This essay discusses the enhancement of the KMV model to achieve greater accuracy in predicting default risk and mitigating the effects of asymmetric information in the financial market. Due to the existence of the problem of asymmetric information persists, with some market participants possessing more information than others. This imbalance disrupts the normal market operation, complicates financial regulation, and reduces market stability. Rating agencies have made efforts to disclose and predict default risks to provide more information to the market. Still, traditional models’ prediction accuracy has struggled to meet the market’s evolving demands. To address these challenges, this essay analyzes an improved model, the SIZE-PSO-KMV model. This model builds on the KMV model but introduces a differentiation between large and small firms. By doing so, it refines default risk predictions, thereby alleviating information asymmetry. Enhanced accuracy empowers financial regulators to make more informed decisions and helps prevent future financial crises. The SIZE-PSO-KMV model’s validity is established through rigorous testing, including a comparison with other KMV models and out-of-sample tests. The results demonstrate that this model significantly outperforms traditional KMV models in predicting default risk. Additionally, it adapts to the size of firms, acknowledging that large and small firms face distinct default risk profiles.

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

In recent years, there have been concerted efforts toward predicting ship maneuvering in shallow water since the majority of ship’s accidents near harbors commonly occur in shallow and restricted waters. Enhancement of ship maneuverability at the design stage is crucial in ensuring that a ship navigates safely. However, though challenging, establishing the mathematical model of ship maneuvering motion is recognized as crucial toward accurately predicting the assessment of maneuverability. This paper focused on a study on sensitivity analysis of the hydrodynamic coefficients on the maneuverability prediction of KVLCC2 in shallow waters. Hydrodynamic coefficients at different water depths were estimated from the experimental results conducted in the square tank at Changwon National University (CWNU). The simulation of standard maneuvering of KVLLC2 in shallow waters was compared with the results of the Free Running Model Test (FRMT) in shallow waters from other institutes. Additionally the sensitivity analysis of all hydrodynamic coefficients was conducted by deviating each hydrodynamic derivative from the experimental results. The standard maneuvering parameters including turning tests and zig-zag maneuvers were conducted at different water depths and their effects on the standard maneuvering parameters were assessed to understand the importance of different derivatives in ship maneuvering in shallow waters.

진동수 기반 내하력 평가모델을 실교량에 접목시켜 실시간으로 그 결과를 추정하기 위해 모니터링 및 평가 시스템 프레임 웍을 제시하고자한다. 실시간 내하력 평가 시스템은 무선 IOT가속도계를 부착하여 유효 상시진동데이터를 원격으로 획득하고 이를 통해 진동수 및 점성비를 분석하여 모델에서 요구하는 성능계수를 결정하고 이 결과들을 종합하여 이전 내하력 결과 대비 현재의 내하력을 추정하는 방법이다.

본 연구는 물리적 수리·수문모형의 적용이 제한적인 감조하천에서의 수위예측을 목적으로 하고 있으며, 이를 위해 한강 잠수교를 대상으로 딥러닝 오픈소스 소프트웨어 라이브러리인 TensorFlow를 활용하여 LSTM 모형을 구성하고 2011년부터 2017년까지의 10분 단위의 잠수교 수위, 팔당 댐 방류량과 한강하구 강화대교지점의 예측조위 자료를 이용하여 모형학습(2011~2016) 및 수위예측(2017)을 수행하였다. 모형 매개변수는 민감도 분석을 통해 은닉층의 개수는 6개, 학습속도는 0.01, 학습횟수는 3000번로 결정하였으며, 모형 학습 시 학습정보의 시간적 양을 결정하는 중요한 매개변수인 시퀀스길이는 1시간, 3시간, 6시간으로 변화시키며 모의하였다. 최종적으로 선행시간에 따른 모의 예측능력을 평가하기 위해 LSTM 모형의 예측 선행시간을 6개(1 ~ 24시간)로 구분하여 실측수위와 예측수위와의 비교·분석을 수행한 결과, LSTM 모형의 최적의 성능을 내 는 결과는 시퀀스길이를 1시간으로 하였을 때로 분석되었으며, 특히 선행시간 1시간에 대한 예측정확도는 RMSE는 0.065 m, NSE는 0.99로 실 측수위에 매우 근접한 예측 결과를 나타내었다. 또한 시퀀스길이에 상관없이 선행시간이 길어질수록 모형의 예측 정확도는 2017년 전기간에 걸쳐 평균적으로 RMSE 0.08 m에서 0.28 m로 오차가 증가하였으며, NSE는 0.99에서 0.74로 감소하였다.

