• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.75-98
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• 2021

This paper compares three statistical models that examine the relationship between national and provincespecific fertility rates. The three models are two of the regression models and a cointegration model. The regression model is by substituting Gompit transformation for the cumulative fertility rate by the average for ten years, and this model applies the raw data without transformation of the fertility data. A cointegration model can be considered when fitting the unstable time series of fertility rate in probability process. This paper proposes the following when it is intended to derive the relation of non-stationary fertility rate between the national and provinces. The cointegrated relationship between national and regional fertility rates is first derived. Furthermore, if this relationship is not significant, it is proposed to look at the national and regional fertility rate relationships with a regression model approach using raw data without transformation. Also, the regression model method of substituting Gompit transformation data resulted in an overestimation of fertility rates compared to other methods. Finally, Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon and Gyeonggi province are expected to show a total fertility rate of 1.0 or less from 2025 to 2030, so an urgent and efficient policy to raise this level is needed.

• Journal of the Korean Society of Safety
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• v.35 no.6
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• pp.25-31
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• 2020

Boiling Liquid Expanding Vapor Explosion(BLEVE) can cause not only economic damage to the plant but also serious casualties. LPG accidents account for 89.6 percent of all accidents caused by gas leaks in Korea over the past nine years, while casualties from accidents also account for 73 percent of all accidents, according to statistics from the Korea Gas Safety Corporation. In addition, a potential explosion and a fire accident from one LPG storage tank may affect the nearby storage tanks, causing secondary and tertiary damage (domino effect). The safety distance standards for LPG used by LPG workplaces, charging stations, and homes in Korea have become stricter following the explosion of LPG charging stations in Bucheon. The safety distance regulation is divided into regulations based on the distance damage and the risk including frequency. This study suggests two approaches to optimizing the safety distance based on the just consequence and risk including frequencies. Using the Phast 7.2 Risk Assessment software by DNV GL, the explosion overpressure and heat radiation were derived according to the distance caused by BLEVE in the worst-case scenario, and accident and damage probability were derived by considering the probit function and domino effect. In addition, the safety distance between LPG tanks or LPG charging stations was derived to minimize damage effects by utilizing these measures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.219-224
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• 2020

Binary classification has been broadly investigated in machine learning. In addition, binary classification can be easily extended to multi class problems. To successfully utilize machine learning methods for classification tasks, preprocessing and feature extraction steps are essential. These are important steps to improve their classification performances. In this paper, we propose a new learning method based on weighted least squares. In the weighted least squares, designing weights has a significant role. Due to this necessity, we also propose a new technique to obtain weights that can achieve feature transformation. Based on this weighting technique, we also propose a method to combine the learning and feature extraction processes together to perform both processes simultaneously in one step. The proposed method shows the promising performance on five UCI machine learning data sets.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.43-50
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• 2022

Correlation Power Analysis(CPA) is a sub-channel attack method that measures the detailed power consumption of attack target equipment equipped with cryptographic algorithms and guesses the secret key used in cryptographic algorithms with more than 90% probability. Since CPA performs analysis based on statistics, a large amount of data is necessarily required. Therefore, the CPA must measure power consumption for at least about 15 minutes for each attack. In this paper proposes a method of using a Convolutional Neural Network(CNN) capable of accumulating input data and predicting results to solve the data collection problem of CPA. By collecting and learning the power consumption of the target equipment in advance, entering any power consumption can immediately estimate the secret key, improving the computational speed and 96.7% of the secret key estimation accuracy.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• Journal of the Korean Society of Food Culture
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• v.21 no.1
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• pp.57-64
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• 2006

The purpose of this study was to measure the pizza purchasing behavioral characteristics of respondents and importances of factors affecting pizza purchase, to estimate the effects of attributes on pizza restaurant choice, and to predict probability of selecting a particular pizza restaurant. The questionnaire consisted of two parts: The paired experimental profiles, purchasing behavior and importances of factors affecting pizza purchase. This study generated profiles of 16 hypothetical pizza restaurant based on the seven attributes. The profiles comprised 16 discrete sets of variables, each of which had two levels. For this study, researcher randomly selected 150 students of university as respondents. Twenty students did not complete the survey instrument, resulting in a final sample size of 129. All estimations were carried out using frequencies, $X^2$, independent samples t-test, phreg procedure of SAS package. The results are as follows. Some purchasing behavioral characteristics and importances of factors affecting pizza purchase were significantly different by gender. Based on the estimated models developed for male student group and female student group, the Chi-square statistics were significant at p<0.001. The parameter estimate for late delivery time with male student group was highest, and the parameter estimate for price with female student group was highest. The pizza restaurant that charged \20,000, offered 100% discount on eleventh pizza, promised to deliver pizza in 40 mins, usually delivered the pizza as promised time, offered only 1 type of pizza crust, delivered warm pizza, offered the money-back guarantee was favored by each of male student group and female student group. The results from this study suggested that there was an opportunity to increase market share and profit by improving operations so that customers receive discount and money-back guarantee simultaneously, and by reducing price, delivery time.

