• Journal of the Korean Institute of Gas
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• v.14 no.1
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• pp.28-36
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• 2010

We have garnered 3,593 data of gas accidents reported for 12 years from 1995, and then analyzed the LPG vaporizer accidents according to their types and causes based on the classified database. According to the results the gas rupture has been the most common accident followed by the release, explosion and then fire accidents, the most frequent accident-occurring sub-cause is LPG check floater faults. In addition, we have applied the Poisson Probability Functions to predict the most-likely probabilities of fire, explosion, release and rupture with the LPG vaporizer in the upcoming 5 years. In compliance with Poisson Probability Functions results, in the item which occurs below 3 "LPG-Vaporizer-Fire", in the item which occurs below 5 "LPG-Vaporizer-Products Faults-Check Floater" and the item which occurs below 10 appeared with "LPG-Vaporizer-Products Faults". From this research we have assured the successive database updating will highly improve the anticipating probability accuracy and thus it will play a key role as a significant safety- securing guideline against the gas disasters.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.2
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• pp.163-168
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• 2015

In order to perform estimations with high reliability, it is necessary to deal with the tail part of the cumulative distribution function (CDF) in greater detail compared to an overall CDF. The use of a generalized Pareto distribution (GPD) to model the tail part of a CDF is receiving more research attention with the goal of performing estimations with high reliability. Current studies on GPDs focus on ways to determine the appropriate number of sample points and their parameters. However, even if a proper estimation is made, it can be inaccurate as a result of an incorrect threshold value. Therefore, in this paper, a GPD based on the Akaike information criterion (AIC) is proposed to improve the accuracy of the tail model. The proposed method determines an accurate threshold value using the AIC with the overall samples before estimating the GPD over the threshold. To validate the accuracy of the method, its reliability is compared with that obtained using a general GPD model with an empirical CDF.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.1
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• pp.9-16
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• 2017

As a preliminary study of enter or leaving traffic patterns of the Korea main port, port Management Information System (Port-MIS) data was used to check the volume of vessels entering and leaving the port of Busan, and three consecutive days from each seasons were selected for study. Selected 12-day General Information Center on Maritime Safety & Security (GICOMS) data was also used to analyze the traffic pattern in the main traffic lane of Busan port for dangerous goods carrier. Also, the distance between dangerous goods carriers and Oryukdo breakwater of east breakwater in the main traffic lane was analyzed. Collision probability was estimated using the cumulative probability distribution function of the normal distribution for the maritime traffic safety audit scheme based on the assumption that a ship's trajectory has a normal distribution for a section of the route. However, in case of entry or leaving thorough the Oryukdo breakwater and entry thorough the east breakwater, ship's sailing trajectories were revealed not to follow a normal distribution via regularity testing using a KS-test and SW-test. Especially in the north port, the tendency of the right side of the ship to pass was remarkable. It is desirable to develop a traffic model suitable for the characteristics of the port rather than to apply general traffic theories, and to apply this model to a maritime traffic safety diagnosis, so further research is needed.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.257-266
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• 2007

The research on red tide is generally in progress through field work, such as the naked eye and sampling. It was difficult to forecast exactly the course, from appearance of red tide to disappearance. with the established ways of investigation and analysis. Accordingly it is need to analyze environmental factors in time and space, the appearance of red tide and the path of its migration by more objective and scientific methods. In this study, GIS is applied to analyse the space character of red tide and the interpolation of IDW(Inverse Distance Weight) is applied to assume the density distribution of red tide after gather data by using Arc/Info. After IDW interpolation, the sea area occurred over 1,000 cells/ml of red tide density is extracted with CON and SUM Function of Grid Module, and the density of the sea area is accumulated daily. As a result of this study, the distribution condition of red tide is found timely and spacially by applying GIS to the sea area of red tide, the results indicated that the spatial density and the cumulative frequency about the origin of red tide using GIS, the sea area demonstrated that the maximum density and the maximum frequency varied significantly over the Nammyun of Namhae-Is. with the maximum frequency being 49 times. accordingly if data about the areas of red tide will occur from the present are accumulated, the shifting route of red tide occurrence and extinction can be predicted.

• Fire Science and Engineering
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• v.26 no.4
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• pp.31-41
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• 2012

This study collected 5,100 cases of gas accident occurred in Korea for 14 years from 1995 to 2008, established Database and based on it, analyzed them by detailed forms and reasons. As the result of analyzing the whole city gas accidents with Poisson analysis, the item of "Careless work-Explosion-Pipeline' showed the highest rate of accidents for the next 5 years. And, "Joint Losening and corrosion-Release-Pipeline" showed the lowest rate of accident. In addition, for the result of analyzing only accidents related to LPG vaporizer, "LPG-Vaporizer-Fire" showed the highest rate of accident and "LPG-Vaporizer-Products Faults" showed the lowest rate of accident. Also, as the result of comparing and analyzing foreign LPG accident accompanied by Jet fire, facility's defect which is liquid outflow cut-off device and heat exchanger's defect were analyzed as the main reason causing jet fire, like the case of Korea, but the number of accidents for the next 5 years was the highest in "LPG-Mechanical-Jet fire" and "LPG-Mechanical-Vapor Cloud" showed the highest rate of accidents. By grafting Poisson distribution theory onto gas accident expecting program of the future, it's expected to suggest consistent standard and be used as the scale which can be used in actual field.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.3-12
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• 2016

Bluetooth Low Energy (BLE) protocol has attracted attention as a promising technology for low data throughput and low energy wireless sensor networks. Fast device discovery is very important in a BLE based wireless network. It is necessary to configure the network to work with minimized energy consumption because the BLE network nodes are expected to operate a long time typically on a coin cell battery. However, since it is difficult to obtain low energy and low latency at the same time, the BLE standard introduces wide range setting of parameters related to device discovery process and let the network operators to set up parameter values for the application. Therefore, it is necessary to analyze the performance of device discovery according to the related parameter values prior to BLE network operation. In this paper we analyze the expected value and the cumulative distribution function of the number of advertisements for device discovery in the BLE network. In addition, we propose a scheme for controlling the interval between advertising events that can improve the performance of device discovery without increasing energy consumption.