한국형 e-Navigation의 내항성 안전 모듈은 운항 중인 선박을 실시간으로 모니터링하고 내항성의 이상 상태를 사전에 경고함으로써 선박의 안정성을 확보하는 선내 원격 모니터링 서비스 중 하나이다. 일반적으로 선박설계를 위한 내항성능은 주어진 조건에서 선체 운동 시뮬레이션을 수행하여 평가하여 왔다. 하지만 운항 중 선박의 내항성능을 실시간으로 평가하기 위해 이러한 시뮬레이션을 실제 운항조건에 맞추어 수행하는 것은 계산시간의 한계로 인해 현실적이지 않다. 본 연구에서는 기계학습 기반의 근사모델을 활용하여 선박의 내항성능 평가 요소들 중 하나인 횡동요 운동특성을 합리적으로 보다 빠르게 예측하는 방법을 소개하고자 한다. 다양한 학습 기법과 데이터의 샘플링 조건을 적용하여, 얻어진 근사모델의 결과와 운동해석 결과의 오차가 거의 1% 내로 일치함을 보였다. 따라서 이러한 방법을 활용하면 선박의 실시간 내항 성능을 평가하는데 효율적으로 사용할 수 있을 것으로 판단된다.

Concrete has recently been modified to have various performance and properties. However, the conventional method for predicting the compressive strength of concrete has been suggested by considering only a few influential factors. so, In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were used for learning the Deep Learning Algorithm to derive a prediction model. The purpose of this study is to suggest a method of constructing a prediction model that predicts the compression strength with high accuracy based on Deep Learning Algorithms.

The stability of the river in the restored river is an important issue in maintenance and management. Bed elevation change simulation can be an effective way to predict the direction of river restoration by predicting long and short term bed elevation change of river. A 2D numerical model (CCHE2D) was implemented to simulate the long-term bed elevation change. The study area is located in the Cheongmi-Cheon Notap-ri and 1.2 km long. The flow scenario was constructed using the flow data that was measured at the water level observatory located upstream at the Janghowon Bridge. The bed elevation change pattern according to restoration of abandoned channel was analyzed and the stability of river was evaluated.

The interannual variability of summer temperature during June-August (JJA) in South Korea was associated with geopotential height averaged in the East Sea (Korea-Japan Index, KJI) and in the subtropical western North Pacific (Western North Pacific Subtropical High Index, WNPSHI). The KJI was coupled with a decaying El Niño one month in advance, while the WNPSHI was influenced by Sea Surface Temperature (SST) anomaly in the western North Pacific and a developing El Niño one to three months ahead. Additionally, the JJA temperature over South Korea was affected by SST anomaly in the western North Pacific in May. Based on these teleconnections, a multivariate regression model using the SST surrogates for the KJI and WNPSHI and an univariate model using an area-averaged May SST were developed to reconstruct the JJA temperature over South Korea. Both of the empirical models reproduced the JJA and monthly temperatures reasonably well. However, when the simulated SSTs from global climate models were used, the multivariate model outperformed the univariate model. Further, for JJA temperature prediction, the multivariate model with 6-month lead SST outstripped one-month lead prediction of global climate models. Therefore, the empirical-dynamical approach can pave a promising way for summer temperature prediction in South Korea.

시계열 데이터를 활용하는 모형은 신뢰할 수 있는 자료를 확보한 경우에는 모형 구축이 용이하고 예측 선행 시간 확보를 위해 신속한 모의가 가능한 장점 때문에 규모가 작은 하천의 홍수예측 모형으로 고려할 수 있다. 이 중 Transfer Function Noise (TFN) 모형은 이탈리아, 영국 등 해외에서는 1970년대부터 시간단위 자료를 이용한 하천유량 예측에 적용되었으나, 우리나라에서는 주로 일 단위 혹은 월 단위의 하천유량 모의에 적용되었다. 국내 수문 자료의 품질 향상으로 그동안 축적된 수문자료를 통해 시간단위 자료를 이용한 홍수예측 모형의 구축 기반이 갖추어졌다. 본 연구의 목적은 소규모 하천을 대상으로 외생변수의 반영이 가능하고 동적시스템과 오차항을 결합하여 예측 오차를 줄이는데 용이한 TFN 모형을 구축하고 그 적용성을 검토하는 것이다. 이를 위해 1시간 단위 자료를 이용하여 TFN 모형을 구축하였으며 구축된 모형을 이용한 홍수 예측 결과를 홍수예보 실무에 활용 중인 저류함수모형의 홍수 예측 결과와 비교하였다. 비교 결과 홍수사상에 따라 TFN 모형과 저류함수 모형이 각각 더 나은 결과를 보이는 사상이 있었으며, 실무에서 TFN 모형을 홍수예측 모형으로 활용할 수 있을 것으로 판단하였다.

