• 물과 미래
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• 제22권4호
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• pp.409-410
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• 1989

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• International Journal of Control, Automation, and Systems
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• 제5권4호
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• pp.355-363
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• 2007

The paper deals with the Kalman stochastic filtering problem for linear continuous-time systems with both instantaneous and time-delayed measurements. Different from the standard linear system, the system state is corrupted by multiplicative white noise, and the instantaneous measurement and the delayed measurement are also corrupted by multiplicative white noise. A new approach to the problem is presented by using projection formulation and reorganized innovation analysis. More importantly, the proposed approach in the paper can be applied to solve many complicated problems such as stochastic $H_{\infty}$ estimation, $H_{\infty}$ control stochastic system with preview and so on.

• 한국CDE학회논문집
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• 제17권2호
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• pp.111-122
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• 2012

Capacity planning plays an important role not only for master production plan but also for facility or layout design in shipbuilding. Product work breakdown structure, attributes of production resources, and production method or process data are associated in order to make the discrete event simulation model of shipyard layout plan. The production amount of each process and the process time is assumed to be stochastic. Based on the stochastic discrete event simulation model, the production capacity of each facility in shipyard is estimated. The stochastic model of product arrival time, process time and transferring time is introduced for each process. Also, the production capacity is estimated for the assumed master production schedule.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 춘계학술발표회논문집(2)
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• pp.725-730
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• 1996

In this work, the software behavior under memory faults in operation phase is modeled and simulated using the stochastic activity network, generalized stochastic Petri nets. This networks permit the representation of concurrency, timeliness, fault tolerance, and degradable performance of system and provide a means for determining the stochastic behavior of a complex system. We estimate the reliability of an application software in the digitized system in nuclear power plants and show the sensitivity of the software reliability to the major physical parameters which affect the software failure in normal operation phase. We found that the effects of the hardware faults on the software failure should be considered for predicting the software dependability accurately in operation phase.

• Structural Engineering and Mechanics
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• 제5권6호
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• pp.839-851
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• 1997

Closed-form solutions are analytically derived for stochastic properties of earthquake ground motion fields, which are conditioned by an observed time series at certain observation sites and are characterized by spectra with uncertainties. The theoretical framework presented here can estimate not only the expectations of such simulated earthquake ground motions, but also the prediction errors which offer important information for the field of engineering. Before these derivations are made, the theory of conditional random fields is summarized for convenience in this study. Furthermore, a method for stochastic interpolation of power spectra is explained.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.292-302
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• 2018

This study proposes a Bayesian stochastic frontier model that is well-suited to productivity/efficiency analysis particularly using panel data. A unique feature of our proposal is that both production frontier and efficiency are estimable for each individual firm and their linkage to various firm characteristics enriches our understanding of the source of productivity/efficiency. Empirical application of the proposed analysis to Human Capital Corporate Panel data enables identification and quantification of the effects of Human Resource factors on firm efficiency in tandem with those of firm types on production frontier. A comprehensive description of the Markov Chain Monte Carlo estimation procedure is forwarded to facilitate the use of our proposed stochastic frontier analysis.

• 한국시뮬레이션학회논문지
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• 제18권2호
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• pp.49-55
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• 2009

본 연구는 확률적 시스템에서 관심 성과함수의 기대치의 최적을 유도하는 서비스 자원의 최적 배분 문제를 조사하였다. 이러한 목적으로 통제변수를 활용하여 성과함수 기대치에 대한 서비스 자원 파라미터의 gradient를 구하는 방법을 제안하고 이를 최적화 기법의 탐색과정에 적용하여 가용 자원의 최적 배분 문제를 분석하였다. 제안된 gradient 추정 방법은 시뮬레이션 실험에서 입력 파라미터의 차원이 증가하더라도 추가로 표본점의 수를 증가시킬 필요가 없이 단일점에서 시뮬레이션 반응 결과만을 활용하고 또한 시뮬레이션의 발전과정에서 성과함수와 입력 파라미터 사이의 논리적인 관계를 기술할 필요가 없어 적용하기에 편리하다고 볼 수 있다. 본 연구의 결과를 다 차원 파라미터 공간으로의 확장하는 문제와 다양한 형태의 시뮬레이션 모형으로 적용 문제는 향후 연구해야 할 과제로 생각된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.73-77
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• 1986

Optimal input design problem for linear regression model with constrained output variance has been considered. It is shown that the optimal input signal for the linear regression model can also be realized as an ARMA process. Monte-Carlo simulation results show that the optimal stochastic input leads to comparatively better estimation accuracy than white input signal.

• Wind and Structures
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• 제6권2호
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• pp.107-126
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• 2003

This paper describes a simple and practical approach through the application of Linear Stochastic Estimation (LSE) to reconstruct wind-induced pressure time series from the covariance matrix for structural load analyses on a low building roof. The main application of this work would be the reduction of the data storage requirements for the NIST aerodynamic database. The approach is based on the assumption that a random pressure field can be estimated as a linear combination of some other known pressure time series by truncating nonlinear terms of a Taylor series expansion. Covariances between pressure time series to be simulated and reference time series are used to calculate the estimation coefficients. The performance using different LSE schemes with selected reference time series is demonstrated by the reconstruction of structural load time series in a corner bay for three typical wind directions. It is shown that LSE can simulate structural load time series accurately, given a handful of reference pressure taps (or even a single tap). The performance of LSE depends on the choice of the reference time series, which should be determined by considering the balance between the accuracy, data-storage requirements and the complexity of the approach. The approach should only be used for the determination of structural loads, since individual reconstructed pressure time series (for local load analyses) will have larger errors associated with them.