• Knowledge Management Research
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• v.22 no.2
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• pp.247-268
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• 2021

Investors mainly use PER and PBR among financial ratios for valuation and investment decision-making. I conduct an analysis of two basic financial ratios from a statistical perspective. Financial ratios contain key accounting numbers which reflect firm fundamentals and are useful for valuation or risk analysis such as enterprise credit evaluation and default prediction. The distribution of financial data tends to be extremely heavy-tailed, and PER and PBR show exceedingly high level of kurtosis and their extreme cases often contain significant information on financial risk. In this respect, Extreme Value Theory is required to fit its right tail more precisely. I introduce not only GPD but exGPD. GPD is conventionally preferred model in Extreme Value Theory and exGPD is log-transformed distribution of GPD. exGPD has recently proposed as an alternative of GPD(Lee and Kim, 2019). First, I conduct a simulation for comparing performances of the two distributions using the goodness of fit measures and the estimation of 90-99% percentiles. I also conduct an empirical analysis of Information Technology firms in Korea. Finally, exGPD shows better performance especially for PBR, suggesting that exGPD could be an alternative for GPD for the analysis of financial ratios.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.47-57
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• 2019

Purpose: This paper aims to investigate the concentration of fine particulate matter (PM2.5) in the Seoul area by predicting unhealthy days due to PM2.5 and comparing the regional differences. Methods: The extreme value theory is adopted to model and compare the PM2.5 concentration in each region, and each best model is selected through the goodness of fitness test. The maximum likelihood estimation technique is applied to estimate the parameters of each distribution, and the fitness of each model is measured by the mean absolute deviation. The selected model is used to estimate the number of unhealthy days (above $75{\mu}g/m^3$ PM2.5 concentrations) in each region, with which the actual number of unhealthy days are compared. In addition, the level of PM2.5 concentration in each region is analyzed by calculating the return levels for periods of 6 months, 1 year, 3 years, and 5 years. Results: The Mapo (MP) area revealed the most unhealthy days, followed by Gwanak (GW) and Yangcheon (YC). On the contrary, the number of unhealthy days was low in Seodaemun (SDM), Songpa (SP) and Gangbuk (GB) areas. The return level of PM2.5 was high in Gangnam (GN), Dongjak (DJ) and YC. It will be necessary to prepare for PM2.5 than other regions. On the contrary, Gangbuk (GB), Nowon (NW) and Seodaemun (SDM) showed relatively low return levels for PM2.5. However, in most of the regions of Seoul, PM25 is generated at a very poor level ($75{\mu}g/m^3$) every 6months period, and more than $100{\mu}g/m^3$ PM2.5 occur every 3 years period. Most areas in Seoul require more systematic management of PM2.5. Conclusion: In this paper, accurate prediction and analysis of high concentration of PM2.5 were attempted. The results of this research could provide the basis for the Seoul Metropolitan Government to establish policies for reducing PM2.5 and measuring its effects.

• Atmosphere
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• v.18 no.4
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• pp.355-365
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• 2008

Synoptic and climatological characteristics of heat waves over Korea and Europe as well as their biometeorological impacts were compared. In July of 1994, excess deaths of about 2,388 in the population of South Korea are estimated by the modified excess death calculation algorithm ofKysely (2004). The excess deaths correspond to the net mortality increase of 12.5% in July of 1994 if we compare the estimated value to the expected number of deaths in this month (i.e., about 19,171). The comparative study of heat waves in Korea and Europe shows that the record-breaking heat waves in both regions are closely associated with prolonged droughts. In particular, reduction of soil moisture, precipitation and cloud cover and enhancement of insolation during the drought periods are very likely to be related to the increase in the intensity and the duration ofheat waves. Climate models predict that the frequency, intensity, and duration of heat waves in the 21 st century will be greatly enhanced in both areas. In order to reduce the biometeorological and socioeconomic impacts due to heat waves, not only the development of heat-related mortality prediction model that can be widely applied to many climate regimes, but also studies on the climatological association between extreme temperatures and abnormal hydrological cycle are needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.158-158
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• 2016

