• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.658-663
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• 1987

The new controller developed here, which is the facility with only one measurement, is a new concept for the level controller of the existing nuclear steam generator. A MACS (Microcomputer-based Adaptive Control System of a Steam Generator) is quite practical and efficient, and has also simple structure and higher flexibility in the installment for actual plant. A key ingredient of this system is adaptive regulator which can calculate adaptive, optimal valve position in response to changes in the dynamics of the process and the disturbances. In spite of many difficulties in the steam generator water level control at low power, it can be concluded from the experimental and simulation results, that the MACS can provide optimal, robust steam generator level control from zero to full power. The amount of the control input effort can be reduced by adjusting the weighting factor. However, the steady state water level errors are generated. To avoid the steady errors, the different adaptive algorithm should be investigated in the future. The 3 second sampling time is acceptable for this system. However, action should be taken to shorten the sampling time for better digital control.

• 한국CDE학회논문집
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• 제3권1호
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• pp.15-21
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• 1998

Several adaptive and direct slicing methods have been developed to make the slice data for RP parts with better accuracy and speed. This research deals with a new adaptive slicing algorithm that shows drastic improvement in computing time for calculating the slices of a part. First, it uses less number of sampling points fur each slice in determining the thickness of the next slice. Secondly, the idea of contour map is utilized to determine the optimal sampling point on each slice. Thirdly, the calculation efficiency is further improved by introducing vertical character lines of the given part. The results in terms of accuracy and speed are compared with the existing methods.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1999년도 가을 학술발표회 논문집
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• pp.351-358
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• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• 한국통신학회논문지
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• 제32권1A호
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• pp.75-82
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• 2007

이 논문은 IEEE 802.15.4기반 센서 네트워크에서 파워 소모를 줄이는 방법을 고려하였다. 파워 소모를 줄이기 위해 함께 동작되는 센서들의 sampling time에 바탕을 두어 비콘 구간을 조절하는 적응적 비콘 구간 제어방법과 실제 전송될 데이터 량에 따라 적응적으로 슈퍼프레임 구간을 조절하는 적응적 슈퍼프레임 구간 제어 방법을 제안한다. 또한 위 두 가지 방법의 통합 방법인 적응적 통합 제어 방법을 제안한다. 이 논문에서 제안한 파워 절감 방식의 유효성을 보이기 위해 ns-2 시뮬레이터를 통해 모의실험 하였다.

• 한국통신학회논문지
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• 제28권11B호
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• pp.1038-1049
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• 2003

트래픽 부하 측정은 네트웍 트래픽 엔지니어링의 기반이 된다. 그러나 고속 링크에서 트래픽 부하 정보를 얻기 위해 모든 패킷을 측정하는 것은, 라우터의 패킷 포워딩 성능을 저해시키므로 확장성이 결여된다. 이에 따라 샘플링 기법이 트래픽 측정의 대안으로 제시되었다. 샘플링은 라우터의 성능 저해를 최소화시킬 수 있으나 샘플링으로 예측되는 트래픽 부하는 실제 트래픽 부하와 차이를 보이게 되며, 이와 같은 오류가 제한되지 못한다면 측정값을 기반으로 하는 응용들에 부영향을 미치게 된다. 본 논문에서는 샘플링 오류를 오류 허용범위 내로 제한시킬 수 있는 적응성 있는 패킷 샘플링 기법을 제안한다. 제안 기법은 수학적 분석을 통해 얻어진 부하 예측 오류에 영향을 미치는 주요 트래픽 파라메터를 각 블록의 시작마다 예측하여 샘플링 확률을 동적으로 적응시킨다. 본 논문에서는 또한 실제 측정된 인터넷 트래픽을 이용하여 제안 기법의 확장성과 성능을 검증하였다

• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.593-610
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• 2015

적응 기각 추출 (adaptive rejection sampling)방법은 특정한 형태의 확률분포로 부터 확률표본을 추출하기 위한 대표적인 난수생성기 (random number generator)로서, 추출된 표본으로부터 제안분포 (proposal distribution)가 개선이 되는 장점을 가지고 있다. 그러나, 기존에 제안된 적응기각추출 방법은 확률분포의 형태가 로그-오목 함수 (log-concave function)인 경우에만 사용이 가능하기 때문에 적용범위가 제한적이다. 최근의 연구결과에서는, 이러한 단점을 보완하기 위해 다양한 형태의 적응기각추출이 진행되고 있으며, 이에 본 논문에서는 기존의 적응기각추출 방법을 포함한 총 5가지의 난수 생성 방법에 대해서 고찰하고, 아울러 모의실험을 통해 각 방법들간의 성능에 대하여, 적합성과 효율성의 관점에서 실증적으로 비교 분석하도록 한다.

• Smart Structures and Systems
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• 제17권4호
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• International Journal of Contents
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• 제10권3호
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• pp.17-25
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• 2014

In this paper, we describe an adaptive Markov chain Monte Carlo-based particle filter that effectively addresses real-time multi-face tracking on mobile platforms. Because traditional approaches based on a particle filter require an enormous number of particles, the processing time is high. This is a serious issue, especially on low performance devices such as mobile phones. To resolve this problem, we developed a tracker that includes a more sophisticated likelihood model to reduce the number of particles and maintain the identity of the tracked faces. In our proposed tracker, the number of particles is adjusted during the sampling process using an adaptive sampling scheme. The adaptive sampling scheme is designed based on the average acceptance ratio of sampled particles of each face. Moreover, a likelihood model based on color information is combined with corner features to improve the accuracy of the sample measurement. The proposed tracker applied on various videos confirmed a significant decrease in processing time compared to traditional approaches.

• Journal of the Korean Statistical Society
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• 제35권2호
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• pp.157-166
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• 2006

This research proposes an efficient Monte Carlo algorithm for computing error probability in high performance digital communication st stems. It characterizes special features of the problem and suggests an importance sampling algorithm specially designed to handle the problem. It uses a shifted exponential density as the importance sampling density, and shows an adaptive way of choosing the rate and the origin of the shifted exponential density. Instead of equal allocation, an intelligent allocation of the samples is proposed so that more samples are allocated to more important part of the error probability. The algorithm uses the nested feature of the error space and avoids redundancy in estimating the probability. The algorithm is applied to an example data set and shows a great improvement in accuracy of the error probability estimation.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.355-363
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• 2007

In this paper a Bayesian estimation of the two-parameter kappa distribution was discussed under the noninformative prior. The Bayesian estimators are obtained by the Gibbs sampling. The generation of the shape parameter and scale parameter in the Gibbs sampler is implemented using the adaptive rejection Metropolis sampling algorithm of Gilks et al. (1995). A Monte Carlo study showed that the Bayesian estimators proposed outperform other estimators in the sense of mean squared error.