• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.1
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• pp.15-29
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• 2010

Spalling is a kind of instability phenomenon of surrounding rock around underground openings subjected to high in-situ stress according to the development of extension fractures. Three kinds of spalling criteria have been presented so far; however, all spalling criteria have the range of values so that the fuzziness and vagueness of spalling criterion cannot be avoided. In this study, a new fuzzy probability model is proposed to predict the probability of spalling in a systematic way by using fuzzy probability theory. Many of the underground opening projects worldwide are evaluated with the proposed method. Prediction results expressed as the spalling probability agree well with the in-situ observations. In particular, a new fuzzy probability model considering all three evaluation indices of spalling by adopting weighting factors based on relative reliability among three evaluation indices is able to resolve erroneous prediction of spalling by choosing only one prediction method. Moreover, the more reasonable value of spalling probability could have been obtained by adopting the modified damage index to the newly proposed fuzzy probability model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of Radiation Protection and Research
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• v.35 no.1
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• pp.6-11
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• 2010

Embodying the safety of radioactive waste disposal requires the relevant safety criteria and the corresponding stylized methods to demonstrate its compliance with the criteria. This paper proposes a conceptual model of risk-based safety evaluation for integrating complex potential radiation exposure situations in radioactive waste disposal. For demonstrating compliance with a risk constraint, the approach deals with important exposure scenarios from the viewpoint of the receptor to estimate the resulting risk. For respective exposure situations, it considers the occurrence probabilities of the relevant exposure scenarios as their probability of giving rise to doses to estimate the total risk to a representative person by aggregating the respective risks. In this model, an exposure scenario is simply constructed with three components:radionuclide release, radionuclide migration and environment contamination, and interaction between the contaminated media and the receptor. A set of exposure scenarios and the representative person are established from reasonable combinations of the components, based on a balance of their occurrence probabilities and the consequences. In addition, the probability of an exposure scenario is estimated on the assumption that the initiating external factors influence release mechanisms and transport pathways, and its effect on the interaction between the environment and the receptor may be covered in terms of the representative person. This integrated approach enables a systematic risk assessment for complex exposure situations of radioactive waste disposal and facilitates the evaluation of compliance with risk constraints.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.135-145
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• 2020

Recently, the disaster rate of the construction industry has increased with high-rise, and complexity of the building, unlike the decrease in the disaster rate in other industries. Although risk assessment is performed to reduce the occurrence of disasters, it is difficult to estimate the risks accurately due to activity in which no disaster has occurred, and inconsistencies in the level of details of work. In this study, in order to evaluate the risk of the major activity for the apartment construction work, the activity was identified by referring to the risk assessment model of construction industry type by the KOSHA. The construction work types and activities were consistently organized in level of work into nine work types and 82 activities were through experts consultation. Analyzing the disaster types that occurred during work through KOSHA disaster cases, calculating the probability of disaster occurrence according to the type of disaster, and combining the probability of disaster with the severity of disaster to estimate the risk assessment method was presented. Using the daily report of the construction site of the apartment, the results of a case study confirmed the validity of the risk calculation method presented in this study.

• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.2
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• pp.138-145
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• 2011

In this paper time series wave data are simulated by the Monte Calo method using random numbers to generate random phases of the wave signal. The simulated wave signasl are used to study the characteristics of freak waves. Various sea states are represented by combinations of the significant wave height $H_s$ defined in the spectrum method and the significant wave steepness $S_s$. For a fixed value of $S_s$, the probability of the occurrence of the freak wave is decreased as $H_s$ increases. For a fixed value of $H_s$ the probability of the occurrence of the freak wave increases as $S_s$ increases. The average value of the maximum wave height increase as $S_s$ increases, but the average height of freak wave remains the same and the value approaches two times of $H_s$. For the fixed value of $S_s$, average kurtosis of wave elevation increases as $H_s$ increases, but for a fixed $H_s$, the average kurtosis decreases as $S_s$ increases. The average of abnormality index(AI) is around 2.11 irregardless of $H_s$ and $S_s$. The maximum value of AI lies between 2.5 - 3.0. Therefore it is conjectured that AI maximum due to linear focusing is 3.0.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.627-636
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• 1999

