• Journal of Environmental Impact Assessment
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• v.31 no.5
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• pp.321-333
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• 2022

In this study, the concentration and distribution characteristics of BTEX (benzene toluene, ethylbenzene, and xylene) emitted from Daesan Petrochemical Industrial Complex were examined to determine their potential hazards to local residents. Residents living nearby the complex areas may be exposed to the chemicals through various media (air, water, and soil), especially by air. This study evaluated human health risks by inhalation using both deterministic and probabilistic risk assessment approaches. As a result of the deterministic risk assessment, the non-cancer risk was much lower than the regulation limit of hazard index (HI 1.0) for all the points. However, in case of cancer risk evaluation, it was found that the risk of excess cancer for benzene at point A located in the industrial complex was 2.28×10^-6, which slightly exceeded the standard regulatory limit of 1.0×10^-6. In addition, the probabilistic risk assessment revealed that the percentile exceeding the standard of 1.0×10^-6was found to be 45.3%. The sensitivity analysis showed that exposure time (ET) had the greatest impact on the results. Based on the risk assessment study, it implied that ethylbenzene, toluene, and xylene had little adverse effects on potential human exposure, but benzene often exceeded the cancer risk standard (1.0×10^-6). Further studies on extensive VOCs monitoring are needed to evaluate the potential risks of industrial complex areas.

• Tunnel and Underground Space
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• v.12 no.1
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• pp.19-30
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• 2002

Rock joint orientation is one of important geometric attributes that have an influence on the stability of rock structures such as rock slopes and tunnels. Especially, statistical models of the geometric attributes of rock joints can provide a probabilistic approach of rock engineering problems. The result from probabilistic modeling relies on the choice of statistical model. Therefore, it is critical to define a representative statistical model for joint orientation data as well as joint size and intensity and build up a series of modeling procedure including analytical validation. In this paper, we have examined a theoretical methodology for the statistical estimate and hypothesis analysis based upon Fisher distribution and bivariate normal distribution. In addition, we have proposed the algorithms of random number generator which is applied to the simulation of rock joint networks and risk analysis.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.186-194
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• 2019

Deterministic optimization, commonly used to find the geophysical inverse solutions, have its limitation that it cannot find the proper solution since it might converge into the local minimum. One of the solutions to this problem is to use global optimization based on a stochastic approach, among which a large number of particle swarm optimization (PSO) applications have been introduced. In this paper, I developed a geophysical inversion algorithm applying PSO method for the layered-earth resistivity inversion of the small-loop electromagnetic (EM) survey data and carried out numerical inversion experiments on synthetic datasets. From the results, it is confirmed that the PSO inversion algorithm could increase the inversion success rate even when attempting the inversion of small-loop EM survey data from which it might be difficult to find a best solution by applying the Gauss-Newton inversion algorithm.

• Journal of Food Hygiene and Safety
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• v.18 no.3
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• pp.107-112
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• 2003

Probabilistic exposure assessment has been recognized as an important tool in microbial risk assessment, because of obtained the desired results to characterize of variability and uncertainty associated with the microbial hazards. In addition, it will be provided much more actuality information than the point-estimate approaches. In this study, we present methodology using mathematical probability distribution in exposure assessment and estimating of contamination level of Listeria monocytogenes in meat and meat products as a case study. The result of estimation contaminatin level was mean ($50^{th}$ percentile) -4.08 Log CFU/g minimum ($5^{th}$ percentile) -4.88 Log CFU/g, maximum ($95^{th}$ percentile) -3.56 Log CFU/g.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.252-265
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• 2022

Subsurface velocity is critical for the accurate resolution geological structures. The estimation of acoustic impedance is also critical, as it provides key information regarding the reservoir properties. Therefore, researchers have developed various inversion approaches for the estimation of reservoir properties. The Markov chain Monte Carlo method, which is a stochastic method, has advantages over the deterministic method, as the stochastic method enables us to attenuate the local minima problem and quantify the uncertainty of inversion results. Therefore, the Markov chain Monte Carlo inversion method has been applied to various kinds of geophysical inversion problems. However, studies on the Markov chain Monte Carlo inversion are still very few compared with deterministic approaches. In this study, we reviewed various types of reversible jump Markov chain Monte Carlo applications and explained the key concept of each application. Furthermore, we discussed future applications of the stochastic method.

