• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.77-96
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• 2018

A reverse mortgage provides a pension until the death for the insured or last survivor. Long-term risk management is important to estimate the contractual period of a reverse mortgage. It is also necessary to study prediction methods of mortality rates that appropriately reflect the improvement trend of the mortality rate since the extension of the life expectancy, which is the main cause of aging, can have a serious impact on the pension financial soundness. In this study, the Lee-Carter (LC) model reflects the improvement in mortality rates; in addition, multiple life model are also applied to a reverse mortgage. The mortality prediction method by the traditional LC model has shown a dramatic improvement in the mortality rate; therefore, this study suggests mortality projection based on the projection of the skewness for the mortality that has been applied to appropriately reflect the improvement trend of the mortality rate. This paper calculates monthly payments using future mortality rates based on the projection of the skewness of the mortality. As a result, the mortality rates based on this method less reflect the mortality improvement effect than the mortality rates based on a traditional LC model and a larger pension amount is calculated. In conclusion, this method is useful to forecast future mortality trend results in a significant reduction of longevity risk. It can also be used as a risk management method to pay appropriate monthly payments and prevent insufficient payment due to overpayment by the issuing institution and the guarantee institution of the reverse mortgage.

• Journal of Korean Neurosurgical Society
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• v.30 no.8
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• pp.970-975
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• 2001

Objective : By conducing a review of clinical outcomes for patients with aneurysm treated using current microneurosurgical techniques and intensive care unit management, we speculated that grading systems based only on clinical condition or CT finding after admission failed to provide a significant stratification of outcome between individual grades of patients, because these systems did not include the factor for postoperative vasospasm. We hypothesized that postoperative blood flow velocity could have a significant impact on outcome prediction for patients surgically treated for intracranial aneurysms. Methods : We conducted a analysis on patient- and lesion-specific factors that might have been associated with outcome in a series of 55 aneurysm operations performed with measurements of blood-flow velocity with transcranial Doppler ultrasonography(TCD). In the new scale(NS) score system, 1 point is assigned additionally for the case with Hunt and Hess(H-H)/World Federation of Neurological Surgeons(WFNS) Grade IV or V, Fisher Scale(FS) score 3 or 4, aneurysm size greater than 10mm, patient age older than 60 years, blood-flow velocity higher than 120cm/sec, and posterior circulation lesion. By adding the total points, a 6-point scale score(score 0-6) is obtained. Results : Age of patient, size of aneurysm, clinical condition(H-H grade and WFNS), FS score, and blood flow velocity(TCD 1day after operation) were independently and strongly associated with long-term outcome. When NS scores were applied to 55 patients with at least 6 months follow-up, the correlation of individual scores with outcome was strongly validated the retrospective findings. Conclusion : It was speculated that TCD could be used to assess postoperative vasospasm and to monitor noninvasively the patients with aneurysmal SAH. This NS score system is easy to apply, divide patients into groups with different outcome, and is comprehensive, allowing for more accurate prediction of surgical outcome.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.40-49
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• 2013

Current service system of the Korea Meteorological Administration (KMA) for blooming date forecasting in spring depends on regression equations derived from long term observations in both temperature and phenology at a given station. This regression based system does not allow a timely correction or update of forecasts that are highly sensitive to fluctuating weather conditions. Furthermore, the system cannot afford plant responses to climate extremes which were not observed before. Most of all, this method may not be applicable to locations other than that which the regression equations were derived from. This note suggests a way to replace the location restricted regression equations with a thermal time based phenology model to complement the KMA blooming forecast system. Necessary parameters such as reference temperature, chilling requirement and heating requirement were derived from phenology data for forsythia, azaleas and Japanese cherry at 29 KMA stations for the 1951-1980 period to optimize spring phenology prediction model for each species. Best fit models for each species were used to predict blooming dates and the results were compared with the observed dates to produce a correction grid across the whole nation. The models were driven by the KMA's daily temperature data at a 5km grid spacing and subsequently adjusted by the correction grid to produce the blooming date maps. Validation with the 1971-2012 period data showed the RMSE of 2-3 days for Japanese cherry, showing a feasibility of operational service; whereas higher RMSE values were observed with forsythia and azaleas.

