• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1229-1244
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• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1160-1169
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• 1996

In this study, an experiment of information retrieval using fuzzy connection matrix of keywords was conducted. A query for retrieval was constructed from each keyword and Boolean operator such as AND, OR, NOT. In a workstation environment, the performance of the fuzzy retrieval system was proved to be considerably effective than that of the system using the crisp set theory. And both recall ratio and precision ratio showed that the proposed technique would be a possible alternative in future information retrieval. Some special features of this experimental system were ; ranking the results in the order of connectivity, making the retrieval results correspond flexibly by changing the threshold value, trying to accord the retrieval process with the retrieval semantics by treating the averse-connectivity (fuzzy value) as a semantic approximation between kewords.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.526-537
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• 1999

In the paper. we propose an evaluation model based the adaptive fuzzy systems, which can transform the physical features of a color pattern to the emotional features. The model is motivated by the Soen's psychological experiments, in which he found the physical features such as average hue, saturation, intensity and the dynamic components of the color patterns affects to the emotional features represented by a pair of adjective words having the opposite meanings. Our proposed model consists of two adaptive fuzzy rule-bases and the y-model, a l i r ~ r ys et operator, to fuze the evaluation values produced by them. The model shows con~parablep erformances to the neural network for the approximation of the nonlinear transforms, and it has the advantage to obtain the linbwistic interpretation from the trained results. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.

• Advances in nano research
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• v.12 no.2
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• pp.213-221
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• 2022

Silicon carbide (SiC) is a binary carbon-silicon compound. In its two-dimensional form, monolayer SiC is composed of a monolayer carbon and silicon atoms constructed as a honeycomb lattice. SiC has recently been receiving increasing attention from researchers owing to its intriguing electronic properties. In this present work, SiC nanoribbons (SiCNRs) are modelled and simulated to obtain accurate electronic properties, which can further guide fabrication processes, through bandgap engineering. The primary objective of this work is to obtain the electronic properties of monolayer SiCNRs by applying numerical computation methods using nearest-neighbour tight-binding models. Hamiltonian operator discretization and approximation of plane wave are assumed for the models and simulation by applying the basis function. The computed electronic properties include the band structures and density of states of monolayer SiCNRs of varying width. Furthermore, the properties are compared with those of graphene nanoribbons. The bandgap of ASiCNR as a function of width are also benchmarked with published DFT-GW and DFT-GGA data. Our nearest neighbour tight-binding (NNTB) model predicted data closer to the calculations based on the standard DFT-GGA and underestimated the bandgap values projected from DFT-GW, which takes in account the exchange-correlation energy of many-body effects.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.520-529
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• 2005

A feature-based 3D modeling algorithm is presented in this paper. Since conventional methods use depth-based techniques, they need much time for the image matching to extract depth information. Even feature-based methods have less computation load than that of depth-based ones, the calculation of modeling error about whole pixels within a triangle is needed in feature-based algorithms. It also increase the computation time. Therefore, the proposed algorithm consists of three phases, which are an initial 3D model generation, model evaluation, and model refinement phases, in order to acquire an efficient 3D model. Intensity gradients and incremental Delaunay triangulation are used in the Initial model generation. In this phase, a morphological edge operator is adopted for a fast edge filtering, and the incremental Delaunay triangulation is modified to decrease the computation time by avoiding the calculation errors of whole pixels and selecting a vertex at the near of the centroid within the previous triangle. After the model generation, sparse vertices are matched, then the faces are evaluated with the size, approximation error, and disparity fluctuation of the face in evaluation stage. Thereafter, the faces which have a large error are selectively refined into smaller faces. Experimental results showed that the proposed algorithm could acquire an adaptive model with less modeling errors for both smooth and abrupt areas and could remarkably reduce the model acquisition time.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.1
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• pp.75-90
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• 1995

A mathematical model for the dynamic analysis of a riser system with the inclusion of internal flow and nonlinear effects due to large structural displacements is developed to investigate the effect of internal flow on marine riser dynamics. The riser system accounts fir the nonlinear boundary conditions and includes a steady flow inside the pipe which is modeled as an extensible or inextensible. tubular beam subject to nonlinear three dimensional hydrodynamic loads such as current or wave excitation. Galerkin's finite element approximation and time incremental operator are implemented to derive the matrix equation of equilibrium for the finite element system and the extensibility or inextensibility condition is used to reduce degree of freedom of the system and the required computational time in the case of a nonlinear model. The algorithm is implemented to develop computer programs used in several numerical applications. The investigations of the effect of infernal flow on riser vibration due to current or wave loading are performed according to the change of various parameters such as top tension, internal flow velocity, current velocity, wave period, and so on. It is found that the effect of internal flow can be controlled by the increase of top tension. However, careful consideration has to be given in the design point particularly for the long riser under the harmonic loading such as waves. And it is also found that the consideration of nonlinear effects due to large structural displacements increases the effect of internal flow on riser dynamics.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.