• Spatial Information Research
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• v.20 no.1
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• pp.81-90
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• 2012

When analyzing geographical phenomena, two properties need to be considered. One is the spatial dependence structure and the other is a variation or an uncertainty inhibited in a geographic space. Two problems are encountered due to the properties. Firstly, spatial dependence structure, which is conceptualized as spatial autocorrelation, generates heterogeneous geographic landscape in a spatial process. Secondly, generic statistics, although suitable for dealing with stochastic uncertainty, tacitly ignores location information im plicit in spatial data. GIS is a versatile tool for manipulating locational information, while spatial statistics are suitable for investigating spatial uncertainty. Therefore, integrating spatial statistics to GIS is considered as a plausible strategy for appropriately understanding geographic phenomena of interest. Geographic hot-spot analysis is a key tool for identifying abnormal locations in many domains (e.g., criminology, epidemiology, etc.) and is one of the most prominent applications by utilizing the integration strategy. The article aims at reviewing spatial statistical perspective for analyzing spatial processes in the framework of GIS by carrying out empirical analysis. Illustrated is the analysis procedure of using spatial scan statistic for detecting clusters in the framework of GIS. The empirical analysis targets for identifying spatial clusters of breast cancer incidents in Erie and Niagara counties, New York.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.290-297
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• 2010

At present, the design live load of railway is divided into common railway and high speed railway separately in Korea. L22 which is based on American railway standards is used for common railway and HL25 which is based on Eurocode is used for high speed railway. Although, the design load is the starting point for design of railway, any research for developing design load does not exist at all. However, Europe and Japan develops the design load model consistently for advanced design. Recently, deterministic, probabilistic and cost performance approaches are investigated for developing new design load in Europe which is called LM2000. In the present paper, as a step for developing new design live load model for Korean railway, deterministic processes will be introduced. The safety margins are analyzed based on serviced real trains versus proposed new design load model and a necessity for new design live load will be presented quantitatively.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.6
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• pp.129-139
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• 2008

Due to uncertainty of numerous variables in durability model, a probalistic approach is increasing. Monte Carlo simulation (Level III method) is an easily accessible method, but requires a lot of repeated operations. This paper evaluated the effectiveness of First Order Second Moment method (Level II method), which is more convenient and time saving method than MCS, to predict the corrosion initiation in harbor concrete structure. Mean Value First Order Second Moment method (MV FOSM) and Advanced First Order Second Moment method (AFOSM) are applied to the error function solution of Fick's second law modeling chloride diffusion. Reliability index and failure probability based on MV FOSM and AFOSM are compared with the results by MCS. The comparison showed that AFOSM and MCS predict the similar reliability index and MV FOSM underestimates the probability of corrosion initiation by chloride attack. Also, the sensitivity of variables in durability model to corrosion initiation probability was evaluated on the basis of AFOSM. The results showed that AFOSM is a simple and efficient method to estimate the probability of corrosion initiation in harbor structures.

• Computational Structural Engineering
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• v.10 no.4
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• pp.143-154
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• 1997

Astochastic finite element model which reflects both the effect of discontinuities and the uncertainty of material properties in underground rock mass has been developed. Latin Hypercube Sampling technique has been mobilized and compared with the Monte Carlo simulation method. To consider the effect of discontinuities, the joint finite element model, which is known to be suitable to explain faults, cleavage, things of that nature, has been used in this study. To reflect the uncertainty of material properties, multi-random variables are assumed as the joint normal stiffness and the joint shear stiffness, which could be simulated in terms of normal distribution. The developed computer program in this study has been verified by practical example and has been applied to analyze the circular cavern with discontinuous rock mass.

• Journal of Creative Information Culture
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• v.5 no.2
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• pp.145-156
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• 2019

In this study, we have developed and evaluated an information extracting module based on the named entity recognition technique. For the given purpose in this paper, the module was designed to apply to the problem dealing with extraction of postal address information from arbitrary documents without any prior knowledge on the document layout. From the perspective of information technique practice, our approach can be said as a probabilistic n-gram (bi- or tri-gram) method which is a generalized technique compared with a uni-gram based keyword matching. It is the main difference between our approach and the conventional methods adopted in natural language processing that applying sentence detection, tokenization, and POS tagging recursively rather than applying the models sequentially. The test results with approximately two thousands documents are presented at this paper.