• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.179-184
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• 2005

Shear strength parameters obtained from filed survey are important factors in the analysis of slope stability. In this study, sensitivity analysis was performed to evaluate the effect of input parameters on the analysis of slope stability. The input parameters selected for sensitivity analysis were slope angle, cohesion, and friction angle. Monte-Carlo Simulation method was used for calculating input parameters and the factor of safety was computed by means of limit equilibrium method. A rock slope, which has failed in the field, was used for the sensitivity analysis in the analysis of slope stability. The result of analysis shows that the factor of safety of the rock slope was a little low. From partial correlation coefficient(PPC) of input parameters determined from the sensitivity analysis, slope stability was dependant on cohesion and slope angle. The effect of friction angle was lower than that of cohesion and slope angle on slope stability.

• Journal of the Korea Safety Management & Science
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• v.17 no.4
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• pp.87-96
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• 2015

This study analyzed the safety management efficiency through input-oriented BCC Model using DEA by making of Models, in which this study selected the number of staff affiliated with the construction site, number of staff members on the construction team, and number of safety personnel as Input Factor, along with the number of disaster victims and zero-accident accomplished hours as output elements, targeting 40-or-so apartment building construction sites which are being built by 3 domestic construction companies. As a result of efficiency, it was 0.643, the number of efficient DMU was 4. In the aspect of returns to scale, 21 construction sites(52.5%) found to be in a state of increasing returns while 14 construction sites(35%) was in a state of Decreasing returns. The reason for inefficiency was found to lie in technical factors in case of 30 construction sites(75%). Lastly, inefficient DMU is in need of 38.9 % and 66% reduction in site personnel and safety personnel. Based on study results, the 29 construction sites accounting for 72.5% of DMU for safety management efficiency evaluation object turned out to do inefficient safety management that doesn't fit the size of each company.

• Journal of the Korean Society of Safety
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• v.36 no.2
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• pp.111-119
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• 2021

Multi-dimensional analysis of reactor safety-KINS standard (MARS-KS) is a thermal-hydraulic code to simulate multiple design basis accidents in reactors. The code has been essential to assess nuclear safety, but has mainly focused on light water reactors, which are in the majority in South Korea. Few previous studies considered pressurized heavy water reactor (PHWR) applications. To verify the code applicability for PHWRs, it is necessary to develop MARS-KS input decks under various transient conditions. This study proposes an input model to simulate small-break loss of coolant accidents for PHWRs. The input model includes major equipment and experimental conditions for test B9802. Calculation results for selected variables during steady-state closely follow test data within ±4%. We adopted the Henry-Fauske model to simulate break flow, with coefficients having similar trends to integrated break mass and trip time for the power supply. Transient calculation results for major thermal-hydraulic factors showed good agreement with experimental data, but further study is required to analyze heat transfer and void condensation inside steam generator u-tubes.

• Journal of Energy Engineering
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• v.21 no.2
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• pp.158-167
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• 2012

The purpose of this paper is to examine the factors that encouraged the industrial growth of Koran city gas industry during 1995-2009, by carrying out input-output structural decomposition analysis(IO-SDA) using Syrquin's model. The results show that the main factors which contributed to the growth of the Korean city gas industry are final domestic demand(48.4%) and technological change(38.6%). By examining the results for the three periods of 1995-2000, 2000-2005, and 2005-2009, the tendency of changes between the two main factors is drawn. In contrast to the drastic decreasing tendency of the final domestic demand's contribution to the growth, 84.5%, 18.9%, and 15.4%, respectively for each period, there is an increasing tendency for technological change as seen by the results of 7.4%, 70.0%, and 42.2%, respectively. These findings may be a result from the fact that the rate of gas supply in the residential sector has been saturated recently. They are also reflective of the energy consumption trend of industrial activities as there has been a shift in the approach for supplying energy, from the traditional approach which use fossil fuels to the newer approach which uses environmentally friendly energy sources. For the continued growth of the city gas industry, policymakers sould consider greater investment in the expansion of city gas supply infrastructure for industrial activities rather than for the residential sector.

• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.1-7
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• 2011

The methodology of KOSPI forecast has been considered as one of the most difficult problem to develop accurately since short-term KOSPI is correlated with various factors including politics and economics. In this paper, we presents a methodology for forecasting short-term trends of stock price for five days using the feature selection method based on a neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. A technical indicator are selected for preprocessing KOSPI data in the first step. In the second step, thirty-nine numbers of input features are produced by wavelet transforms. Twelve numbers of input features are selected as the minimized numbers of input features from thirty-nine numbers of input features using the non-overlap area distribution measurement method. The proposed method shows that sensitivity, specificity, and accuracy rates are 72.79%, 74.76%, and 73.84%, respectively.

