• 수산경영론집
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• 제41권3호
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• pp.45-58
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• 2010

This study estimated the technical efficiency of coastal composite fishery in Korea by using the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA) methods, and the results on the respective method were compared. In the DEA method, the constant returns to scale (CRS) and the variable returns to scale (VRS) output-oriented DEA models were separated and technical efficiencies were estimated, respectively. The average estimated value of technical efficiency by the SFA method (0.633) was found to be lower than that by the VRS-DEA method (0.738), while it was higher than that by the CRS-DEA method (0.479). It was found that strong correlation exists between the SFA method and the VRS-DEA method. The method which can utilize both methods in mutually complementing way for the estimation of technical efficiency was also considered.

• 대한산업공학회지
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• 제27권2호
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• pp.169-175
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• 2001

Most of studies on automated storage/retrieval (AS/R) system assumed that storage capacity is given, although it is a very important decision variable in the design phase. We propose a simple algorithm to estimate the required storage capacity, i.e., number of aisles and number of openings in vertical and horizontal directions in each aisle, of an AS/R system under stochastic demand, in which storage requests occur endogenously and exogenously while the retrieval requests occur endogenously from the machines. Two design criteria, maximum permissible overflow probability and maximum allowable storage/retrieval (S/R) machine utilization, are used to compute the storage capacity. This model can be effectively used in the design phase of new AS/R systems.

• 한국멀티미디어학회논문지
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• 제11권6호
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• ETRI Journal
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• 제45권1호
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• pp.119-130
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• 2023

We propose a quantized gradient search algorithm that can achieve global optimization by monotonically reducing the quantization step with respect to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the white noise hypothesis states, a quantization step is sufficiently small and the quantization is well defined, the round-off error caused by quantization can be regarded as a random variable with identically independent distribution. Thus, we rewrite the searching equation based on a gradient descent as a stochastic differential equation and obtain the monotonically decreasing rate of the quantization step, enabling the global optimization by stochastic analysis for deriving an objective function. Consequently, when the search equation is quantized by a monotonically decreasing quantization step, which suitably reduces the round-off error, we can derive the searching algorithm evolving from an optimization algorithm. Numerical simulations indicate that due to the property of quantization-based global optimization, the proposed algorithm shows better optimization performance on a search space to each iteration than the conventional algorithm with a higher success rate and fewer iterations.

• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.489-499
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• 2000

We propose a new instrumental variable estimator of the complex parameter of a class of univariate complex-valued diffusion processes defined by the possibly non-stationary and/or nonlinear stochastic differential equations. On the basis of the exact finite sample distribution of the pivotal quantity, we construct the exact confidence intervals and the exact tests for the parameter. Monte-Carlo simulation suggests that the new estimator seems to provide a viable alternative to the maximum likelihood estimator (MLE) for nonlinear and/or non-stationary processes.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• 대한산업공학회지
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• 제13권1호
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• pp.61-67
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• 1987

A tool for optimizing simulated discrete variable stochastic systems, SIMICOM was developed and presented in [5]. In this paper an evaluation of its performance and results of comparisons with other popular methods for dealing with simulation-optimization problems will be provided. Based on several test problems it is concluded that SIMICOM dominates those methods.

• 대한기계학회논문집A
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• 제39권1호
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• pp.63-69
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• 2015

본 논문의 주 목적은 최대하중조건을 변화시키면서 확률변수의 잔차를 이용하여 확률론적 피로균열전파모델들을 평가하고, Mg-Al-Zn 합금의 피로균열성장거동의 변동성을 묘사하기에 적합한 확률론적 모델을 제시하는 것이다. 평가에 사용된 모델은 피로균열성장의 변동성을 나타내기 위하여 실험적 피로균열전파모델인 Paris-Erdogan 모델, Walker 모델, Forman 모델과 수정 Forman 모델에 확률변수를 도입한 모델이다. 최대하중조건에 따른 Mg-Al-Zn 합금의 피로균열전파거동의 확률적 변동성을 묘사하기에 적합한 모델은 '확률론적 Paris-Erdogan 모델'과 '확률론적 Walker 모델'임을 밝혔으며, 최대하중조건이 피로균열성장의 확률적 변동성에 미치는 영향 또한 고찰하였다.

• Earthquakes and Structures
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• 제1권1호
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• pp.43-67
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• 2010

A gradient-based evolutionary optimization methodology is presented for finding the optimal design of both the added dampers and their supporting members to minimize an objective function of a linear multi-storey structure subjected to the critical ground acceleration. The objective function is taken as the sum of the stochastic interstorey drifts. A frequency-dependent viscoelastic damper and the supporting member are treated as a vibration control device. Due to the added stiffness by the supplemental viscoelastic damper, the variable critical excitation needs to be updated simultaneously within the evolutionary phase of the optimal damper placement. Two different models of the entire damper unit are investigated. The first model is a detailed model referred to as "the 3N model" where the relative displacement in each component (i.e., the spring and the dashpot) of the damper unit is defined. The second model is a simpler model referred to as "the N model" where the entire damper unit is converted into an equivalent frequency-dependent Kelvin-Voigt model. Numerical analyses for 3 and 10-storey building models are conducted to investigate the characters of the optimal design using these models and to examine the validity of the proposed technique.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.189-192
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• 2005

본 논문에서는 병렬 데이터베이스 아키텍처로 널리 사용되고 있는 공유 데이터베이스 시스템에 대하여 각 구성요소에 대한 고장을 고려한 신뢰도 모델을 모델링 한다. 각 구성요소인 데이터베이스, 메모리, 프로세서, 버스는 개별적으로 고장이 날 수 있으며, 복구 모델을 이용하여 복구 될 수 있다. 시스템이 동작하는 한 복구가 가능하며, 데이터베이스가 고장 나거나, 시스템 구성요소인 프로세서, 메모리, 버스가 하나라도 고장 나면 전체 시스템이 다운되는 것으로 가정한다. 이러한 고장 및 복구조건을 고려한 시스템의 신뢰도 분석을 페트리 네트의 확장 모델이며 모델링 기능이 풍부한 마르코프 reward 모델을 이용하여 수행한다. Stochastic Reward Net(SRN)이 갖고 있는 variable cardinality, enabling 함수, 시간천이 우선순위 등의 기능을 이용하여 신뢰도 모델을 개발한다.