In 60 years when the double $CO_2$concentration is anticipated the average annual rainfall depth is expected to be increased by 5 10% due to global warming. However, in the water resources area the frequency change of meteorological extremes such as droughts and floods attracts more interests than the increase of annual rainfall amount. Even though recent frequent occurrences of this kind of meteorological extremes are assumed as an indirect proof of global warming, the prediction of its overall tendency has not yet been made. Thus, in this research we propose a possible methodology to be used for its prediction. The methodology proposed is based on the frequency distribution of daily rainfall be Todorovie and Woolhiser(1975), and Katz(1977), where the input parameters are modified to consider the change of monthly or annual rainfall depth and, thus, to result in the change of frequency distribution. We adopt two values(10mm, 50mm) as thresholds and investigate the change of occurrence probability due to the change monthly and annual rainfall depth. these changes do not directly indicate the changes of occurrence probability of floods and droughts, but it may still be a very useful information for their prediction. Finally, the changes of occurrence probability were found to be greater when considering the monthly rainfall rather than the annual rainfall, and those in rainy season than those in dry season.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.16-23
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• 2001

In this paper, we propose the pseudo n-gram language models for speech recognition with middle size vocabulary compared to large vocabulary speech recognition using the statistical n-gram language models. The proposed method is that it is very simple method, which has the standard structure of ARPA and set the word probability arbitrary. The first, the 1-gram sets the word occurrence probability 1 (log likelihood is 0.0). The second, the 2-gram also sets the word occurrence probability 1, which can only connect the word start symbol and WORD, WORD and the word end symbol . Finally, the 3-gram also sets the ward occurrence probability 1, which can only connect the word start symbol , WORD and the word end symbol . To verify the effectiveness of the proposed method, the word recognition experiments are carried out. The preliminary experimental results (off-line) show that the word accuracy has average 97.7% for 452 words uttered by 3 male speakers. The on-line word recognition results show that the word accuracy has average 92.5% for 20 words uttered by 20 male speakers about stock name of 1,500 words. Through experiments, we have verified the effectiveness of the pseudo n-gram language modes for speech recognition.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1007-1015
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• 2013

A stochastic probability model based on the non-homogeneous Poisson process is represented that can correctly analyze the time-dependent linear and nonlinear behaviors of total damage over the occurrence process of loads. Introducing several types of damage intensity functions, the probability of failure and the total damage with respect to mean time to failure has been investigated in detail. Taking particularly the limit state to be the random variables followed with a distribution function, the uncertainty of that would be taken into consideration in this paper. In addition, the stochastic probability model has been straightforwardly applied to the rubble-mound breakwaters with the definition of damage level about the erosion of armor units. The probability of failure and the nonlinear total damage with respect to mean time to failure has been analyzed with the damage intensity functions for armor units estimated by fitting the expected total damage to the experimental datum. Based on the present results from the stochastic probability model, the preventive management for the armor units of the rubble-mound breakwaters would be suggested to make a decision on the repairing time and the minimum amounts repaired quantitatively.

• Journal of Korea Water Resources Association
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• v.42 no.9
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• pp.747-758
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• 2009

In this study, long-term variabilities of rainfall-occurrence characteristics are analyzed using rainfall data at Seoul, which is the longest data record existing in world. first, the accuracy of Chukwooki data set (CWK) are evaluated in view of rainfall-occurrence probability by analyzing the transition probabilities and occurrence characteristics based on Markov chain. And long-term inter-monthly variabilities of transition probabilities are analyzed using two dimensional LOWESS regression. From the results of analyzed transition probabilities and occurrence characteristics, it is different that rainfall-occurrence characteristics between CWK and modern rain gage data set (MRG) for original rainfall data sets (M00). For characteristics of rainfall series, occurrences probabilities of rainfall are increased and durations of each rainfall are shorter than past. And from the results of analyzing the long-term inter-monthly variabilities of transition probabilities, in case of M20, lengths of dry spells between CWK and MRG are not different significantly and lengths of wet spells are decreased persistently after A.D. 1830. Especially, decreasing trend for lengths of wet spells at recent september are appeared significantly. These results are considered with increasing trend of recent rainfall, it is concluded that recent frequencies and intensities of rainfall are increasing.