• Journal of Korean Neurosurgical Society
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• 제53권6호
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• pp.342-348
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• 2013

Objective : Several scales are currently used to assess occlusion rates of coiled cerebral aneurysms. This study compared these scales as predictors of recanalization. Methods : Clinical data of 827 patients harboring 901 aneurysms treated by coiling were retrospectively reviewed. Occlusion rates were assessed using angiographic grading scale (AGS), two-dimensional percent occlusion (2DPO), and volumetric packing density (vPD). Every scale had 3 categories. Followed patients were dichotomized into either presence or absence of recanalization. Kaplan-Meier analysis was conducted, and Cox proportional hazards analysis was performed to identify surviving probabilities of recanalization. Lastly, the predictive accuracies of three different scales were measured via Harrell's C index. Results : The cumulative risk of recanalization was 7% at 12-month, 10% at 24-month, and 13% at 36-month of postembolization, and significantly higher for the second and third categories of every scale (p<0.001). Multivariate-adjusted hazard ratios (HRs) of the second and third categories as compared with the first category of AGS (HR : 3.95 and 4.15, p=0.004 and 0.001) and 2DPO (HR : 4.87 and 3.12, p<0.001 and 0.01) were similar. For vPD, there was no association between occlusion rates and recanalization. The validated and optimism-adjusted C-indices were 0.50 [confidence (CI) : -1.09-2.09], 0.47 (CI : -1.10-2.09) and 0.44 (CI : -1.10-2.08) for AGS, 2DPO, and vPD, respectively. Conclusion : Total occlusion should be reasonably tried in coiling to maximize the benefit of the treatment. AGS may be the best to predict recanalization, whereas vPD should not be used alone.

• 디지털산업정보학회논문지
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• 제18권3호
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• pp.75-82
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• 2022

The purpose of the Consumer sentiment index survey is to determine the consumer's economic situation and consumption spending plan, and it is used as basic data for diagnosing economic phenomena and forecasting the future economic direction. The purpose of this paper is to analyze and predict the future Consumer sentiment index using the ARMA model based on the past consumer index. Consumer sentiment index is determined according to consumer trends, so it can reflect consumer realities. The consumer sentiment index is greatly influenced by economic indicators such as the base interest rate and consumer price index, as well as various external economic factors. If the consumer sentiment index, which fluctuates greatly due to consumer economic conditions, can be predicted, it will be useful information for households, businesses, and policy authorities. This study predicted the Consumer sentiment index for the next 3 years (36 months in total) by using time series analysis using the ARMA model. As a result of the analysis, it shows a characteristic of repeating an increase or a decrease every month according to the consumer trend. This study provides empirical results of prediction of Consumer sentiment index through statistical techniques, and has a contribution to raising the need for policy authorities to prepare flexible operating policies in line with economic trends.

• 한국지리정보학회지
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• 제25권4호
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• pp.1-18
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• 2022

현재 우리나라 기상청에서는 6개월 누적강수량 기준인 SPI6(standardized precipitation index 6)을 이용하여 기상가뭄을 지역별로 평가하고 있다. 하지만, SPI는 69개 기상관측소의 강수량만을 고려하여 산정되는 지수로 복합적인 이유로 나타나는 가뭄사상은 정확하게 판단하지 못하고 있는 실정이다. 따라서, 본 연구의 목적은 강수량만을 고려한 SPI와 강수량, 식생지수 및 기온을 복합적으로 고려하는 SDCI(Scaled Drought Condition Index)를 경기도 지역을 대상으로 산정 및 비교하고자 하였다. 또한, SPI와 SDCI의 비교를 통해 산정된 결과를 활용하여 지점자료기반 가뭄지수와 위성영상기반 가뭄지수의 장단점을 파악하고자 하였다. SDCI를 산정하기 위해 MODIS(MODerate resolution Imaging Spectroradiometer) 위성영상자료, 종관기상관측(ASOS) 자료 및 크리깅 기법을 사용하였다. 강수량의 지속기간은 2014년의 8개 시점에 대해 1개월, 3개월, 6개월을 각각 적용하여 SDCI1, SDCI3, SDCI6을 산정하였다. SDCI 산정 결과, SPI와 달리 약 두달 전부터 가뭄양상을 나타내기 시작하여 경기도 시군별 가뭄에 대해서 잘 드러냈다. 이를 통해, 위성영상자료와 지점자료의 결합이 가뭄지수 변화 양상에 있어서 효율성을 높였으며, 기존의 건조 지역과 더불어 습윤 지역에 대해 가뭄예측 가능성을 증대시켰음을 파악할 수 있었다.

