• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• 대한수학회보
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• 제58권3호
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• pp.729-744
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• 2021

In this paper we prove global existence and uniqueness of axisymmetric strong solutions for the three dimensional electro-hydrodynamic model based on the coupled Navier-Stokes-Poisson-Nernst-Planck system in the exterior of a cylinder. The key ingredient is that we use the axisymmetry of functions to derive the L^p interpolation inequalities, which allows us to establish all kinds of a priori estimates for the velocity field and charged particles via several cancellation laws.

• 로봇학회논문지
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• 제11권4호
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• pp.226-234
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• 2016

In this study, a model-referenced underwater navigation algorithm is proposed for high-precise underwater navigation using monocular vision near underwater structures. The main idea of this navigation algorithm is that a 3D model-based pose estimation is combined with the inertial navigation using an extended Kalman filter (EKF). The spatial information obtained from the navigation algorithm is utilized for enabling the underwater robot to navigate near underwater structures whose geometric models are known a priori. For investigating the performance of the proposed approach the model-referenced navigation algorithm was applied to an underwater robot and a set of experiments was carried out in a water tank.

• 디지털산업정보학회논문지
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• 제10권2호
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• pp.203-211
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• 2014

In recent highly competitive business environment, collaboration has become one of the important business strategies for companies to survive and/or prosper. There are many different types of collaboration strategies, and it is crucial for companies to select the right ones according to the types of collaboration they require. To select the right type of collaboration options for business, in the past research, there have been two important criteria to classify collaboration types, namely governance (who makes key decisions - one kingpin participant or all players?) and membership (can anyone participate, or just select players?). In this research, we add a new classification criterion, recognition of collaboration purpose, which means whether collaborators know or do not know the purpose of collaboration in advance. Recently, we see many cases in which social media data are used in many unknown purposes a priori. In this research, we add such cases to develop new classification model.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2011년 춘계학술대회논문집
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• pp.458-464
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• 2011

A new method identifies coupled fluid-structure system with a reduced set of state variables is presented. Assuming that the structural model is known a priori either from an analysis or a test and using linear transformations between structural and aeroelastic states, it is possible to deduce aerodynamic information from sampled time histories of the aeroelastic system. More specifically given a finite set of structural modes the method extracts generalized aerodynamic force matrix corresponding to these mode shapes. Once the aerodynamic forces are known, an aeroelastic reduced-order model can be constructed in discrete-time, state-space format by coupling the structural model and the aerodynamic system. The resulting reduced-order model is suitable for constant Mach, varying density analysis.

• 지구물리와물리탐사
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• 제10권1호
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• pp.44-49
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• 2007

고해상도를 가지는 지하 밀도 영상을 얻기 위한 3 차원 중력 역산은 모델 변수들이 급격하게 많아지는 문제가 발생한다. 이 논문에서는 모델 변수들의 수를 줄이기 위해서 오일러 디컨벌루션의 해를 사전정보로 활용하는 3 차원 중력역산을 제안하였다. 이 논문에서 고안한 역산 알고리즘의 핵심은 오일러 디컨벌루션의 해가 얻어진 주위로 역산 공간을 제한하여 역산 해의 비유일성을 줄인 점이다. 먼저 중력 자료에 대한 3 차원 오일러 디컨별루션의 해를 구하고, 오일러 디컨벌루션의 해가 나타나는 주위에서만 3 차원 확장 탐색 역산을 수행하여 지하 밀도 영상을 구하였다. 이 3 차원 중력 역산 방법은 합성 모델에 적용하여 그 성능을 검증하였고, 석회암 지대에 존재하는 공동의 분포를 밝히기 위한 고정밀 중력탐사 자료 역산에도 적용하였다. 결과적으로, 오일러 디컨벌루션의 해를 사전정보로 이용한 역산을 이용하여 분해능이 향상된 고해상도의 지하 멸도 영상을 구할 수 있었다.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1405-1419
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• 2018

An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.

• Journal of information and communication convergence engineering
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• 제7권4호
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• pp.539-544
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• 2009

An estimation of 3D velocity field including occluded parts without maxing tracer to the fluid had not only never been proposed but also impossible by the conventional computer vision algorithm. In this paper, we propose a new method of three dimensional optical flow of the fluid based on physical model, where some boundary conditions are given from a priori knowledge of the flow configuration. Optical flow is obtained by minimizing the mean square errors of a basic constraint and the matching error terms with visual data using Euler equations. Here, Navier-Stokes motion equations and the differences between occluded data and observable data are employed as the basic constrains. we verify the effectiveness of our proposed method by applying our algorithm to simulated data with partly artificially deleted and recovering the lacking data. Next, applying our method to the fluid of observable surface data and the knowledge of boundary conditions, we demonstrate that 3D optical flow are obtained by proposed algorithm.

• 전자공학회논문지B
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• 제29B권9호
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• pp.37-49
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• 1992

The standard definition of computational vision is a set of inverse problems of recovering surfaces from images. Thus the common characteristics of the most early vision problems are ill-posed. The main idea for solving ill-posed problems is to restrict the class of admissible solutions by introducing suitable a priori knowledge. Standard regurarization methods lead to satisfactory solutions of early vision problems but cannot deal effectively and directly with a few general problems, such as discontinuity and fusion of information from multiple modules. In this paper, we discuss limitations of standard regularization theory and present new stochastic method. We will outline a rigorous approach to overcome part of ill-posedness of image restoration, edge detection, and stereo vision problems, based on Bayes estimation and MRF(Markov random field) model, that effectively deals with the problems. This result makes one hope that this framework could be useful in the solution of other vision problems.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.1222-1229
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• 1993

An advanced design environment , which is based on adaptive and knowledge -based finite elements (INTELMESH), has been developed. Unlike other approaches, INTEMMESH incorporates the information about the object geometry as well as the boundary and loading conditions to generate an ${\alpha}$-priori finite element mesh which is more refined around the critical regions of the problem domain. INTEMMESH is designed for planar domains and axisymmetric 3-D structures of elasticity and heat transfer subjected to mechanical and thermal loading . It intelligently identifies the critical regions/points in the problem domain and utilize the new concepts of substructuring and wave propagation to choose the proper mesh size for them. INTEMMESH generates well-shaped triangular elements by applying trangulartion and Laplacian smoothing procedures. The adaptive analysis involves the intial finite elements analyze and an efficient ${\alpha}$-posteriori error analysis involves the initial finite element anal sis and an efficient ${\alpha}$-posteriori error analysis and estimation . Once a problem is defined , the system automatically builds a finite element model and analyzes the problem though automatic iterative process until the error reaches a desired level. It has been shown that the proposed approach which initiates the process with an ${\alpha}$-priori, and near optimum mesh of the object , converges to the desired accuracy in less time and at less cost. Such an advanced design/analysis environment will provide the capability for rapid product development and reducing the design cycle time and cost.