• Journal of the Korea Concrete Institute
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• v.24 no.5
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• pp.553-558
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• 2012

Airborne chlorides are transported to inland by sea wind to be attached to seashore concrete structure surface then penetrated into concrete structure members. Since the surface attached chloride amount are dependent on the amount of airborne chlorides, the prediction of distribution of airborne chlorides is important information in preventing chloride corrosion problems in seashore concrete structures. The prediction of surface chloride amount from airborne chlorides environment is extremely difficult than concrete directly in contact with seawater. In addition, their penetrating tendency is different from that of concrete immersed in seawater. In this study, properties of surface and penetrated chlorides under airborne chlorides environment are investigated. Concrete specimens were manufactured and exposed to marine environment for 3 years. The specimens were analyzed at the time durations of 1, 2, and 3 years to check surface chloride amount to penetrated chloride depth. The results revealed that there were certain differences according to surface roughness of concrete and with and without washing effect due to rainfalls. The evaluation results showed that penetrated chlorides depend on amount of airborne chlorides and duration of exposure. In addition, a notable tendency of having deeper chloride penetration and higher chloride content in concrete members under long-term exposure was observed.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Journal of Korean Society of Forest Science
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• v.88 no.3
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• pp.285-291
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• 1999

Loblolly(Pinus taeda L.) and slash(Pinus elliottii Engelm.) pines are the most important timber producing species in the Southern United States. Site index equations to estimate site index curves(base age 25 years) for loblolly pine and slash pine plantations have been developed based on long-term repeated measurement data sets. To check magnitude of errors in estimating site index, each cumulative measurement cycle data sets and all combined data sets were used to recalculate site index values. The Chapman-Richards' growth function was selected for stand height prediction. Anamorphic base age invariant site index curves were presented based on this height prediction equation. Statistics used in the evaluation were mean of the differences and mean of the absolute differences. For plantation ages less than 5 years, site index values showed very sensitive fur both species based on the evaluation test.

• Journal of Wetlands Research
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• v.17 no.4
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• pp.348-358
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• 2015

Impacts of climate change are being observed in the globe as well as the Korean peninsula. In the past 100 years, the average temperature of the earth rose about 0.75 degree in celsius, while that of Korean peninsula rose about 1.5 degree in celsius. The fifth Assessment Report of IPCC(Intergovermental Panel on Climate Change) predicts that the water pollution will be aggravated by change of hydrologic extremes such as floods and droughts and increase of water temperature (KMA and MOLIT, 2009). In this study, future runoff was calculated by applying climate change scenario to analyze the future water quality for each targe period (Obs : 2001 ~ 2010, Target I : 2011 ~ 2040, Target II : 2041 ~ 2070, Target III : 2071 ~ 2100) in Hongcheon river basin, Korea. In addition, The future water quality was analyzed by using multiple linear regression analysis and artificial neural networks after flow-duration curve analysis. As the results of future water quality prediction in Hongcheon river basin, we have known that BOD, COD and SS will be increased at the end of 21 century. Therefore, we need consider long-term water and water quality management planning and monitoring for the improvement of water quality in the future. For the prediction of more reliable future water quality, we may need consider various social factors with climate components.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.439-447
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• 2020

Re-coating of the mirror is one of the important things to maintain the performance of a telescope. The metal coated on the mirror reflects light, and if the reflectance decreases, then the telescope's performance decreases, so the mirror must be periodically recoated. It is important to predict re-coating cycles for military telescopes and to develop maintenance plans not only for performance, but also for the telescope's availability for missions and the maintenance costs for long-term use. However, most similar telescopes used for astronomy research determine recoating cycles based on experience and operating conditions, and not for prediction of recoating. Therefore, this study predicts the cleaning cycles and re-coating cycles of a military telescope's mirror by using simulation. First, this study analyzed similar cases of domestic and foreign astronomy research institutes and the study also reviewed the need for re-coating and predicting re-coating cycles. Second, this study developed simulation for predicting cleaning and re-coating cycles according to data analysis and modeling. Finally, the study predicts cleaning cycles and re-coating cycles according to varying reflectance reduction (5%, 10%, 15%, 20%) and cleaning conditions (per 3 months, 6 months, 1 year and 2 years). As a result, this study suggests reference criteria to develop the planning for military telescopes and their maintenance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.277-283
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• 2019

Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.435-447
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• 2019

In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.