본 연구에서는 극치 분포의 오른쪽 꼬리 부분 예측 시 안정적인 확률수문량 산정하는 확률분포형과 매개변수 추정 방법을 평가하기 위해 Monte Carlo 모의를 수행하였다. 수문자료의 빈도해석에 적합한 것으로 알려진 generalized extreme value (GEV), Gumbel (GUM), generalized logistic (GLO), gamma3 (GAM3), normal (NOR), log-normal3 (LN3) 총 6개의 확률분포형을 바탕으로 오른쪽 꼬리 부분의 확률수문량 추정 성능을 모의 실험을 통해 평가하고자 한다. 30년 이상 자료를 보유한 기상청 지점의 지속기간별 연최대값 자료를 분석한 결과를 바탕으로 모분포를 GEV분포로 선정하였으며 평균이 1.0, 표준편차 0.5, 왜곡도 계수는 0.5, 1.0, 2.0, 3.0, 4.0이 되도록 가정하였다. 또한 자료 길이에 따른 성능 평가를 위해 표본 크기 20, 50, 100, 150, 200개에 대해 분석을 수행하였다. 위와 같은 가정으로 총 25종류(왜곡도계수 5개 ${\times}$ 표본 크기 5개)의 발생된 모분포에 6가지의 확률분포형과 3가지의 매개변수 추정방법(모멘트법, 최우도법, 확률가중모멘트법)을 조합한 18가지의 모델을 비교 분석해보았다. 평가방법으로는 평균 제곱근 오차(Root Mean Square Error, RMSE), 편의(bias), 평균 상대오차(Mean Relative Difference, MRD), 평균 절대 상대오차(Mean Absolute Relative Difference, MARD)를 사용하여 적용 모델의 성능을 비교 분석하였다.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.547-559
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• 2023

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r²). The XGBoost model had the smallest RMSE and the highest r² value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.97-109
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• 2003

This study is mainly conducted to derive the design drought rainfall by the consecutive duration using probability weighted moments with rainfall in the regional drought frequency analysis. It is anticipated to suggest optimal design drought rainfall of hydraulic structures for the water requirement and drought frequency of occurrence for the safety of water utilization through this study. Preferentially, this study was conducted to derive the optimal regionalization of the precipitation data that can be classified by the climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. Five homogeneous regions in view of topographical and climatological aspects were accomplished by K-means clustering method. Using the L-moment ratio diagram and Kolmogorov-Smirnov test, generalized extreme value distribution was confirmed as the best fitting one among applied distributions. At-site and regional parameters of the generalized extreme value distribution were estimated by the method of L-moments. Design drought rainfalls using L-moments following the consecutive duration were derived by the at-site and regional analysis using the observed and simulated data resulted from Monte Carlo techniques. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE for the design drought rainfall derived by at-site and regional analysis in the observed an simulated data were computed and compared. In has shown that the regional frequency analysis procedure can substantially more reduce the RRMSE. RBIAS and RR in RRMSE than those of at-site analysis in the prediction of design drought rainfall. Consequently, optimal design drought rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Geomechanics and Engineering
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• v.32 no.6
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• Journal of Integrative Natural Science
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• v.8 no.4
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• pp.305-312
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• 2015

The present study showed WHtR to be significantly better than BMI and WC for prediction of metabolic-related diseases in the middle-aged and older people in Korea, based on Bayesian ordered probit model analysis. The variations of WC, BMI and WHtR were compared according to the number of metabolic-related diseases such as hypertension, dyslipidemia, stroke, myocardial infarction, angina pectoris and diabetes. It was found that the three measures showed the similar variation except a very few extreme cases for age less than 40. For subjects over the age of 40, WC was not significant and WHtR gave more influence in greater variability than BMI on the number of metabolic diseases. Also, the rate of change for WHtR was higher than for BMI as the number of metabolic-related diseases increased. Specifically, the difference of the marginal effect of WHtR between no disease and only one disease was 1.81 times higher than that of BMI. Moreover, it was pointed out that the threshold value of WHtR for obesity should be considered differently by age.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.15-26
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• 2023

The purpose of this study is to determine the artificial intelligence-based degradation index from the image of the cross-section of the microstructure taken with a scanning electron microscope of the specimen obtained by the creep test of DA-5161 SX, a nickel-based superalloy used as a material for high-temperature parts. It proposes a new method of quantification and proposes a model that predicts degradation based on Bayesian inference without destroying components of high-temperature parts of operating equipment and a creep life prediction model that predicts Larson-Miller Parameter (LMP). It is proposed that the new degradation indexing method that infers a consistent representative value from a small amount of images based on the geometrical characteristics of the gamma prime phase, a nickel-base superalloy microstructure, and the prediction method of degradation index and LMP with information on the environmental conditions of the material without destroying high-temperature parts.