• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.56-58
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• 2014

최근 우리나라 연안해역에는 선박간의 빈번한 조우상황 및 교통량 증대로 해양사고 위험이 항상 잠재되어 있다. 또한, 선박 조선에 편의한 기술은 증대하고 있으나 충돌사고는 줄어들지 않고 있어, 경험을 기반으로 한 선박 조선은 한계에 왔으며, 선박의 충돌위험도의 정량화가 필요하다. 본 연구는 선박운항자의 주관적인 위험도를 나타내는 모델(PARK's Model / Potential Assessment of RisK)을 이용하여, 선박에서 간편하고 쉽게 이용할 수 있는 어플리케이션을 개발하고 이를 실제 선박에 탑재하여 그 효용성을 점검하여 선박통항 안전성에 기여하고자 한다. 이를 통해 선박에서 뿐만 아니라, 관제사에게 객관적인 정보를 제공하며, 예비 항해사를 위한 충돌예방 교육용 도구로도 사용하고자 한다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.822-831
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• 2021

Although programs have been developed to evaluate the risk of dragging anchors, it is practically difficult for VTS(vessel traffic service) operators to calculate and evaluate these risks by obtaining input factors from anchored ships. Therefore, in this study, the gross tonnage (GT) that could be easily obtained from the ship by the VTS operators was set as an independent variable, and linear and nonlinear regression analyses were performed using the input factors as the dependent variables. From comparing the fit of the polynomial model (linear) and power series model (nonlinear), the power series model was evaluated to be more suitable for all input factors in the case of container ships and bulk carriers. However, in the case of tanker ships, the power supply model was suitable for the LBP(length between perpendiculars), width, and draft, and the polynomial model was evaluated to be more suitable for the front wind pressure area, weight of the anchor, equipment number, and height of the hawse pipe from the bottom of the ship. In addition, all other dependent variables, except for the front wind pressure area factor of the tanker ship, showed high degrees of fit with a coefficient of determination (R-squared value) of 0.7 or more. Therefore, among the input factors of the dragging anchor risk assessment program, all factors except the external force, seabed quality, water depth, and amount of anchor chain let out are automatically applied by the regression analysis model formula when only the GT of the ship is provided.

• Korean Journal of Social Welfare
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• v.59 no.4
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• pp.245-272
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• 2007

The purpose of this study was to create and validate Scale of Resilience to Burnout and Scale of Burnout Risk for social workers in medical settings. For the purpose, instrument development model, a kind of mixed methods research was used. In the first phase, six dimensions of resilience(: professional competency, accomplishment and worthwhileness, firm belief and value about their profession, good teamwork, support by their agency, and individual resources) and six dimensions of risk to burnout(: dissatisfaction with organizational condition, interpersonal stress among team members, organizational conflict, work related stress, deficiency of professionalism, and individual stress) were suggested thorough the preceding papers with qualitative approaches. The second phase involved analysis of a survey of 185 participants that appeared to validate the dimensions of the measures. The construct validity and reliability of each measure were tested. And it was founded that there were its own factors in each concept, although resilience to burnout related negatively to burnout risk. The results of this study suggest mixed methods research is useful to develop measures reflecting voices in the social work field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6C
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• pp.219-229
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• 2010

Slope failure in South Korea generally occurs by the localized heavy rain in a rainy season and typhoon, and it annually causes huge losses of both life and property because nearly 70% of territory in South Korea is covered with mountains. It is required to measure the risk of slope failure quantitatively before proper prevention methods are provided. However, there is no way to estimate the risk based on realtime rainfall, geological characteristics, and geotechnical engineering properties. This study presents the development of digital terrion model to predict slope stability using infinite slope stability theory combined with temporal groundwater change. Case studies were performed to investigate factors to affect slope stability in Japan.

• Information Systems Review
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• v.19 no.4
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• pp.87-110
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• 2017

Analyzing and finding the risk factors in information technology (IT) projects have been discussed because risk management is an important issue in IT project management. This study obtained the risk factor checklists with priorities, analyzed the causal relationship of risk factors, and determined their influences on IT project management. However, only few studies systematically classified IT project risk factors in terms of risk exposure. These studies considered both the probability of occurrence and the degree of risk simultaneously. The present study determined 53 IT project risk factors on the basis of literature and expert group discussions. Additionally, this study presented clustering analysis based on the data of 140 project managers. The IT project risk factor classification framework was divided into four areas (HIHF, HILF, LIHF, and LILF). The present results can be used to help IT project managers establish effective risk management strategies and reduce IT project failures. This study also provides academic implication because it considers both the probability of occurrence and the degree of influence of risk factors.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.164-165
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• 2012

집중호우 및 홍수로 인해 침수지역이 발생할 경우 이를 예측하기 위해 IT를 융합한 방재에 대한 연구가 필요하며 특히 본 논문에서는 도시 침수에 대비하여 교통 통제, 우회 도로 등을 제공하기 위해 정량적인 침수 위험 지수를 접목시키는 방안에 대하여 연구하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.632-643
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• 2020

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.850-858
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• 2008

최근 공공기관 및 금융권에서는 경쟁력 향상을 위한 정보시스템의 통합으로 프로젝트 규모가 대형화되고 또한, 프로젝트 수가 증가하고 있어 적절한 소프트웨어 프로젝트 관리 방안이 필요하다. 그러나 프로젝트 관리 영역간의 미치는 영향에 대하여 체계적인 연구가 미흡하였다. 따라서 선행 연구를 통하여 프로젝트 관리에 중요한 영역을 도출하였고, 도출된 "범위, 일정, 품질, 인력, 위험"의 각 영역들간의 상호 미치는 영향도의 분석과 각 영역의 진행 상태를 "계획, 실행, 완료" 단계로 구분하여 수행도를 분석하였다. 분석된 영향도와 수행도의 결과를 종합하여 프로젝트 관리 수준을 평가하는 모델을 제시하였다. 본 연구는 IT 분야의 전문가 그룹을 통해 프로젝트 관리 영역들간의 영향 분석이 실증적으로 연구가 이루어졌고, 또한 각 영역의 진행상태를 측정 함으로써 실무적인 측면에서 더욱 체계적이고 균형 잡힌 프로젝트 관리와 감리 수행 시에 활용할 수 있을 것이다.