sample system and other worst-case system demonstrated that the algorithm suggested herein is compared favourably with conventional ones in terms of computation accuracy and convergence characteristics.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권10호
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• pp.1412-1417
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• 2004

전체관적인 표현방법인 희소 코딩 또는 독릴 성분 분해(ICA)는 이전의 청각의 처리와 소리 분류의 작업을 해명하는데 성공적으로 적용되었다. 반대로 부분 기반 표현법은 뇌에서 물체를 인식하는 방법을 이해하는 또 다른 방법이다. 이 논문에서, 우리는 소리 분류의 작업에 부분기반 표현법을 학습시키는 비음수화 행렬 분해(NMF)(1) 방법을 적용하였다. 잡음이 존재할 때와 존재하지 않을 때 두 가지 상황에서, NMF를 이용하여 주파수-시간영역의 소리로부터 특징을 추출하는 방법을 설명한다. 실험결과에서는 NMF에 기반을 둔 특징이 ICA에 기반을 두어 추출한 특징보다 소리 분류의 성능을 향상시킴을 보여준다.

• 융합신호처리학회논문지
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• 제11권3호
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• pp.226-231
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• 2010

본 논문에서는 희소 패리티 검사 행열로부터 생성된 생성행열을 사용하여 에러 정정능력과 높은 부호율을 갖는 DC-free 다중 모드 부호를 구성하기 위한 새로운 부호화 기법을 제안 한다. 제안된 기법은 별개의 후보 부호워드들을 생성하기 위해 고속 생성행열들을 이용한다. 복호 과정의 복잡도는 수신된 부호워드의 신드롬이 ‘0’인지 아닌지에 따라 결정된다. 만약 신드롬이 ‘0’ 인 경우 복호는 수신된 부호워드의 잉여 비트들을 삭제하여 간단히 수행되고, ‘1’인 경우에는 합곱 (sum-product) 알고리즘으로 복호가 이루어진다. 제안된 기법은 DC 성분을 억압하면서도 낮은 비트 오율을 가질 수 있다.

• 정보처리학회논문지B
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• 제11B권3호
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• pp.303-308
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• 2004

Gabor 코사인과 사인 변환은 영상주파수 성분을 국부적으로 표현하므로 영상과 비디오 압축 알고리즘에 사용될 수 있다. 압축과 복원에 사용되는 순방향과 역방향 행렬 변환식의 계산 복잡도는 O($N^3$)이다. 이 논문에서는 기저함수들의 길이를 절단하여, 희소기저행렬을 생성하고, 영상압축과 복원에 적용하여 실시간 처리에 용이하게 변환 계산량을 감소시키고자 한다. 기저함수 길이가 감소함에 따라서, 기저함수 에너지에 미치는 절단의 영향을 조사하고 다른 여러 측정량의 변화를 살펴본다. 실험 결과로부터 약 1% 이하의 성능저하로 11배의 곱하기/더하기 수를 감소시킬 수 있음을 보았다.

• 인터넷정보학회논문지
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• 제25권1호
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• pp.49-56
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• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

• 한국전산유체공학회지
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• 제13권4호
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• pp.86-95
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• 2008

For analyses of multi-phase flows in a water-cooled nuclear power plant, a three-dimensional SIMPLE-algorithm based hydrodynamic solver CUPID-S has been developed. As governing equations, it adopts a two-fluid three-field model for the two-phase flows. The three fields represent a continuous liquid, a dispersed droplets, and a vapour field. The governing equations are discretized by a finite volume method on an unstructured grid to handle the geometrical complexity of the nuclear reactors. The phasic momentum equations are coupled and solved with a sparse block Gauss-Seidel matrix solver to increase a numerical stability. The pressure correction equation derived by summing the phasic volume fraction equations is applied on the unstructured mesh in the context of a cell-centered co-located scheme. This paper presents the numerical method and the preliminary results of the calculations.

• Journal of the Korean Data and Information Science Society
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• 제24권5호
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.