• Journal of Navigation and Port Research
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• v.30 no.1 s.107
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• pp.105-111
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• 2006

A LMTT (Linear Motor-based Transfer Technology) is a horizontal transfer system for the yard automation This system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that consists of stator modules on the rail and shuttle car. In this research, the kriging interpolation method using sequential sampling is utilized to find the optimum design of a mover in LMTT. The design variables are considered as the transverse, longitudinal and wheel beam's thicknesses. The objective function is set up as weight, while the constant functions are set up as the stresses generated by four loading conditions. The optimum results obtained by the suggested method are compared with those determined by the GENESIS.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.199-201
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• 2005

This paper proposes a method to evaluate the TTC by considering uncertainty of weather. Impact of the contingency on the system performance could not be addressed in a deterministic way because of the random nature of the system equipment outage and the increase of outage probability according to weather condition. For this reasons, probabilistic approach is necessary to realize evaluation of TTC. This method uses a sequential MCS. In sequential simulation, the chronological behavior of the system is simulated by sampling sequence of the system operating states based on the probability distribution of the component state duration. Therefor, MCS is used to accomplish the probabilistic calculation of TTC with consideration of weather condition.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• Proceedings of the KSRS Conference
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• v.2
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• pp.816-819
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• 2006

One of the most difficult parameters to measure in the sea is current speed and direction. Recently, efforts are being made to estimate the ocean current vectors by utilizing sequential satellite imageries. In this study, we attempted to estimated sea surface current vector (sscv) by using satellite ocean color imageries of SeaWifs around the Korean Peninsula. This ocean color image data has 1-day sampling interval and spatial resolution of 1x1 km. Maximum cross-correlation method is employed which is aimed to detect similar patterns between sequential images. The estimated current vectors are compared to the surface geostrophic current vectors obtained from altimeter of sea level height data. In utilizing the color imagery data, some limitations and drawbacks exist so that in warm water region where phytoplankton concentration is relatively lower than in cold water region, estimation of sscv is poor and unreliable. On the other hand, two current vector fields agree reasonably well in the Korean South Sea region where high concentration of chlorophyll-a and weak tide is observed. In the future, with ocean color images of shorter sampling interval by COMS satellite, the algorithm and methodology developed in the study would be useful in providing the information for the ocean current around Korean Peninsula.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.6
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• pp.546-554
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• 2015

Ultra-wideband(UWB) radar is widely used in many penetration radar applications, such as ground-penetrating radar and foliage-penetrating radar, because it has many advantages in detecting concealed objects. One type of UWB radar system is random noise radar, which many be robust to jamming environment. However conventional random noise radar requires high-speed analog-to-digital convertor(ADC) for matched filtering. In this thesis, a pseudo-random noise radar system that maintains anti-jamming characteristics but does not require high-speed ADC is researched. and The UWB system is implemented in a low frequency system, and its performance has been demonstrated by experiment, which proves the concept of the proposed pseudo-random noise radar system.

• The KSFM Journal of Fluid Machinery
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• v.12 no.3
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• pp.7-12
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• 2009

In this paper, shape optimization based on three-dimensional flow analysis has been performed for impeller design of centrifugal compressor. To evaluate the objective function of an isentropic efficiency, Reynolds-averaged Navier-Stokes equations are solved with SST (Shear Stress Transport) turbulence model. The governing equations are discretized by finite volume approximations. The optimization techniques based on the radial basis neural network method are used for the optimization. Latin hypercube sampling as design of experiments is used to generate thirty design points within design space. Sequential quadratic programming is used to search the optimal point based on the radial basis neural network model. Four geometrical variables concerning impeller shape are selected as design variables. The results show that the isentropic efficiency is enhanced effectively from the shape optimization by the radial basis neural network method.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.389-396
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• 2001

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has a faster searching speed and lower missing scene change detection than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect missing frame and searching precision. In this study, the whole moving pictures were primarily retrieved threshold by the temporal difference of histogram scene change detection method. We suggested a dynamic threshold algorithm using cut detection interval and derived an equation formula to determine optimal primary retrieval threshold which can cut detection interval computation. Experimental results show that the proposed dynamic threshold algorithm using cut detection interval method works up about 30 percent in precision of performance than the sequential searching method.

• Economic and Environmental Geology
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• v.30 no.5
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• pp.493-498
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• 1997

The purpose of this study is to develop geostatistical model for evaluating the abundance of deep-sea manganese nodule. The abundance data used in this study were obtained from the KODOS (Korea Deep Ocean Study) area. The variation of nodule abundance was very high within short distance, while sampling methods was very limited. As the distribution of nodule abundance showed non-gaussian, indicator simulation method was used instead of conditional simulation method and/or ordinary kriging. The abundance data were encoded into a series of indicators with 6 cutoff values. They were used to estimate the conditional probability distribution function (cpdf) of the nodule abundance at any unsampled location. The standardized indicator variogram models were obtained according to variogram analysis. This SIS method had the advantage over other traditional techniques such as the turning bands method and ordinary kriging. The estimating values by indicator conditional simulation near high abundance area were more detailed than by ordinary kriging and indicator kriging. They also showed better spatial characteristics of distribution of nodule abundance.

• Korean journal of applied entomology
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• v.42 no.1
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• pp.29-34
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• 2003

Dispersion pattern for adult citrus red mite (CRM), Panonychus citri (McGregor) using by Taylor's power law (TPL) and Iwao's patchiness regression (IPR) was determined to develop a monitoring method on citrus orchards, on Jeju, in Autumn season, during 2001 and 2002.CRM population was sampled by collecting leaves and fruits. The relationships of CRM adults between leaf and fruit were analyzed by different season. The regression equation for CRM adults between leaf (X) and fruit (Y) was ln(Y+1) : 1.029 ln(X+1) ( $r^2$ : 0.80). The density of CRM was higher on fruit than on leaf according to fruit maturing level. TPL provided better description of mean-variance relation-ship for the dispersion indices compared to IPR. Slopes and intercepts of TPL from leaf and fruit samples did not differ between sample units and surveyed years. Fixed-precision levels (D) of a sequential sampling plan were developed using Taylor's power law parameters generated from adults of CRM in leaf sample. Sequential sampling plans for adults of CRM were developed for decision making CRM population level based on the different action threshold levels (2.0,2.5 and 3.0 mites per leaf) with 0.25 precision. The maximum number of trees and required number of trees sampled on fixed sample size plan on 2.0,2.5 and 3.0 thresholds with 0.25 precision level were 19, 16 and 15 and their critical values T$_{critical}$ at were 554,609 and 659, respectively. were 554,609 and 659, respectively.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1063-1068
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• 2008

A lens system of a camera module for mobile phones is comprised of the composition and design of various shapes of lens. To improve responses such as the modular transfer function (MTF), a lens system should always be constructed by considering uncertainty that can be caused by manufacturing and assembly error. In this study, tolerance optimization using the Latin Hypercube Sampling (LHS) technique is performed. In order to reduce the computational burden of the tolerance optimization process and decrease the influence from numerical noise effectively, we use the Progressive Quadratic Response Surface Modeling (PQRSM), which is one of Sequential Approximate Optimization (SAO) techniques. Using this method, we achieved optimal tolerance for each lens and obtained reliability for satisfying user‘s requirements. In addition, through the design process the manufacturing and assembly cost of a lens system was reduced.