• 보건행정학회지
• /
• 제32권4호
• /
• pp.389-399
• /
• 2022

Background: This study aimed to analyze changes in medical utilization and cost before and after long-term care (LTC) implementation. Methods: We used the National Health Information Database from National Health Insurance Service. The participants were selected who had a new LTC grade (grade 1-5) for 2015. Medical utilization was analyzed before and after LTC implementation. Segmented regression analysis of interrupted time series was conducted to evaluate the overall effect of the LTC implementation on medical costs. Results: The total number of participants was 41,726. A major reason for hospitalization in grade 1 was cerebrovascular diseases, and dementia was the top priority in grade 5. The proportion of hospitalization in grade 1 increased sharply before LTC implementation and then decreased. In grade 5, it increased before LTC implementation, but there was no significant difference after LTC implementation. As for medical cost, in grades 1 to 4, the total cost increased sharply before the LTC implementation, but thereafter, changes in level and trend tended to decrease statistically, and for grade 5, immediately after LTC implementation, the level change was decreasing, but thereafter, the trend change was increasing. Conclusion: Long-term care grades showed different medical utilization and cost changes. Long-term care beneficiaries would improve their quality of life by adequately resolving their medical needs by their grades.

• 한국환경과학회지
• /
• 제9권1호
• /
• pp.19-25
• /
• 2000

As the tideland reclamation is done on a large scale these days, construction work is active in the coastal areas. Facilities in the coastal areas must be built with the tide characteristics taken into consideration. Thus the tide characteristics affect the overall reclamation plan. The analysis of the tide data boils down to a harmonic analysis of the hourly changes of long-term tide data and extraction of unharmonic coefficients from the results. Since considerable amount of tide data of the West Coast are available, the existing data can be collected and can be used to obtain the temporal changes of the tide by being fitted into the tide prediction model. The goal of this thesis lies in assessing whether the mean sea level used in the field agrees with the analysis results from the long-term observation data obtained with their homogeneity guaranteed. To achieve this goal, the research was conducted as follows. First the present conditions of the observation stations, the land level standard, and the sea level standard were analyzed to set up a time series model formula for representing them. To secure the homogeneity of the time series, each component was separated. Lastly the mean sea level used in the field was assessed based on the results obtained form the analysis of the time series.

• The Journal of Asian Finance, Economics and Business
• /
• 제7권8호
• /
• pp.213-224
• /
• 2020

The study assesses the impact of trade liberalization factors on changes in customs revenues in Vietnam. Research data was conducted between 2002 and 2017 on the official website of the Government's Web Portal and The World Bank. This paper uses the vector error correction model to estimate the short-term and long term relationship between data series. The results have proven that tariff reductions have a positive effect on short-term and long-term customs revenues in Vietnam. However, the implementation of other international commitments on trade liberalization has positive short-term and long-term negative impacts on customs revenues in Vietnam. The study's results also show that exchange rate has no effect on changes in customs revenues in the short term but it has a strong impact on increasing customs revenues in the long run. Based on these findings, the article also suggests a number of policies to ensure customs revenues in Vietnam in future. In order to ensure customs revenues, the government of Vietnam should: (1) having some policy to improve the efficiency of customs management in Vietnam; (2) Building appropriate VND exchange rate policy; (3) Establishing reasonable non - tariff barriers to prevent fraud and ovations cause losses in customs revenues.

• Ocean and Polar Research
• /
• 제28권4호
• /
• pp.415-423
• /
• 2006

Interannual variation and long-term trends of coastal sea surface temperature (SST) in Korea were investigated by analyzing 27 coastal SST time series from 1969 to 2004. Long-term linear increasing trend was remarkable with the rate over $0.02^{\circ}C/year$ at almost all the stations. The slope of long-term linear trend was larger at the stations along the eastern coast than in the western and southern regions. It was also noticeable that there was a common tendency of interannual variability with the period of 3-5 years at most of the stations. SST was lower in the 1970's and early 1980's while it was higher in the 1990's and early 2000's after the increase in the late 1980's. The pattern of the interannual variability of SST was similar to that of air temperature. Increasing trend of minimum SST in winter was obvious at most stations na it was larger along the eastern coast, while the linear trend of maximum SST in summer was less definite. Therefore, the decreasing tendency of annual amplitude was mainly due to the increasing tendency of SST in winter.

