• Journal of Korean Society of Disaster and Security
• /
• 제10권1호
• /
• pp.19-27
• /
• 2017

In recent years, ground subsidence has been frequently occurred by underground cavities due to the excessive groundwater inflow, caused by poor construction and management, during tunnel excavation and underground structure construction. In this study, a numerical model (SEEFLOW3D) was developed to estimate groundwater fluctuations for saturated-unsaturated poros media, evaluates the impact on ground excavation with open cut and non-open cut scenarios. In addition, the visual MODFLOW was applied to demonstrate the verification of the model compared with both results. Our results indicated that the RMSE and NRMSE was obtained to range over -3.95~5.7% and 0.56~4.62%, respectively. The developed model was expected to estimate groundwater discharges and apply analysis tool for optimum design of waterproof wall in future.

• Information Systems Review
• /
• 제24권1호
• /
• pp.39-57
• /
• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.

• 한국신재생에너지학회:학술대회논문집
• /
• 한국신재생에너지학회 2006년도 추계학술대회
• /
• pp.202-205
• /
• 2006

최근 지열원 열펌프 시스템 설치가 해마다 평균 10-30%젓도 꾸주히 증가하고 있다 주요 연구동향은 토양열전도 측정, 지열히트펌프 시스템 전주기 성능평가 하이브리드 시스템의 초기비용 저감과 이러한 지열원 열펌프 시스템 설계방법분야 개발에 대해 초점이 맞춰지고 있다. 특히 국내에서 현재 시공되어진 많은 시스템들이 부실시공의 문제에 노출되고 있으며 이러한 시점에서 현재의 저가 입찰제도 보다는 외국 사례와 같은 성능확인 제도로의 전환 및 많은 연구가 필요하다. 성능확인제도는 사전 성능 예측과 사후 성능 확인 검증으로 구성되며 본 기술현안 보고서는 최근 국내외 연구동향 및 사전 성능 예측과 사후 성능 검증 관하여 정리하여 본다.

• Korean Journal of Construction Engineering and Management
• /
• 제11권1호
• /
• pp.101-112
• /
• 2010

The Korean real estate market currently is experiencing a slowdown due to the global economic crisis which has resulted from subprime mortgage lending practices. In response, the Korean government has enforced various policies, based on intend to deregulate real estate speculation, such as increasing the Loan to value ratio (LTV) in order to stimulate housing supply, demand and accompanying housing transactions. However, these policies have appeared to result in deep confusion in the Korean housing market. Furthermore, analyses for housing market forecasting particularly those which examine the impact of the international financial crisis on the Korean real estate market have been partial and fragmentary. Therefore, a comprehensive and systematical approach is required to analyze the real estate financial market and the causal nexus between market determining factors. Thus, with an integrated perspective and applying a system dynamics methodology, this paper proposes Korean Real Estate and Mortgage Market dynamics models based on the fundamental principles of housing markets, which are determined by supply and demand. As well, the potential effects of the Korean government's deregulation policies are considered by focusing on the main factor of these policies: the mortgage loan.

• The Journal of the Convergence on Culture Technology
• /
• 제9권1호
• /
• pp.89-94
• /
• 2023

Recently, credit risk in the Chinese corporate bond market has increased significantly, and there is a possibility that banks that have invested in corporate bonds may become insolvent. The purpose of this study is to empirically analyze the effect of Chinese commercial banks' investment in securities on financial performance. The analysis results are as follows. First, it is estimated that as the share of securities investment by Chinese commercial banks increases, the bank's profitability decreases. It was found that investment in securities did not have a positive impact on profitability due to the increase in credit risk in the corporate bond market and the increase in marginal companies. Second, it is estimated that as the proportion of securities investment by Chinese commercial banks increases, the bank's soundness deteriorates. As credit risk in China's capital market is increasing, continuous management of non-performing assets is required. Chinese commercial banks need portfolio management through securities investment in addition to loan assets to improve profitability. However, volatility should be managed by adjusting the scale of securities management to an appropriate level.

• Management & Information Systems Review
• /
• 제28권3호
• /
• pp.1-23
• /
• 2009

Prior bankruptcy studies have established that bankrupt firm's pre-filing financial ratios are different from those of healthy firms or of randomly selected going concerns. However, they may not be sufficiently different from the financial ratios of other firms in financial distress to allow the development of a ratio-based model that predicts bankruptcy with reasonable accuracy. As the result, in the multiple discriminant model, independent variables divided firms into bankrupt firms and healthy firms are retained earnings to total asset, receivable turnover, net income to sales, financial expenses, inventory turnover, owner's equity to total asset, cash flow to current liability, and current asset to current liability. Moreover four variables Retained earnings to total asset, net income to sales, total asset turnover, owner's equity to total asset indicate that these valuables classify bankrupt firms and distress firms. On the other hand, Owner's Equity to borrowed capital, Ordinary income to Net Sales, Operating Income to Total Asset, Total Asset Turnover and Inventory Turnover are selected to predict bankruptcy possibility in the Logistic regression model.

• Journal of Intelligence and Information Systems
• /
• 제7권1호
• /
• pp.135-147
• /
• 2001

This study proposes bankruptcy prediction model using fuzzy neural networks. Neural networks offer preeminent learning ability but they are often confronted with the inconsistent and unpredictable performance for noisy financial data. The existence of continuous data and large amounts of records may pose a challenging task to explicit concepts extraction from the raw data due to the huge data space determined by continuous input variables. The attempt to solve this problem is to transform each input variable in a way which may make it easier fur neural network to develop a predictive relationship. One of the methods selected for this is to map each continuous input variable to a series of overlapping fuzzy sets. Appropriately transforming each of the inputs into overlapping fuzzy membership sets provides an isomorphic mapping of the data to properly constructed membership values, and as such, no information is lost. In addition, it is easier far neural network to identify and model high-order interactions when the data is transformed in this way. Experimental results show that fuzzy neural network outperforms conventional neural network for the prediction of corporate bankruptcy.

• Journal of Intelligence and Information Systems
• /
• 제13권2호
• /
• pp.15-26
• /
• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.

• Information Systems Review
• /
• 제24권4호
• /
• pp.23-40
• /
• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• KIPS Transactions on Software and Data Engineering
• /
• 제12권8호
• /
• pp.333-340
• /
• 2023

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a post-explanation of the bankruptcy prediction process.