The stochastic method is applied to simulate strong ground motions at seismic stations of seven metropolises in South Korea, creating an earthquake scenario based on the causative fault of the 2016 Gyeongju earthquake. Input parameters are established according to what has been revealed so far for the causative fault of the Gyeongju earthquake, while the ratio of differences in response spectra between observed and simulated strong ground motions is assumed to be an adjustment factor. The calculations confirm the applicability and reproducibility of strong ground motion simulations based on the relatively small bias in response spectra between observed and simulated strong ground motions. Based on this result, strong ground motions by a scenario earthquake on the causative fault of the Gyeongju earthquake with moment magnitude 6.5 are simulated, assuming that the ratios of its fault length to width are 2:1, 3:1, and 4:1. The results are similar to those of the empirical Green’s function method. Although actual site response factors of seismic stations should be supplemented later, the simulated strong ground motions can be used as input data for developing ground motion prediction equations and input data for calculating the design response spectra of major facilities in South Korea.

We estimated decreasing rate of indoor air pollutants with are airborne bacteria, airborne fungi, formaldehyde, total volatile organic compounds, PM10, and PM2.5 in 10 children’s hospitals and 6 childbirth houses located in Seoul and Gyeonggi-do from November to December in 2012. Sectional period was respectively divided for operating and non-operating the air cleaners. There was a trend that concentration of surveyed pollutants in children’s hospitals and childbirth houses during operating period decreased among indoor air. We used Monte-Calro simulation to remove uncertainty and identify efficiency of eliminated pollutants such as surveyed pollutants by the air cleaners. Average efficiency of removal were 61.39 ± 21.42% for airborne bacteria, 71.77 ± 19.65% for airborne fungi, 73.37 ± 24.62% for formaldehyde, 71.20 ± 25.96% for total volatile organic compounds, 65.16 ± 23.80% for PM10, and 71.06 ± 23.97% for PM2.5.

Petri Nets(PNs), as a graphical and mathematical tool, provide a uniform environment for modeling, formal analysis, and design of discrete event systems. The main objective of this paper is to model and analyze the manufacturing system using PNs as well as simulate the manufacturing system. The analysis method used in this paper is Embedded Markov Chain(EMC) method for Generalized Stochastic Petri nets(GSPN). The simulation tools used in this paper are Arena and Simnet V. 1.37. The results of three methods, EMC method, PNs simulation and Arena simulation, are obtained and compared.

Recently many research and investment have been made for waste biomass to bio solid fuel development in Korea. In this study, monte-carlo simulation is applied to estimate the bio solid fuel product quality made from various biomass as raw materials. Commercial biomass and waste biomass raw material samples were collected to investigate the basic properties and to predict mixed effect to solid bio-fuel product. The results show that predicted subject properties about applying a single quality items was reasonable, but for mixed biomass raw material, correlations and logical relevance of several items will be need to assume that require additional consideration for it. The stochastic techniques established through this study will be applicable to bio-solid fuel product development with a variety of waste biomass.

Climate indices generally contain nonstationary oscillations (NSO). Not much study has been done in the literature to reproduce the NSO processes through a stochastic time series model. Therefore, we proposed a model that reproduces the NSO of climate indices employing EMD- NSO resampling (NSOR) technique. The proposed simulation model was tested with three climate indices (i.e. AO, ENSO, and PDO) for the annual and winter (January, February, and March - JFM) datasets. The results of the proposed model are compared with the ones of the Contemporaneous Shifting Mean and Contemporaneous Autoregressive Moving Average (CSM-CARMA) model. One set of the 5000 year records is simulated from each model. The results (ex. Figure 1) indicated that the proposed model is superior to the CSM-CARMA model for reproducing the NSO process while the other basic statistics are comparatively well preserved in both models.

We illustrate in the current study that fitting a univariate time series model to each extracted component might end up with the underestimation of the serial dependence that the observation data might contain. A alternative for parameter estimation is suggested to preserve the serial dependence of the observation variable using the relationship between the observation variable and the decomposed variable. The case study of the Upper Colorado River basin shows that some improvement is made through the suggested alternative.

자동화 컨테이너 터미널에서 안벽크레인, AGV (Automated Guided Vehicle)와 같은 하역장비의 작업은 수많은 요인에 영향을 받으며, 이로 인해 각 장비의 작업시간 예측에 있어 불확실성이 존재한다 이러한 불확실성은 AGV 배차를 어렵게 만들고 작업효율을 떨어뜨리는 주요 원인 중 하나이다. 본 논문에서는 이러한 불확실성에 대처하기 위하여 확률적 시뮬레이션 기반 AGV 배차 알고리즘을 제안한다. 제안 방안은 AGV에 작업을 할당할 때, 할당된 작업의 수행 및 이후 일정 기간 동안의 AGV의 작업에 대해 확률적 시뮬레이션을 여러 번 반복 수행하여 작업할당에 대한 평가치의 확률적 표본을 수집한다. 수집한 표본으로부터 평가의 기대치를 추정하고 이를 이용하여 대안을 평가함으로써 불확실성의 영향을 줄인다. 평가의 신뢰도를 높이기 위해서는 많은 수의 표본을 수집해야 하므로 실시간 제약 하에서 수집 가능한 확률적 표본의 수를 늘리기 위해 이벤트 기반의 고속 시뮬레이션을 디자인하였다. 시뮬레이션 실험을 통해 검증한 결과 불확실성이 있는 환경에서 제안방안의 성능이 정적인 환경을 가정하는 방안보다 뛰어남을 확인하였다.

본 연구의 목적은 간헐 수문사상인 시간강수계열의 구조적 특성을 고찰하여 강수량 모의발생을 위한 추계학적 모형을 개발하는 것이다. 이를 위하여 본 연구에서는 강수발생과정에 대한 추계학적 모형은 이재준과 이정식(2002)이 개발한 추계학적 모형을 이용하였으며, 강수량과정을 위하여 사상내의 시간강수량을 비정상 1차 자기회귀모형으로 기술하였다. 시간강수계열의 강수발생과정과 강수량과정을 조합하면 시간강수사상의 발생패턴과 사상기간내의 강수의 종속구조를 모의할 수