• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.4
• /
• pp.294-301
• /
• 2016

In this paper, we present methods to efficiently parallelize iterative 3D image reconstruction by exploiting trigeneous devices (three different types of device) at the same time: a CPU, an integrated GPU, and a discrete GPU. We first present a technique that exploits single instruction multiple data (SIMD) architectures in GPUs. Then, we propose a performance estimation model, based on which we can easily find the optimal data partitioning on trigeneous devices. We found that the performance significantly varies by up to 6.23 times, depending on how SIMD units in GPUs are accessed. Then, by using trigeneous devices and the proposed estimation models, we achieve optimal partitioning and throughput, which corresponds to a 9.4% further improvement, compared to discrete GPU-only execution.

• Journal of applied mathematics & informatics
• /
• v.32 no.3_4
• /
• pp.389-403
• /
• 2014

In this paper, we first provide comparison results of preconditioned AOR methods with direct preconditioners $I+{\beta}L$, $I+{\beta}U$ and $I+{\beta}(L+U)$ for solving a linear system whose coefficient matrix is a large sparse irreducible L-matrix, where ${\beta}$ > 0. Next we propose how to find a near optimal parameter ${\beta}$ for which Krylov subspace method with these direct preconditioners performs nearly best. Lastly numerical experiments are provided to compare the performance of preconditioned iterative methods and to illustrate the theoretical results.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.1
• /
• pp.227-237
• /
• 2004

In this paper we presented a Bayesian inference approach for estimating the location and scale parameters of the lognormal distribution using iterative Gibbs sampling algorithm. We also presented estimation of location parameter by two non iterative methods, importance sampling and weighted bootstrap assuming scale parameter as known. The estimates by non iterative techniques do not depend on the specification of hyper parameters which is optimal from the Bayesian point of view. The estimates obtained by more sophisticated Gibbs sampler vary slightly with the choices of hyper parameters. The objective of this paper is to illustrate these tools in a simpler setup which may be essential in more complicated situations.

• Structural Engineering and Mechanics
• /
• v.30 no.4
• /
• pp.467-480
• /
• 2008

An aggregation multigrid method (AMM) is a leading iterative solver in solid mechanics. Recently, AMM is applied for solving Schur Complement system in the FE analysis of shell structures. In this work, an extended application of AMM for solving Schur Complement system in the FE analysis of continuum elements is presented. Further, the performance of the proposed AMM in multiple load cases, which is a challenging problem for an iterative solver, is studied. The proposed method is developed by combining the substructuring and the multigrid methods. The substructuring method avoids factorizing the full-size matrix of an original system and the multigrid method gives near-optimal convergence. This method is demonstrated for the FE analysis of several elastostatic problems. The numerical results show better performance by the proposed method as compared to the preconditioned conjugate gradient method. The smaller computational cost for the iterative procedure of the proposed method gives a good alternative to a direct solver in large systems with multiple load cases.

• Journal of Radiation Protection and Research
• /
• v.42 no.4
• /
• pp.212-221
• /
• 2017

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1427-1443
• /
• 2018

Object-based image analysis (OBIA) allows higher computation efficiency and usability of information inherent in the image, as it reduces the complexity of the image while maintaining the image properties. Superpixel methods oversegment the image with a smaller image unit than an ordinary object segment and well preserve the edges of the image. SLIC (Simple linear iterative clustering) is known for outperforming the previous superpixel methods with high image segmentation quality. Although the input parameter for SLIC, number of superpixels has considerable influence on image segmentation results, impact analysis for SLIC parameter has not been investigated enough. In this study, we performed optimal parameter analysis and evaluation of change detection for SLIC-based superpixel techniques using KOMPSAT data. Forsuperpixel generation, three superpixel methods (SLIC; SLIC0, zero parameter version of SLIC; SNIC, simple non-iterative clustering) were used with superpixel sizes in ranges of $5{\times}5$ (pixels) to $50{\times}50$ (pixels). Then, the image segmentation results were analyzed for how well they preserve the edges of the change detection reference data. Based on the optimal parameter analysis, image segmentation boundaries were obtained from difference image of the bi-temporal images. Then, DBSCAN (Density-based spatial clustering of applications with noise) was applied to cluster the superpixels to a certain size of objects for change detection. The changes of features were detected for each superpixel and compared with reference data for evaluation. From the change detection results, it proved that better change detection can be achieved even with bigger superpixel size if the superpixels were generated with high regularity of size and shape.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.E2
• /
• pp.75-81
• /
• 2003

Variational data assimilation, which is recently introduced to the air quality modeling, is a promising tool for obtaining optimal estimates of initial conditions and other important parameters such as emission and deposition rates. In this paper. two advanced techniques for variational data assimilation, based on the adjoint and quasi-inverse methods, are tested for a simple air quality problem. The four-dimensional variational assimilation (4D-Var) requires to run an adjoint model to provide the gradient information in an iterative minimization process, whereas the inverse 3D-Var (I3D-Var) seeks for optimal initial conditions directly by running a quasi -inverse model. For a process with small dissipation, I3D-Vu outperforms 4D-Var in both computing time and accuracy. Hybrid application which combines I3D-Var and standard 4D-Var is also suggested for efficient data assimilation in air quality problems.

• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.139-151
• /
• 2004

Kim et al. (2002) developed public use SAS-based software for two-way stratification design, which is called SOCSLP We describe the details of a new approach implemented using SOCSLP and key differences between the approach and the sampling schemes of Sitter and Skinner (1994) and Winkler (2001). In addition, a numerical example is given to compare those methods with respect to the probabilities of selecting sample arrays.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.8
• /
• pp.415-422
• /
• 2000

This paper presents a new algorithm for hierarchical optimal control of nonlinear systems. The proposed method is simple because the solutions are obtained by only exchanging informations of coefficient vector based on interaction prediction principle and FWT(fast Walsh transform) in upper and lower level. Since we solve two point boundary problem with Picard's iterative method and the backward integral operational matrix of Walsh function to obtain the optimal vector of each independent subsystem, the algorithm is simple and its operation is fast without inverse matrix and kronecker product operation. In simulation, the proposed algorithm's usefulness is proved by comparison with the global optimal control methods.

• 전기의세계
• /
• v.21 no.3
• /
• pp.48-52
• /
• 1972

The application of pontryagin's Maximum Principle to the optimal control eventually leads to the problem of solving the two point boundary value problem. Most of problems have been related to their own special factors, therfore it is very hard to recommend the best method of deriving their optimal solution among various methods, such as iterative Runge Kutta, analog computer, gradient method, finite difference and successive approximation by piece-wise linearization. The gradient method has been applied to the optimal control of two point boundary value problem in the power systems. The most important thing is to set up some objective function of which the initial value is the function of terminal point. The next procedure is to find out any global minimum value from the objective function which is approaching the zero by means of gradient projection. The algorithm required for this approach in the relevant differential equations by use of the Runge Kutta Method for the computation has been established. The usefulness of this approach is also verified by solving some examples in the paper.