• 대한산업공학회지
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• 제5권1호
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• pp.59-66
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• 1979

The objective of this paper is to develop a stochastic inventory system model under the so-called continuous-review policy with a Poisson one-at-a-time demand process, iid customer inter-arrival times {Xi}, backorders allowed, and constant procurement lead time $\gamma$. The distributions of the so-called inventory position process {$IP_{(t-r)}$} and lead time demand process {$D_{(t-r,t)}$} are formulated in terms of cumulative demand by time t, {$N_t$}. Then, for the long-run expected average annual inventory cost expression, the "ensemble" average is estimated, where the cost variations for stock ordering, holding and backorders are considered stationary.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• 한국해안·해양공학회논문집
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• 제31권3호
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• pp.158-169
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• 2019

추계학적 확률과정의 하나인 감마 확률과정을 이용하여 구조물의 잔류유효수명을 확률론적으로 예측할 수 있는 수학적 모형을 수립하였다. 수립된 모형은 과거부터 현재 시점까지 관측된 피해자료와 관련된 표본의 불확실성과 장래 시간 진행에 따른 누적피해의 불확실성을 올바로 고려할 수 있다. 또한 최소자승법과 모멘트법을 함께 사용하여 경사제의 재령, 운용환경 그리고 피해이력을 고려할 수 있는 모수 추정법을 제시하였다. 먼저 현재 재령의 단일 피해 자료를 갖는 임의의 조건에서 모수에 대한 민감도 분석을 수행하여, 잔류유효수명과 관련된 여러가지 거동 특성들을 분석하였다. 또한 잔류유효수명 예측모형을 경사제에 적용하였다. 경사제 피복재의 피해 이력에 대한 실험자료를 이용하여 감마 확률과정의 모수를 추정하였는데 실험자료와 매우 잘 일치하였다. 해석 결과에 의하면 현재 시점으로부터 상당히 오랜 시간이 경과하면 파괴한계를 초과할 확률이 일정한 값으로 수렴해야 하는 제약 조건을 잘 만족하였다. 한편 기대 잔류유효수명은 피해 이력의 거동 특성에 따라 각기 다르게 산정되었다. 특히 피해의 변동계수가 크면 추계학적으로 산정된 기대 잔류유효수명은 결정론적 회기모형의 해석 결과와 큰 차이를 보인다. 이는 해석과정에 포함된 불확실성의 영향으로 판단된다. 변동계수가 크면 파괴한계에 도달하는 시간의 분포가 넓게 퍼지기 때문이다. 따라서 본 연구에서 수립된 추계학적 잔류유효수명 예측모형은 현재 재령에서 경사제의 피해에 대한 확률적 평가를 수행할 수 있을 뿐만 아니라 장래 시간의 진행에 따른 누적피해의 불확실성을 올바로 고려할 수 있다.

• 대한토목학회논문집
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• 제14권3호
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• pp.523-534
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• 1994

본 연구의 목적은 간헐수문과정인 일강수계열의 모의발생 모델을 개발하는 것이다. 이를 위하여 연구(I)에서는 교대재생과정을 이용하여 강수발생과정을 해석하였으며, 본 연구(II)에서는 강수발생과정으로 Markov 연쇄를 이용하고 습윤일의 강수량 분포를 조합하여 일 강수계열을 모의발생하는 추계학적 모델을 개발하였다. Markov 연쇄로는 상태 2(건조, 습윤)의 1차 연쇄를 사용하였으며, 습윤일의 강수량 분포는 연속확률분포인 Gamma, Pearson Type-III(PT3), Extremal Type-III(T3E), Weibull 분포를 적용하였다. 일 강수계열 자료의 계절적 변동성을 고려하여 월별로 분리하여 해석하였으며, 강수발생과정과 습윤일의 강수량과정을 조합하여 구성한 두 개의 모의발생 모델 M-W, M-G 모델을 낙동강과 섬진강 유역의 7개 관측소에 적용하여 관측치와 모의발생치를 비교하므로써 모의발생 모델의 적용성을 확인하였다.