• Atmosphere
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• v.33 no.5
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• Nutrition Research and Practice
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• v.17 no.5
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• pp.945-958
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• 2023

BACKGROUND/OBJECTIVES: Food literacy (FL) is a crucial skill for selecting sustainable and healthy food options, necessitating the identification of vulnerable groups in the community using valid measurement tools. Identifying weak domains in FL is essential for enhancing the overall FL. This study examined the FL levels of Seoul citizens based on their sociodemographic characteristics and assessed the relationship between FL, food intake, and weight status. SUBJECTS/METHODS: This study utilized the data from the Seoul Food Survey, a cross-sectional study employing representative samples of Seoul citizens. Data collection occurred from September to October 2021, with 4,039 citizens aged 18 yrs and above participating in face-to-face surveys. Thirty-three FL items were assessed, comprising 14 items in the nutrition and safety (NS) domain, eight items in the cultural and relational (CR) domain, and 11 items in the socio-ecological (SE) domain. In addition, data on food intake sufficiency and obesity status were collected. The descriptive statistics, t-tests, analysis of variance, and logistic regression analysis were used for analysis. RESULTS: Men, students, young adults, older citizens, and people experiencing food insecurity had the lowest scores for all the FL domains. The highest quartile group of NS scores had a higher probability of consuming adequate servings of vegetables and fruits, with significant linear trends observed (P for trend < 0.05). In all three FL domains, the odds ratio for obesity was significantly lower in the groups with high FL scores (P < 0.05). CONCLUSIONS: A close relationship was observed between low FL, obesity, and food intake, even after controlling for other covariates. Vulnerable groups with low FL were also identified. Therefore, it is essential to develop programs to improve FL and the health and well-being of these groups.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.3
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• pp.348-356
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• 2007

Statistical and probabilistic method was used in the analysis of data, which is the most effective one in describing the various natures, and the methodology relating the results with the design was developed. Influents and effluents of three treatment plants were analyzed and the focus was made on BOD, COD, SS, IN, TP The fluctuations of influent such as BOD, COD, SS were extremely large and their standard deviations(st.dev) were more than 10 mg/L. but those of TN, TP were small; the st.dev was 6.6 mg/L for TN, 0.6 mg/L for TP, respectively. But, effluent concentration showed consistent pattern regardless of the influent fluctuations, the st.dev was ranged between 0.28 and 4.48 mg/L. Effluent distributional characteristics were as follows; BOD, COD were distributed normally, but SS, TN, and TP, log-normally; unsymmetric and skewed to the right. The coefficient of reliability(COR) based on the results of statistics of data was introduced to evaluate the process performance an4 to reflect the process performance to the process design. The coefficient of reliability relates the design value(the goal) with the standards and it can be used in operating treatment facilities under a certain reliability level and/or in evaluating the reliability of the treatment facilities on operation. Each treated water quality of effluent showed the half of water quality standards in the level of 50% percentile and all treatment plant was achieved 100% probability of water quality standards. It was concluded that the variability of the process performance should be reflected to the design procedure and the standards through the analysis based on the statistics and the probability.

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.420-433
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• 2016

The purpose of this study is to improve the calibration matrixes of 2-D and 3-D convective rainfall rates (CRR) using the brightness temperature of the infrared $10.8{\mu}m$ channel (IR), the difference of brightness temperatures between infrared $10.8{\mu}m$ and vapor $6.7{\mu}m$ channels (IR-WV), and the normalized reflectance of the visible channel (VIS) from the COMS satellite and rainfall rate from the weather radar for the period of 75 rainy days from April 22, 2011 to October 22, 2011 in Korea. Especially, the rainfall rate data of the weather radar are used to validate the new 2-D and 3-DCRR calibration matrixes suitable for the Korean peninsula for the period of 24 rainy days in 2011. The 2D and 3D calibration matrixes provide the basic and maximum CRR values ($mm\;h^{-1}$) by multiplying the rain probability matrix, which is calculated by using the number of rainy and no-rainy pixels with associated 2-D (IR, IR-WV) and 3-D (IR, IR-WV, VIS) matrixes, by the mean and maximum rainfall rate matrixes, respectively, which is calculated by dividing the accumulated rainfall rate by the number of rainy pixels and by the product of the maximum rain rate for the calibration period by the number of rain occurrences. Finally, new 2-D and 3-D CRR calibration matrixes are obtained experimentally from the regression analysis of both basic and maximum rainfall rate matrixes. As a result, an area of rainfall rate more than 10 mm/h is magnified in the new ones as well as CRR is shown in lower class ranges in matrixes between IR brightness temperature and IR-WV brightness temperature difference than the existing ones. Accuracy and categorical statistics are computed for the data of CRR events occurred during the given period. The mean error (ME), mean absolute error (MAE), and root mean squire error (RMSE) in new 2-D and 3-D CRR calibrations led to smaller than in the existing ones, where false alarm ratio had decreased, probability of detection had increased a bit, and critical success index scores had improved. To take into account the strong rainfall rate in the weather events such as thunderstorms and typhoon, a moisture correction factor is corrected. This factor is defined as the product of the total precipitable waterby the relative humidity (PW RH), a mean value between surface and 500 hPa level, obtained from a numerical model or the COMS retrieval data. In this study, when the IR cloud top brightness temperature is lower than 210 K and the relative humidity is greater than 40%, the moisture correction factor is empirically scaled from 1.0 to 2.0 basing on PW RH values. Consequently, in applying to this factor in new 2D and 2D CRR calibrations, the ME, MAE, and RMSE are smaller than the new ones.