• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• Imaging Science in Dentistry
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• v.44 no.1
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• pp.21-29
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• 2014

Purpose: To determine the conversion coefficients (CCs) from the dose-area product (DAP) value to effective dose in cone-beam CT. Materials and Methods: A CBCT scanner with four fields of view (FOV) was used. Using two exposure settings of the adult standard and low dose exposure, DAP values were measured with a DAP meter in C mode ($200mm{\times}179mm$), P mode ($154mm{\times}154mm$), I mode ($102mm{\times}102mm$), and D mode ($51mm{\times}51mm$). The effective doses were also investigated at each mode using an adult male head and neck phantom and thermoluminescent chips. Linear regressive analysis of the DAP and effective dose values was used to calculate the CCs for each CBCT examination. Results: For the C mode, the P mode at the maxilla, and the P mode at the mandible, the CCs were 0.049 ${\mu}Sv/mGycm^2$, 0.067 ${\mu}Sv/mGycm^2$, and 0.064 ${\mu}Sv/mGycm^2$, respectively. For the I mode, the CCs at the maxilla and mandible were 0.076 ${\mu}Sv/mGycm^2$ and 0.095 ${\mu}Sv/mGycm^2$, respectively. For the D mode at the maxillary incisors, molars, and mandibular molars, the CCs were 0.038 ${\mu}Sv/mGycm^2$, 0.041 ${\mu}Sv/mGycm^2$, and 0.146 ${\mu}Sv/mGycm^2$, respectively. Conclusion: The CCs in one CBCT device with fixed 80 kV ranged from 0.038 ${\mu}Sv/mGycm^2$ to 0.146 ${\mu}Sv/mGycm^2$ according to the imaging modes and irradiated region and were highest for the D mode at the mandibular molar.

• The Journal of Internal Korean Medicine
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• v.23 no.1
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• pp.25-31
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• 2002

Objectives : To prevent stroke, it is very important to reduce risk factors which might cause stroke. However, previous studies that having investigated coffee consumption associated with stroke reported various results. In addition, there were only a few studies based on the Korean population. Therefore, we studied the association of coffee consumption and the possibility of getting stroke among Koreans. Methods : A case-control study was carried out on 207 cases(stroke patients) and 207 controls(non-stroke patients) in a hospital. Information on characteristics, health habits, dietary habits and coffee consumption were obtained through direct interview by using an organized questionnaire; WHR(Waist-Hip Ratio) was determined through physical examination. The coffee consumption was classified by the average frequency of intake, such as less than 1 cup/day, 2-3 cups/day, more than 5 cups/day). Possible confounding effects of age, sex, smoking and alcohol drinking were controlled by multiple logistically regressive analysis. Results : After adjusting age and sex, coffee consumption significantly increased risk factors of stroke(${\leq}$1 cup/day OR=1.018, 95% CI=0.631-1.644; 2-3 cup/day OR=1.782, 95%CI=1.032-3.079;${\geq}$5 cup/day OR=1.210, 95% CI=0.588-2.490). When other factors were controlled, the risk factors of stroke were associated with alcohol drinking, whereas no significant association was observed with coffee consumption. Conclusion : Coffee consumption is not a major risk factor of causing stroke in this study. Prospective and cohort study on the association between coffee consumption and the possibility of getting strokes among the Korean population will be needed in the future.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• Health Policy and Management
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• v.31 no.3
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• pp.364-373
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• 2021