• Journal of the Korean Society of Safety
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• v.21 no.4 s.76
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• pp.102-107
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• 2006

Construction accidents have not decreased in spite of much effort such as new work methods, education and research related to safety works. Specially, many fatal accidents happened in construction works which involve the apartment, building, school, church, hotel, hospital, bank work and the other works. These accidents are mainly caused by unanticipated risk factors. From these reasons, this study researched fatal accidents which happened in construction works during last 13 years($1992{\sim}2004$) and analyzed the input workers and a work period of construction work. According to the input workers and a work period, the results are as following. During 13years($1992{\sim}2004$), the fatal accidents, related to the construction work, were happened to the 1,977 cases. These results were occupied the 21.32% of the total industry accidents. According to the result of the construction, the fatal accident rate of a concrete form work was the highest rate of 16.24% (321 cases) and a temporary work, a steel frame work was the each rate of 12.39%(245 cases), 10.07%(199 cases). Comparing to other work types, the fatal accident rate of those three work types(concrete form work, temporary work, steel work) was represented highly. We surveyed input workers and work period of construction work site. From the result of survey, input workers of a concrete form work were occupied with 13,720. The risk rate of the work type, which was considered input workers and work period, was represented 0.3622(a steel frame work), 0.1142(a temporary work), 0.0782(a tower crane) and 0.0772(a concrete work).

• Journal of the Korean Geotechnical Society
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• v.21 no.5
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• pp.75-82
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• 2005

Shear strength parameters obtained from field tests are important factors in the analysis of slope stability. In this paper, sensitivity analysis was performed to evaluate the effect of input parameters on the analysis of slope stability. The input parameters selected for sensitivity analysis were slope angle, cohesion, and friction angle. Monte-Carlo Simulation method was to estimate input parameters for sensitivity analysis in slope stability, and the limit equilibrium method was used to calculate the factor of safety of slope stability. A rock slope, failed in the field, was used for the sensitivity analysis of input parameters in the analysis of slope stability. The result of analysis shows that the factor of safety of the rock slope was a little low. From partial correlation coefficient (PCC) of input parameters from the sensitivity analysis, slope stability was dependant mainly on cohesion and slope angle. The effect of friction angle was smaller than those of cohesion and slope angle on slope stability.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.5_2
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• pp.194-205
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• 2010

To predict urban growth according to changes in landcover, probability factors werecal culated and mapped. Topographic, geographic and social and political factors were used as prediction variables for constructing probability maps of urban growth. Urban growth-related factors included elevation, slope, aspect, distance from road,road ratio, distance from the main city, land cover, environmental rating and legislative rating. Accounting for these factors, probability maps of urban growth were constr uctedusing frequency ratio (FR) and logistic regression (LR) methods and the effectiveness of the results was verified by the relative operating characteristic (ROC). ROC values of the urban growth probability index (UGPI) maps by the FR and LR models were 0.937 and 0.940, respectively. The LR map had a slightly higher ROC value than the FR map, but the numerical difference was slight, with both models showing similar results. The FR model is the simplest tool for probability analysis of urban growth, providing a faster and easier calculation process than other available tools. Additionally, the results can be easily interpreted. In contrast, for the LR model, only a limited amount of input data can be processed by the statistical program and a separate conversion process for input and output data is necessary. In conclusion, although the FR model is the simplest way to analyze the probability of urban growth, the LR model is more appropriate because it allows for quantitative analysis.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.73-79
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• 2019

Selecting a proper journal to submit a research paper is a difficult task for researchers since there are many journals and various decision factors to consider during the decision process. For this reason, journal recommendation services are exist as a kind of intelligent research assistant which recommend potential journals. The existing services are executing a recommendation based on topic similarity and numerical filtering. However, it is impossible to calculate topic similarity when a researcher does not input paper data, and difficult to input clear numerical values for researchers. Therefore, the journal recommendation method which consider the importance of decision factors is proposed by constructing the preference matrix based on the paper publication history of a researcher. The proposed method was evaluated by using the actual publication history of researchers. The experiment results showed that the proposed method outperformed the compared methods.