In this study, a weighted ensemble method of numerical weather prediction by ensemble models is applied for PyeongChang area. The post-processing method takes into account combination and calibration of forecasts from different numerical models, assigning greater weight to ensemble models that exhibit the better performance. Three different numerical models, including European Center Medium-Range Weather Forecast, Ensemble Prediction System for Global, and Limited Area Ensemble Prediction System, were used to perform the post-processing method. We compared the model outputs from the weighed combination of ensembles with those from the Ensemble Model Output Statistics (EMOS) model for each raw ensemble model. The results showed that the weighted ensemble method can significantly improve the post-processing performance, compared to the raw ensemble method of the numerical models.

Purpose – This current study will investigate the average financial ratio of top and failed five-star hotels in the Jeju area. A total of 14 financial ratio variables are utilized. This study aims to; first, assess financial ratio of the first-class hotels in Jeju to establishing variables, second, develop distress prediction model for the first-class hotels in Jeju district by using logit analysis and third, evaluate distress prediction capacity for the first-class hotels in Jeju district by using logit analysis. Research design, data, and methodology – The sample was collected from year 2015 and 14 financial ratios of 12 first-class hotels in Jeju district. The results from the samples were analyzed by t-test, and the independent variables were chosen. This was an empirical study where the distress prediction model was evaluated by logit analysis. This current research has focused on critically analyzing and differentiating between the top and failed hotels in the Jeju area by utilizing the 14 financial ratio variables. Results – The verification result of the accuracy estimated by logit analysis has shown to indicate that the distress prediction model’s distress prediction capacity was 83.3%. In order to extract the factors that differentiated the top hotels in the Jeju area from the failed hotels among the 14 chosen, the analysis of t-black was utilized by independent variables. Logit analysis was also used in this study. As a result, it was observed that 5 variables were statistically significant and are included in the logit analysis for discernment of top and failed hotels in the Jeju area. Conclusions – The distress prediction press’ prediction capability was compared in this research analysis. The distress prediction press prediction capability was shown to range from 75-85% by logit analysis from a previous study. In this current research, the study’s prediction capacity was shown to be 83.33%. It was considered a high number and was found to belong to the range of the previous study’s prediction capacity range. From a practical perspective, the capacity of the assessment of the distress prediction model in the top and failed hotels in the Jeju area was considered to be a prominent factor in applications of future hotel appraisal.

우리나라에서는 도시 개발사업을 위한 환경영향평가를 실시하는데 있어 개발 전․중․ 후의 강우유출량을 분석하도록 규정하고 있다. 도시개발에 따른 수문학적 변화를 분석하고 대책을 수립하기 위해 수문모델이 사용되고 있으나 대부분의 경우 현장의 자료가 충분하지 않은 관계로 그 산정결 과의 신뢰도가 문제될 수 있다. 본 연구에서는 대전의 관평천 일부유역에서 2015년 7월 부터 2016년 7월 까지 자동 모니터링 장치을 이용하고 또 한 및 현장 측정을 통해 확보된 강우량 및 유출유량의 연속자료를 활용하여 SWMM을 이용하는 경우 강우 유출량 예측의 정확도를 제고하고자 하 였다. 토양침투량 산정을 위해 대표적으로 사용되는 Curve Number 방법, Horton 방법 및 Green-Ampt 방법들을 사용한 경우에 대해서 투수지 역과 불투수 지역에 대해 각각 최적의 Manning 조도계수와 지표면 저류깊이를 산정하여 제시하였다. 본 연구의 결과는 우리나라의 도시 유역에 서 실측자료를 이용하여 강우 유출 모델을 보정하였다는 면에서 의미가 있다고 판단되며 추후 유역의 개발등의 상황에 대해는 강우 시 유출량 및 수질현상을 더욱 정확하게 예측하고 나아가서 향후의 유역 내 수문조건 변화 요인에 대한 영향을 분석하는 데 정확도를 향상시킬 수 있을 것으로 기대된다.