• Journal of Information Processing Systems
• /
• 제14권6호
• /
• pp.1508-1520
• /
• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Communications for Statistical Applications and Methods
• /
• 제26권5호
• /
• pp.497-506
• /
• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• 천문학회보
• /
• 제41권1호
• /
• pp.45.3-46
• /
• 2016

Solar activity shows a self-similarity as it has many periods of activity cycle in the time series of long-term observation, such as 13.5, 51, 150, 300 days, and 11, 88 years and so on. Since fractal dimension is a quantitative parameter for this kind of an irregular time series, we applied this method to long-term observations including sunspot number, total solar irradiance, and 3.75 GHz solar radio flux to predict the start and maximum times as well as expected maximum sunspot number for the next solar cycle. As a result, we found that the radio flux data tend to have lower fractal dimensions than the sunspot number data, which means that the radio emission from the sun is more regular than the solar activity expressed by sunspot number. Based on the relation between radio flux of 3.75 GHz and sunspot number, we could calculate the expected maximum sunspot number of solar cycle 24 as 156, while the observed value is 146. For the maximum time, estimated mean values from 7 different observations are January 2013 and this is quite different to observed value of February 2014. We speculate this is from extraordinary extended properties of solar cycle 24. As the cycle length of solar cycle 24, 10.1 to 12.8 years are expected, and the mean value is 11.0. This implies that the next solar cycle will be started at December 2019.

• 한국인터넷방송통신학회논문지
• /
• 제13권1호
• /
• pp.101-107
• /
• 2013

현실세계에서는 광범위한 업무영역에서 대용량의 시계열자료들이 실시간으로 발생되고 있다. 하지만 동적인 특징으로 표현되는 시계열자료들의 이해와 설명을 위한 최적의 모형을 결정하는 일은 쉽지가 않다. 이러한 시계열자료들의 특징을 잘 설명할 수 있는 모형을 추정하기 위하여 본 연구에서는 시계열데이터의 모형추정에 적합한 은닉마아코프모델을 통해 시계열자료의 장, 단기 예측모형을 추정하였고 이를 통해 미래의 운동패턴예측을 확인하였다. 실제 주식시장의 여러 자료들을 통해 최적의 모형추정을 위한 정보기준과 가장 효율적인 자료길이를 통해 모형의 상태수를 정확하게 추정하는지를 확인하였다. 실험결과 유효한 상태의 수 추정과 단기의 예측이 장기예측보다 유사운동패턴 예측률이 더욱 유사함을 확인하였다.

• 한국관개배수논문집
• /
• 제5권2호
• /
• pp.9-19
• /
• 1998

A series of model tank and rigid wall permeameter tests was performed in order to determine the long-term hydraulic conductivity of the sand-bentonite liners reinforced by geotextile. Main conclusions are as follows 1. The maximum dry density and optimum

• 한국정보통신학회논문지
• /
• 제11권11호
• /
• pp.2039-2046
• /
• 2007

최근에 RR(Return Routability) 프로토콜을 개선하기 위해 공개키 기반의 OMIPv6 시리즈가 제안되었다. OMIPv6 시리즈는 강력한 장기키(long-term key)를 생성하는 초기 단계와 장기키를 바탕으로 이후의 바인딩 갱신 과정을 최적화 하는 후속 단계로 구성된다. 본 논문에서는 OMIPv6 시리즈의 후속 단계를 성능과 보안성, 적용성 측면에서 비교 분석한 후, 비교 분석 결과에 근거하여 개선안을 제시한다. 또한, 제안한 개선안이 성능과 보안성, 적용성을 전체적으로 고려할 때 다른 프로토콜에 비해 우수함을 보인다.