• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1179-1185
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• 2003

시뮬레이션 상의 입력모델에 대한 기존의 연구는 과거의 자료를 바탕으로 선형의 모수적인 (parametric) 모형을 개발하는데 초점을 두고 있다. 그러나 이 경우에는 입력이 매우 복잡한 형태를 가지면 모수적인 모형을 잦는 것이 불가능해지므로 비모수적인(non-parametric) 접근방법이 절실한 실정이다 예로 인터넷 트래픽 모델의 시뮬레이션 수행시 입력으로 제공되는 단위 시간당 요구되는 웹 페이지의 수 같은 경우 데이터들 간데 종속관계가 매우 심하고 복잡하여 모수적 모형을 세우는데 어려움이 있다. 이러한 시스템들을 시뮬레이션 방법으로 분석 하고자 할 때, 기존의 trace-driven 시뮬레이션 방법이나 모수적 모형을 찾아 다수의 사실적인 시뮬레이션 입력 자료를 확보하는 것은 현실적으로 어려움이 있다. 따라서. 비모수적인 방법으로 다수의 사실적인 시뮬레이션 입력 자료를 생성하는 것이 필요하다. 이러한 비모수적인 방법에 대한 평가기준 설정은 시뮬레이션 상의 입력 모델에 대한 타당성을 제시한다는 점에서 또한 매우 중요하다. 본 논문에서는 붓트스트 랩의 방법중의 하나인 임계값 붓트스트랩을 이용하여 시뮬레이션 입력 자료 생성 방법을 개발하였고 Turing test를 통해 붓스트랩으로 생성산 입력 시나리오를 검증하였다.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.67-79
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• 2011

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.254-260
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• 2005

이 논문에서는 산사태 취약성 분석을 목적으로 확률론적 공간통합의 틀 안에서 범주형 자료와 연속형 자료를 효율적으로 처리할 수 있는 비모수적 우도비 추정 모델과 모수적 예측적 판별 분석 모델을 적용하였다. 적용 모델의 비교를 위해 1998년 여름철 산사태로 많은 피해를 입은 경기도 장흥 지역과 충청북도 보은 지역을 대상으로 사례연구를 수행하였다. 장흥 지역에서는 두 모델이 유사한 예측 능력을 나타내었으나, 보은 지역에서는 모수적 예측적 판별 분석 모델이 상대적으로 높은 예측 능력을 나타내었다. 결론적으로 제안한 두 모델은 산사태 취약성 분석을 위한 연속형 자료 표현에 효율적으로 적용될 수 있으며, 두 모델이 개별적인 연속형 자료 표현의 특성을 가지고 있기 때문에 다른 사례 연구를 통한 검증 작업이 병행되어야 할 것으로 생각된다.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.313-320
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• 2008

A typical software development life cycle consists of a series of phases, each of which has some ability to insert and detect defects. To achieve desired quality, we should progress the defect removal with the all phases of the software development. The well-known model of phase-based defect profile is Gaffney model. This model assumes that the defect removal profile follows Rayleigh curve and uses the parameters as the phase index number. However, these is a problem that the location parameter cannot present the peak point of removed defects when you apply Gaffney model to the actual situation. Therefore, Gaffney model failed to represent the actual defect profile. This paper suggests two different models: One is modified Gaffney model that introduce the parameter of Putnam's SLIM model to replace of the location parameter, the other is the growth function model because the cumulative defect profile shows S-shaped. Suggested model is analyzed and verified by the defect profile sets that are obtained from 5 different software projects. We could see from the experiment, the suggested model performed better result than Gaffney model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.42-54
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• 2019

Crop models depend on a large number of input parameters including the cultivar parameters that represent the genetic characteristics of a given cultivar. The cultivar parameters have been estimated using high quality data for crop growth, which require considerable costs and efforts. The objective of this study was to examine the feasibility of using low quality data for the parameter estimation. In the present study, the cultivar parameters for cv. Shindongjin were estimated using the data obtained from the report of new cultivars development and research from 2005 to 2016. The root mean square errors (RMSE) of the heading dates were less than 3 days when the parameters associated with phenology were estimated. In contrast, the coefficient of determination for yield tended to be less than 0.1. The large errors incurred by the fact that no growth data collected over a season was used for parameter estimation. This suggests that detailed observation data needs to be prepared for parameter calibration, which would be aided by remote sensing approaches. The occurrence of natural disasters during a growing season has to be considered because crop models cannot take into account the effects of those events. Still, our results provide a reasonable range for the parameters, which could be used to set the boundary of a given parameter for cultivars similar to cv. Shindongjin in further studies.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.353-358
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• 2015

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.133-151
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• 2007

The purpose of this article is to examine the effects of item parceling on the consistency of significance testing of the causal parameters with regard to the relationship between the relevant constructs, as well as the effects of the item parceling on the goodness-of-fit indices of LISREL's general models. Most of the researchers' major purpose of using structural equation modeling (SEM) is to test their research hypotheses associated with the causal parameters. Therefore, we investigated three general models of LISREL, rather than the frequently used confirmatory factor analytic (CFA) models by many other researchers. The results of the study showed that there was a high level of consistency in the calculated test statics of causal parameters between the item-parceled solutions and the item-level solutions, and that the item-parceled solutions had better goodness-of-fit indices, such as GFI, AGFI, CFI, and NFI, than the solutions at the item level. However, in terms of RMSEA, there was no such tendency.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.79-87
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• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

• The Mathematical Education
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• v.33 no.2
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• pp.167-175
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• 1994

결측치를 가진 회귀모형의 모수 추정법을 이용하여 목표지향형 평가 모델에서 기초고사(X)와 신고사(I)(Y), 신고사(II)(Z)등 두 개 이상의 고사지로 이루어진 고사집에서 기초고사에는 결측치가 없고 신고사(I), 신고사(II)등에는 결측치가 있는 경우 모수의 최우추정량 계산법을 논하고 E.M. 알고리즘과 평가치는 희귀방적식화에 의하여 우리나라 중등학교 학생의 수학학습능력과 수학적 사고력의 크기를 변별하며 학생들의 진능력이 반영된 평가모델과 최종 성적을 평가 할 수 있는 계산법을 제시하였다.