• 대기
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• 제26권1호
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• pp.1-18
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• 2016

Eleven Tropical Cyclone (TC) intensity guidance models in the western North Pacific have been validated over 2008~2014 based on various analysis methods according to the lead time of forecast, year, month, intensity, rapid intensity change, track, and geographical area with an additional focus on TCs that influenced the Korean peninsula. From the evaluation using mean absolute error and correlation coefficients for maximum wind speed forecasts up to 72 h, we found that the Hurricane Weather Research and Forecasting model (HWRF) outperforms all others overall although the Global Forecast System (GFS), the Typhoon Ensemble Prediction System of Japan Meteorological Agency (TEPS), and the Korean version of Weather and Weather Research and Forecasting model (KWRF) also shows a good performance in some lead times of forecast. In particular, HWRF shows the highest performance in predicting the intensity of strong TCs above Category 3, which may be attributed to its highest spatial resolution (~3 km). The Navy Operational Global Prediction Model (NOGAPS) and GFS were the most improved model during 2008~2014. For initial intensity error, two Japanese models, Japan Meteorological Agency Global Spectral Model (JGSM) and TEPS, had the smallest error. In track forecast, the European Centre for Medium-Range Weather Forecasts (ECMWF) and recent GFS model outperformed others. The present results has significant implications for providing basic information for operational forecasters as well as developing ensemble or consensus prediction systems.

• 대기
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• 제28권4호
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• 천문학논총
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• 제32권3호
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• pp.407-420
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• 2017

We study the operation of a lunisolar calendar in Korea and its time data calculation method. The dates based on the lunisolar calendar have been conventionally used in Korea after the Gregorian calendar was introduced in 1896. With the Astronomy Act enacted in 2010, the lunisolar calendar is presently being used as an official calendar along with the Gregorian calendar. However, no institutionalized regulations have been provided on the time data calculation method by the lunisolar calendar. The Korea Astronomy and Space Science Institute very recently established the regulations on the lunisolar calendar operation in Korea. We introduce the regulations together with historical substances and analyze the time data calculated according to the regulations for 600 years from 1901 to 2500. From our study, we find that the value of ${\Delta}T$ (i.e., the difference between the terrestrial time and the universal time) is the most critical parameter causing uncertainty on the data. We also find that all new Moon days in the almanacs agree with our calculations since 1912. Meanwhile, we find that new Moon and winter solstice times are found to be very close to midnight in 38 and five cases, respectively. For instance, the new Moon time on January 14, 2097 is 0 h 0 min 8 s. In this case, deciding the first day (i.e., new moon day) in a lunar month is difficult because of the large uncertainty in the value of ${\Delta}T$. Regarding with a lunar leap month, we find that the rules of inserting the leap month do not apply for 17 years. In conclusion, we believe that our findings are helpful in determining calendar days by using the lunisolar calendar.

• Korean Circulation Journal
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• 제53권10호
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• pp.677-689
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• 2023

Background and Objectives: There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. Methods: We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. Results: Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. Conclusions: Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

• Journal of Astronomy and Space Sciences
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• 제14권2호
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• pp.381-385
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• 1997

해석적 방법을 이용한 정지위성의 장기 궤도예측 알고리즘을 개발하였다. 적용된 섭동모델에는 5 $\times$5 지구중력포텐셜, 달과 태양의 중력, 태양복사압에 의한 섭동들이 포함되었으며, 모든 섭동들은 장반경, 이심률 백터, 궤도경사각 백터, 평균경도의 구성요소로 이루어진 춘분점 궤도요소의 영년변화, 단주기 변화, 장주기변화 섭동항들로 급수전개되었다. 해석적 방법에 의한 무궁화 위성의 궤도예측의 결과를 코웰방법을 이용한 궤도예측의 결과와 비교하였다. 이 비교를 통해서 새로 개발된 해석적 방법을 이용한 궤도예측 알고리즘은 3개월동안 약$pm35m$ 이내로 장반경을 정밀하게 예측할 수 있다는 것을 알 수 있다.

• 기본간호학회지
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• 제14권3호
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• pp.382-390
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• 2007

Purpose: This study was done to identify the risk factors influencing drinking permissiveness in university students. Method: The participants in this descriptive survey on causal relations were 219 students enrolled in university who were selected by convenience sampling. The data collected from April to July, 2005 were used in multiple regression analysis to build a prediction model. Results: Differences in drinking permissiveness according to general characteristics were as follows: gender, drinking frequency, drinking in more than one place each time and frequency of excessive drinking. The relationship between drinking permissiveness and amount of alcohol consumption (drinking frequency/month, amount/each time) showed positive correlations. The relationship between drinking permissiveness and motives to drink (social, enhancement, confirmity, coping motives) also showed positive correlations. The causal factors of drinking permissiveness were social motives, capacity/each time and drinking frequency/month. Conclusion: The findings suggest that board intervention programs should be provided to prevent problems of excessive drinking. It is also recommended that a program be developed that can help control the variables identified in this study along with follow up study to verify the model.

• 설비공학논문집
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• 제20권3호
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• pp.205-212
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• 2008

This paper describes the application of an inverse building model to a calibrated forward building model using EnergyPlus program. Typically, inverse models are trained using measured data. However, in this study, an inverse building model was trained using data generated by an EnergyPlus model for an actual office building. The EnergyPlus model was calibrated using field data for the building. A training data set for a month of July was generated from the EnergyPlus model to train the inverse model. Cooling load prediction of the trained inverse model was tested using another data set from the EnergyPlus model for a month of August. Predicted cooling loads showed good agreement with cooling loads from the EnergyPlus model with root-mean square errors of 4.11%. In addition, different control strategies with dynamic cooling setpoint variation were simulated using the inverse model. Peak cooling loads and daily cooling loads were compared for the dynamic simulation.