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

홍수범람모의에 주로 활용되는 LISFLOOD-FP 모형은 하도에서 1차원 운동파 방정식을 이용하고, 상대적으로 평평하여 흐름이 확산되는 홍수터 에서 단순화된 2차원 확산파 방정식을 이용하여 흐름을 해석한다. 본 연구에서는 분포형 수문모형인 LISFLOOD-FP 모형의 하천홍수위 예측 적용 성을 검토하기 위하여 배수영향을 받는 만경강 하류구간에서 기 발생한 홍수사상을 대상으로 모형을 보정하고 검증하였다. 모형의 주요 매개변수 인 Manning 조도계수와 하류단 경계조건의 민감도를 분석하였고, 초기조건 영향을 검토하기 위하여 warm-up 유무에 따른 해석결과를 비교하 였다. 그 결과, 운동파 모형임에도 불구하고 배수영향을 받는 만경강 하류구간의 홍수위를 비교적 잘 재현하는 것을 확인하였고,􀀁민감도 분석은 실 제 홍수사상의 적용 시 여러 가지 매개변수와 경계 조건에 의해 홍수위 값이 상이한 결과를 나타났다. 이러한 결과를 바탕으로 운동파 수문모형의 적용시 홍수위 해석에 대한 충분한 검증 및 검토가 필요하다고 사료되며, 검증된 모형을 바탕으로 다양한 유역의 홍수범람모의에 적용이 된다면 향 후 홍수피해 저감을 위한 정책적인 의사결정에 기여할 수 있을 것으로 판단된다.

Sensitivity analysis of the WRF model according to the impact of nudging (e.g., nudging techniques and application domains) was conducted during high nocturnal ozone episode to improve the prediction of the regional ozone concentration in the southeastern coastal area of the Korean peninsula. The analysis was performed by six simulation experiments: (1) without nudging (e.g., CNTL case), (2) with observation nudging (ONE case) to all domains (domain 1∼4), (3) with grid nudging (GNE case) to all domains, (4)∼(6) with grid nudging to domain 1, domain 1∼2 and domain 1∼3, respectively (GNE-1, GNE-2, GNE-3 case). The results for nudging techniques showed that the GNE case was in very good agreement with those observed during all analysis periods (e.g., daytime, nighttime, and total), as compared to the ONE case. In particular, the large effect of grid nudging on the near-surface meteorological factors (temperature, relative humidity, and wind fields) was predicted at the coastline and nearby sea during daytime. The results for application domains showed that the effects of nudging were distinguished between the meteorological factors and between the time periods. When applied grid nudging until subdomain, the improvement effects of temperature and relative humidity had differential tendencies. Temperature was increased for all time, but relative humidity was increased in daytime and was decreased in nighttime. Thus, GNE case showed better result than other cases.

This study was evaluated compressive strength of age 28 days of binary blended concrete according to there type of superplasticizer and there type of w/c. In addition, we are proposed modification prediction model equation that can reflect efficiency of water reducing and influence of binders using Lyse equation to predict the compression strength through the conventional W/C.

과거에는 생애주기에 기반 유지관리 계획에 대한 인식이 부족하였기 때문에 검측자료의 축적은 이루어졌으나 이러한 검측 자료를 이용한 구성품의 수명예측 및 보수보강 시나리오 선정 등 유지관리 의사결정 지원을 위해 사용되지는 못하였다. 이에 본 연구에서는 자료 분석을 위한 궤도 검측 데이터 필터링 및 정제기법을 개발하고, 검측데이터 분석 기법 적용을 통한 궤도의 성능 평가 지표 결정, 다변수 구간특성 및 환경인자를 고려한 레일 마모 및 궤도 틀림에 대한 민감도 분석, 파형과 파장을 고려한 검측데이터 분석 등을 수행하였으며, 이러한 연구 결과를 기반으로 하여 검측된 레일 마모데이터를 이용한 불확실성 기반 궤도성능 예측모델 개봘과 관련한 연구를 수행하였다.

Concrete with blast furnace slag (BFS) shows varied strength development properties different from normal concrete. Therefore, a precise prediction of compressive strength using a full maturity model is desired. The purpose of this study is to predict the compressive strength of concrete with BFS by calculating the apparent activation energy (Ea) and rate constant (kT) for each BFS replacement ratio. The method of Carino Model is used in this study for predicting compressive strength of concrete with BFS.

Concrete with blast furnace slag (BFS) shows varied strength development properties different from normal concrete. Therefore, a precise prediction of compressive strength using a full maturity model is desired. The purpose of this study is to predict the compressive strength of concrete with BFS by calculating the apparent activation energy (Ea) and rate constant (kT) for each BFS replacement ratio. The method of Carino Model is used in this study for predicting compressive strength of concrete with BFS.