Background: This study aims to examine the regressiveness of national health insurance (NHI) premium burdens for local subscribers. The government has established a restructuring of health insurance contributions in 2017. Therefore, insurance premium reform began in 2018 and the second national health insurance premium reform will be carried out in 2022. We will analyze local subscribers before and after the policy reform of 2018. Methods: This study used data from 'local premium imposition elements' in the health insurance statistics annual reports (2017-2019) on National Health Insurance Service (NHIS). This study was calculated contribution rates according to levels of income and property for local insured by the method of comparing. Simulations of primary and secondary reforms were conducted in the study to determine regressiveness. Results: Insurance premiums for local subscribers were analyzed separately by income and property insurance premiums. In the income premium analysis, the higher the income, the lower the premium rate, and then the fixed rate was maintained from a certain section. The regressiveness of income insurance premiums has been eased in part. On the other hand, the property insurance premium burden was found to be regressive still by income class. Conclusion: Regressiveness analysis showed that a decrease in income contributions was achieved to local insured in the first phase of reform. But in the second phase of reform, more consideration should be given to reductions of property premium portions of local subscribers. Based on the results, the author suggested policy discussions to reorganizing the new systems of NHI contribution of local Insured.

• Korean Journal of Community Nutrition
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• v.24 no.2
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• pp.127-136
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• 2019

Objectives: This study analyzed the education participation reasons and deterrents of dietitians who work in welfare facilities for the elderly. Methods: The survey was completed by 144 dietitians working at welfare facilities for the elderly in Korea. The survey was conducted in October, 2018, both on-line and off-line, based on the demographic characteristics, work status on welfare facilities for the elderly, Participation Reasons Scale (PRS) and Deterrents to Participation Scale (DPS-G). The data were analyzed using frequency analysis, descriptive analysis, factor analysis, reliability analysis, regressive analysis using SPSS ver. 25.0. Results: The reason for participation were divided into three factors: 'Responsibility of professional and self-development ($5.76{\pm}1.04$)', 'Job stability and personal benefits ($4.98{\pm}1.28$)', and 'Interaction and development of professional competencies ($5.85{\pm}1.00$)'. 'Interaction and development of professional competencies' was the highest motivation factor. Also, the deterrents for participation were divided into four factors: 'Dispositional barrier ($2.70{\pm}1.29$)', 'Dissatisfaction of education usability ($3.39{\pm}1.38$)', 'Institutional barrier ($4.21{\pm}1.45$)', and 'Situational barrier ($2.36{\pm}1.30$)'. 'Institutional barrier' showed the highest deterrents factor. In addition, 'Responsibility of professional and self-development' and 'Interaction and development of professional competencies' were negative attributes for 'Dispositional barrier' (p<0.001). Conclusions: These results provide basic data to promote participation in education and contribute to the improvement of their job ability and education capacity of the food and nutrition management of welfare facilities for the elderly.

• Journal of The Korean Society of Integrative Medicine
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• v.6 no.4
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• pp.149-169
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• 2018

Purpose : This study was to develop a strategy for modeling future workforce projections to serve as a basis for analyzing annual supply of and demand for physical therapists across the South Korea into 2030. Methods : In-and-out movement model was used to project the supply of physical therapists. The demand was projected according to the demand-based method which consists of four-stages such as estimation of the utilization rate of the base year, forecasting of health care utilization of the target years, forecasting of the requirements of clinical physical therapists and non-clinical physical therapists based on the projected physical therapists. Results : Based on the current productivity standards, there will be oversupply of 39,007 to 40,875 physical therapists under the demand scenario of average rate in 2030, undersupply of 44,663 to 49,885 under the demand scenario of logistic model, oversupply of 16,378 to 19,100 under the demand scenario of logarithm, and oversupply of 18,185 to 20,839 under the demand scenario of auto-regressive moving average (ARIMA) model in 2030. Conclusion : The result of this projection suggests that the direction and degree of supply of and demand for physical therapists varied depending on physical therapists productivity and utilization growth scenarios. However, the need for introduction of a professional physical therapist system and the need to provide long-term care rehabilitation services are actively being discussed in entering the aging society. If community rehabilitation programs for rehabilitation of disabled people and the elderly are activated, the demand of physical therapists will increase, especially for elderly people. Therefore, healthcare policy should focus on establishing rehabilitation service infrastructure suitable for an aging society, providing high-quality physical therapy services, and effective utilization of physical therapists.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies - Bitcoin, Ethereum, Litecoin, and EOS - and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies - AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet - representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning-based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.