• Structural Monitoring and Maintenance
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• 제2권2호
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• pp.133-143
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• 2015

Civil engineering infrastructure is aging and requires cost-effective maintenance strategies to enable infrastructure systems operate reliably and sustainably. This paper presents an approach for determining risk-cost balanced repair strategy of corrosion damaged reinforced concrete structures with consideration of uncertainty in structural resistance deterioration. On the basis of analytical models of cover concrete cracking evolution and bond strength degradation due to reinforcement corrosion, the effect of reinforcement corrosion on residual load carrying capacity of corroded reinforced concrete structures is investigated. A stochastic deterioration model based on gamma process is adopted to evaluate the probability of failure of structural bearing capacity over the lifetime. Optimal repair planning and maintenance strategies during the service life are determined by balancing the cost for maintenance and the risk of structural failure. The method proposed in this study is then demonstrated by numerical investigations for a concrete structure subjected to reinforcement corrosion. The obtained results show that the proposed method can provide a risk cost optimised repair schedule during the service life of corroded concrete structures.

• 대한토목학회논문집
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• 제14권3호
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• pp.509-521
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• 1994

본 연구는 간헐 수문과정인 일 강수계열의 모의발생 모델을 개발한 것으로서, 일 강수계열의 구조적 특성인 강수발생과정과 습윤일의 강수량과정을 고려하였다. 본 연구는 두편이 논문으로 구성되어 있으며, 연구(I)에서는 강수발생과정을 위하여 고대재생과정(ARP)을 이용하였으며, 건조 습윤계속기간 분포에 대해서는 TBD, TPD, TNBD, LSD의 4가지 이산형 확률분포를 적용하였다. 후속논문인 연구(II)에서는 강수발생과정으로 Markov 연쇄모델을 이용한다. 그리고 습윤일의 강수량 분포에 대해서는 Gamma 분포, Pearson Type-III 분포, Type-III 극치분포, 3모수 Weibull 분포의 4가지 연속형 확률분포를 적용하였다. 연구(I)에서는 낙동강 유역의 대구, 고령, 밀양, 영주 관측소 및 섬진강 유역의 하동, 순창, 구례 관측소의 일 강수계열 자료를 사용하였으며, 강수발생과정과 습윤일의 강수량과정을 조합하여 구성한 두가지의 일 강수계열 모의발생 모델 A-W, A-G 모델의 적용성을 확인하였다.

• 전기학회논문지
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• 제56권2호
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• pp.248-253
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• 2007

Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

• 한국정밀공학회지
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• 제1권1호
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• pp.34-49
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• 1984

The stability of machine tool systems is analyzed by considering the machining process as a stochastic process without decomposing into machine tool structural dynamics and cutting processes. In doing so the time series analysis technique developed by Wu and Pandit is applied systematically to the relative vibration between cutting tool and work- piece measured under actual working conditions. Various characteristic properties derived from the fitted ARMA(Autoregressive Moving Average) Models and those from raw data directly are investigated in relation with the system stability. Both damping ratio and absolute value of the characteristic roots of the AR part of the most significant dynamic mode are preferred as stability indicating factors to the other pro-perties such as theoretical variance .gamma. (o) or absolute power of the most dominant dynamic mode. Maximum aplitude during a certain interval and variance estimated from raw data are shown to be very sensi- tive to the type of the signal and the location of measurement point although they can be obtained rather easily. The relative vibration signal is also analyzed by FFT(Fast Fourier Transform) Analyzer for the purpose of comparison with the spectrums derived from the fitted ARMA models.

• 전기학회논문지
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• 제57권8호
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• pp.1305-1310
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• 2008

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제12권3호
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• pp.171-190
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• 2008

With growing applications of wireless video streaming, an efficient video traffic model featuring modern high-compression techniques is more desirable than ever, because the wireless channel bandwidths are ever limited and time-varying. We propose a modeling and analysis method for video traffic by a class of stochastic processes, which we call '$GEO^Y/G/{\infty}$ input processes'. We model video traffic by $GEO^Y/G/{\infty}$ input process with gamma-distributed batch sizes Y and Weibull-like autocorrelation function. Using four real-encoded, full-length video traces including action movies, a drama, and an animation, we evaluate our modeling performance against existing model, transformed-M/G/${\infty}$ input process, which is one of most recently proposed video modeling methods in the literature. Our proposed $GEO^Y/G/{\infty}$ model is observed to consistently provide conservative performance predictions, in terms of packet loss ratio, within acceptable error at various traffic loads of interest in practical multimedia streaming systems, while the existing transformed-M/G/${\infty}$ fails. For real-time implementation of our model, we analyze G/D/1/K queueing systems with $GEO^Y/G/{\infty}$ input process to upper estimate the packet loss probabilities.