• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.185-188
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• 2005

This study selected three different specified concrete strength types of mixture which were applied to domestic seawater concrete structure and measured compressive strength and chloride diffusion coefficient and composed the formula of prediction model of chloride diffusion coefficient in order to provide the useful data for concrete mix decision of seawater structures. As a result, the formula of prediction model of chloride diffusion coefficient which set W/C and compressive strength as parameters and performed multiplex regression analysis which was based on the mathematical theory was confirmed more reliable than the formula of prediction which was composed existing water-cement ratio function.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.940-946
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• 2012

This paper proposes a method to predict maximum traction for unmanned robots on rough terrain in order to improve traversability. For a traction prediction, we use a friction-slip model based on modified Brixius model derived empirically in terramechanics which is a function of mobility number $B_n$ and slip ratio S. A friction-slip model includes characteristics of various rough terrains where robots are operated such as soil, sandy soil and grass-covered soil. Using a friction-slip model, we build a prediction model for terrain parameters on which we can know maximum static friction and optimal slip with respect to mobility number $B_n$. In this paper, Mobility number $B_n$ is estimated by modified Willoughby Sinkage model which is a function of sinkage z and slip ratio S. Therefore, if sinkage z and slip ratio are measured once by sensors such as a laser sensor and a velocity sensor, then mobility number $B_n$ is estimated and maximum traction is predicted through a prediction model for terrain parameters. Estimation results for maximum traction are shown on simulation using MATLAB. Prediction Performance for maximum traction of various terrains is evaluated as high accuracy by analyzing estimation errors.

• Information Systems Review
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• v.18 no.2
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• pp.173-198
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• 2016

Financial distress can damage stakeholders and even lead to significant social costs. Thus, financial distress prediction is an important issue in macroeconomics. However, most existing studies on building a financial distress prediction model have only considered idiosyncratic risk factors without considering systematic risk factors. In this study, we propose a prediction model that considers both the idiosyncratic risk based on a financial ratio and the systematic risk based on a business cycle. Ultimately, we build several IT artifacts associated with financial ratio and add them to the idiosyncratic risk factors as well as address the imbalanced data problem by using an oversampling technique and synthetic minority oversampling technique (SMOTE) to ensure good performance. When considering systematic risk, our study ensures that each data set consists of both financially distressed companies and financially sound companies in each business cycle phase. We conducted several experiments that change the initial imbalanced sample ratio between the two company groups into a 1:1 sample ratio using SMOTE and compared the prediction results from the individual data set. We also predicted data sets from the subsequent business cycle phase as a test set through a built prediction model that used business contraction phase data sets, and then we compared previous prediction performance and subsequent prediction performance. Thus, our findings can provide insights into making rational decisions for stakeholders that are experiencing an economic crisis.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2049-2056
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• 2011

The reinforced-roadbed materials composed of crushed stones are used for preventing vertical deformation and reducing impact load caused by highspeed train. Repeated load application can induce deformation in the reinforced-roadbed layer so that it causes irregularity of track. Thus it is important to understand characteristics of permanent deformation in the reinforced-subbase materials. The characteristics of permanent deformation can be simulated by prediction model that can be obtained by performing repetitive triaxial test. The prediction model of permanent deformation is a key-role in construction of design method of track. The prediction model of permanent deformation is represented in usual as the hyperbolic function with increase of number of load repetition. The prediction model is sensitive to many factors including stress level etc. so that it is important to define parameters of the model as clearly as possible. Various data obtained from repetitive triaxial test and resonant column test using the reinforced-roadbed of crushed stone are utilized to develop a new prediction model based on concept of shear-stress ratio and elastic modulus. The new prediction model of permanent deformation can be adapted for developing design method of track in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.246-252
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• 2017

In the defense acquisition, a company's goal is to maximize profits, and the government's goal is to allocate budgets efficiently. Each year, the government estimates the ratio of indirect cost sector to defense companies, and estimates the ratio to be applied when calculating cost of the defense articles next year. The defense industry environment is changing rapidly, due to the increasing trend of defense acquisition budgets, the advancement of weapon systems, the effects of the 4th industrial revolution, and so on. As a result, the cost structure of defense companies is being diversifying. The purpose of this study is to find an alternative that can enhance the rationality of the current methodology for estimating the ratio of indirect cost sector of defense companies. To do this, we conducted data analysis using the R language on the cost data of defense companies over the past six years in the Defense Integrated Cost System. First, cluster analysis was conducted on the cost characteristics of defense companies. Then, we conducted a regression analysis of the relationship between direct and indirect costs for each cluster to see how much it reflects the cost structure of defense companies in direct labor cost-based indirect cost rate estimates. Lastly a new ratio prediction model based on regularized regression analysis was developed, applied to each cluster, and analyzed to compare performance with existing prediction models. According to the results of the study, it is necessary to estimate the indirect cost ratio based on the cost character group of defense companies, and the direct labor cost based indirect cost ratio estimation partially reflects the cost structure of defense companies. In addition, the current indirect cost ratio prediction method has a larger error than the new model.

• Journal of Distribution Science
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• v.17 no.11
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• pp.73-79
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• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.529-536
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• 2014

Physical characteristics of cement-admixed clay such as water content, specific gravity, unit weight and void ratio are main factors for strength, compressibility and prediction of consolidation behavior. In the past, the physical characteristics of admixed soils could be understanded through complex laboratory tests and field survey after construction. In this study, the tests were performed with conditions such as clay water contents 0%-170%, cement contents 5%-25% and curing period 3-90days after that analyzed for changes which are water content, specific gravity unit weight and void ratio of admixed soils. A prediction of properties through mechanical relationships with clay in situ water content, cement content and curing period could be proposed using the test results. The prediction equation of void ratio of admixed soils was derived using void ratio equation in geotechnical engineering and compared with test results of bangkok clay and then this study could be verified.

• Journal of ILASS-Korea
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• v.18 no.1
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• pp.35-43
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• 2013

The empirical correlations for the prediction of breakup length of liquid jet in uniform cross flow are reviewed and classified in this study. The breakup length of liquid jets in cross flow was normally discussed in terms of the distances from the nozzle exit to the column breakup location in the x and y directions, called as column fracture distance and column fracture height, respectively. The empirical correlations for the prediction of column fracture distance can be classified as constant form, momentum flux ratio form, Weber number form and other parameter form, respectively. In addition, the empirical correlations for the prediction of column fracture height can be grouped as momentum flux ratio form, Weber number form and other parameter form, respectively. It can be summarized that the breakup length of liquid jet in a cross flow is a basically function of the liquid to air momentum flux ratio. However, Weber number, liquid-to-air viscosity ratio and density ratio, Reynolds number or Ohnesorge number were incorporated in the empirical correlations depending on the investigators. It is clear that there exist the remarkable discrepancies of predicted values by the existing correlations even though many correlations have the same functional form. The possible reasons for discrepancies can be summarized as the different experimental conditions including jet operating condition and nozzle geometry, measurement and image processing techniques introduced in the experiment, difficulties in defining the breakup location etc. The evaluation of the existing empirical correlations for the prediction of breakup length of liquid jet in a uniform cross flow is required.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.257-267
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• 2020

In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.