• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.116-124
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• 2023

As the flood damage on small streams increase due to the increase in frequency of extreme climate events, the need to measure hydraulic data of them has increased for disaster risk management. National Disaster Management Institute, Ministry of Interior and Safety develops CADMT, a CCTV-based automatic discharge measurement technology, and operates pilot small streams to verify its performance and develop disaster risk management technology. The research selects two small streams such as the Neungmac and the Jungsunpil streams to develop the Nomograph by using the 4-Parameter Logistic method using only the observed rainfall data from the Automatic Weather System operated by the Korea Meteorological Agency closest to the small streams and discharge data collected by using the CADMT. To evaluate developed Nomograph, the research forecasts floods discharges in each small stream and compares the result with the observed discharges. As a result of the evaluations, the forecasted value is found to represent the observed value well, so if more accurate observed data are collected and the Nomograph based on it is developed in the future, the high-accuracy flood prediction and warning will be possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1419-1429
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• 2010

Industry structure and environment of the domestic bank have been changed by an influx of large foreign-banks and advanced financial products when the currency crisis erupted in Korea. In a competitive environment, accurate forecasts of changes and tendencies are essential for the survival and development. Forecast of whether to approve loan applications for customer or not is an important matter because that is related to profit generation and risk management on the bank. Therefore, this paper proposes the method to improve forecast accuracy of loan underwriting. Processes in experiments are as follows. First, we select the predictor variables which affect significantly to the result of loan underwriting by correlation analysis and feature selection technique, and then cluster the customers by the 2-Step clustering technique based on selected variables. Second, we find the most accurate forecasting model for each clustering by applying LR, NN and SVM. Finally, we compare the forecasting accuracy of the proposed method with the forecasting accuracy of existing application way.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Korean Journal of Construction Engineering and Management
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• v.15 no.1
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• pp.3-10
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• 2014

High-rise buildings need thorough cost management because of large size and high risk. Cost management makes a budget by establishing and analyzing detail element at planning phase, needs cost control as each design phase, then reflected to next design. This research develops a schematic estimation model based on vertical and horizontal elements at design phase for structure cost of high-rise buildings to reduce error range and use data as design management. Usability of the model is confirmed by case study. The estimation model is expected to contribute to making the cost model more effective and satisfactory to concerned in construction or budget department and manage keeping track of the cost.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.323-334
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• 2015

he current VaR model based on the J.P. Morgan's RiskMetrics structurally can not reflect the future economic situation. In this study, we propose a One-factor model resulting from the Wiener stochastic process decomposed into a systematic risk factor and an idiosyncratic risk factor. Therefore, we are able to perform a preemptive risk management by means of reflecting the predicted common risk factors in the model. Stocks in the portfolio are satisfied with the independence to each other because the common factors are fixed by the predicted value. Therefore, we can easily determine the investment in each stock to minimize the variance of the portfolio. In addition, the portfolio VaR is decomposed into the sum of the individual VaR. So we can effectively implement the constitution of the portfolio to meet the target maximum losses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.471-480
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• 2015

The market of public-private partnership (PPP) projects has reduced from 9.4 trillion won in 2007 to 4.5 trillion won in 2012. However, the need of PPP projects is brought up by a massive down scale of government financial business. Previous studies regarding PPP projects mostly evaluate profitability from the financial perspective or analyze risk factors as a whole. Although PPP projects generally have complex structure involving diverse stakeholders, such as contractor, financial investor, and special purpose company (SPC) operators, existing studies have rarely considered the different viewpoints of PPP project stakeholders. Therefore, purpose of this study is to develop a structural equation model (SEM) considering the diverse stakeholders of PPP projects. To this end, the authors first reviewed the organizational structure of PPP projects. Next, the identification of the factors affecting project profitability are done via comprehensive literature reviews. After that, we conducted in-depth interviews and questionnaire surveys to reflect stakeholders' perspectives (contractors, financial investors, and SPC operators). As a result, a SEM model is developed to analyze direct and indirect effect on the PPP project performances. Finally, using the analysis results, relevant implications and directions for improvements are discussed. The prediction of the business performance of contractor, financial investor, and SPC operator is expect to be possible through the model developed and supports the strategy deduction that is appropriate for the participants.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.10a
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• pp.13.2-13.2
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• 2011

뼈의 역학기능을 예측하는 인자(predictive factor) 로서 골밀도(BMD)만으로는 충분하지 않다는 최근의 임상결과는, 골밀도 이외의 새로운 뼈의 강도 및 골절리스크를 지배하는 인자의 중요성을 보여준다. 이와 같은 골역학기능에 대한 골밀도 이외의 부가적인 지배인자를 골질(bone quality)이라고 하는데, 다양한 골질관련인자(bone quality-related factor) 중 하나의 지표로서 뼈의 주성분인 생체아파타이트(BAp)의 결정학적 방향성에 주목, 대표적인 경조직 질환을 해석하였다. 파골세포결손에 의해 대리석증을 유발하는 op/op마우스는 골밀도의 변화뿐만 아니라, 골질의 유의한 변화가 있었다. 즉, 이와 같은 결과는 파골세포결손에 의한 조골세포의 활성저하의 의해 골질이 저하됨을 시사하는 결과이며, 파골세포 과잉의 의해 골다공증을 유발하는 OPG-KO마우스는 골밀도가 급격히 저하됨과 동시에, BAp배향성도 급격히 낮아졌다. 즉, 골대사회전의 상승에 따른 섬유성골(woven bone)의 형성에 의해 BAp의 결정성장이 억제되며, 그 결과 BAp배향성이 저하된다고 사료된다. 이상, 본 연구에서는 대표적인 골 질환조직을 각각의 정상골과 비교함으로써, 골양(BMD)의 변화뿐만 아니라 골질(BAp배향성)의 변화를 발견하였다. 이와 같은 변화는 골질지표로서 BAp배향성이 유효하다는 것을 강하게 시사한다. 따라서 본 연구에 의해 얻어진 견해는 경조직 질환의 병리해명에 적용 가능함과 동시에, 경조직 질환의 진단 응용이나 치료약 개발, 임플란트 개발 등 폭넓은 분야에 유용하다고 할 수 있다.

• Proceedings of KOSOMES biannual meeting
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• 2007.11a
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• pp.193-195
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• 2007

해양생태계로 유입되는 화학물질의 총합적인 평가 및 관리를 위해서는 동 화합물의 해양환경중의 거동 및 운영, 생태계에의 영향, 관리방안에 따른 화학물질의 변화 예측 및 리스크 평가 등을 행할 필요가 있으며, 이를 위하여는 화학물질에 대한 생태계 모델이 유용한 수단이 될 수 있다. 본 연구에서는 여러 화학물질에 적용할 수 있으며, 지역특성, 존재 데이터 상황, 대상 수산물의 특성을 고려하여 여러 상태함수 및 프로세스의 추가와 삭제가 가능한 3차원 생태계 모델(EMT-3D)을 사용하여 해양환경중의 PFCs 관련물질을 대상으로 그 적용성을 검토하였으며, 민감도 분석 및 시나리오 분석을 행하여 영향인자를 판별하고 대안에 따른 영향을 평가하였다.