• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.449-453
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• 2014

Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.

• 한국건설관리학회논문집
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• 제19권5호
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• pp.81-89
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• 2018

본 연구의 목적은 플랜트 건설사업에서 모듈러 공법의 적용이 확대됨에 따라 모듈러 플랜트에 대한 업무특성을 고려하여 위험을 평가하고 위험에 대응하기 위한 예비비를 예측하는 것이다. 연구방법은 모듈러 플랜트의 업무특성을 고려하여 위험의 영향을 평가하기 위한 모델(방법)과 예비비를 예측하기 위한 모델(방법)을 제시한다. 그리고 제시된 모델을 기반으로 모듈러 플랜트 건설사업 1곳을 사례로 선정하여 위험요인의 영향을 평가하고 예비비를 예측한다. 상기와 같은 목적과 방법에 따라 도출된 결과는 다음과 같다. 위험요인의 발생확률과 영향점수를 평가하여 중요 위험요인 15개를 선정하였다. 그리고 모듈러 플랜트의 특성을 고려하기 위하여 설계(E), 구매(P), 제작(F), 운송(T), 시공(C)단계로 업무를 분류하여 예측된 예비비는 기초사업비(610,503,596 천원) 대비 약 6.739%이며, 설계(E) 2.850%, 구매(P) 6.225%, 제작(F) 6.211%, 운송(T) 4.165%, 시공(C) 8.168%로 도출되었다. 본 모델은 위험관리를 위한 의사결정 과정에서 정량적인 결과를 도출하는 방법으로 활용된다.

• International Journal of Quality Innovation
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• 제7권2호
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• pp.97-124
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• 2006

The purpose of this research is to create an expert risk-based piping system inspection model. The proposed system includes a risk-based piping inspection system and a piping inspection guideline system. The research procedure consists of three parts: the risk-based inspection model, the risk-based piping inspection model, and the piping inspection guideline system model. In this research procedure, a field plant visit is conducted to collect the related domestic information (Taiwan) and foreign standards and regulations for creating a strategic risk-based piping inspection and analysis system in accordance with the piping damage characteristics in the petrochemical industry. In accordance with various piping damage models and damage positions, petrochemical plants provide the optimal piping inspection planning tool for efficient piping risk prediction for enhancing plant operation safety.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 추계학술대회 논문집
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• pp.617-618
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• 2009

• International Journal of Advanced Culture Technology
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• 제7권1호
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• pp.8-13
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• 2019

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non-HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.

• 한국멀티미디어학회논문지
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• 제25권3호
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• pp.510-518
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• 2022

This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.

• Nuclear Engineering and Technology
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• 제52권12호
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• pp.2836-2846
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• 2020

During severe nuclear power plant (NPP) accidents, a H₂/CO mixture can be generated in the reactor pressure vessel by core degradation and in the containment as well by molten corium-concrete interaction. In spite of its importance, a state-of-the-art methodology predicting H₂/CO combustion risk relies predominantly on empirical correlations. It is therefore necessary to develop a proper methodology for flammability evaluation of H₂/CO mixtures at ex-vessel phases characterized by three factors: CO concentration, high temperature, and diluents. The developed methodology adopted Le Chatelier's law and a calculated non-adiabatic flame temperature model. The methodology allows the consideration of the individual effect of the heat transfer characteristics of hydrogen and carbon monoxide on low flammability limit prediction. The accuracy of the developed model was verified using experimental data relevant to ex-vessel phase conditions. With the developed model, the prediction accuracy was improved substantially such that the maximum relative prediction error was approximately 25% while the existing methodology showed a 76% error. The developed methodology is expected to be applicable for flammability evaluation in chemical as well as NPP industries.

• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• 한국재난정보학회 논문집
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• 제16권4호
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• pp.842-848
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• 2020

연구목적: 노후화된 발전기의 지속 가능한 운영을 위하여 효율적이며, 안전한 운영이 중요하다. 효율적 운영이란 경제적 관점이며, 안전한 운영은 발전 설비의 치명적 사고 발생에 대한 발생 이전의 사전 조치를 말한다. 그러므로 발전기의 지속가능 운영 모니터링을 위하여 관련된 센서 설치와 이를 기반으로 지속 가능에 대한 예측할 수 있는 모델에 대한 연구가 필요하다. 연구방법: 전기와 열에 대한 수요 예측, 엔진의 성능과 이상을 탐지하는 예측, 그리고 재 난 안전에 대한 예측 모델을 제시하였다. 이를 위하여 필요한 센서를 정의하였으며, 이를 기반으로 예측 모델을 각각 개발하여 수행하였다. 연구결과: 수요 예측 모델은 기존의 79%에서 90% 이상으로 예측 정확도를 향상시켰으며, 다른 2개 모델도 시스템의 지속가능한 안정적 운영을 지원하였다. 결론: 노후화된 발전설비의 지속가능 운영을 지원하기 위한 3가지 종류의 예측 모델을 개발하고 이를 제이비주식회사의 발전 설비에 실제 적용하여 운영하고 있다.

• 한국산학기술학회논문지
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• 제19권4호
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• pp.603-610
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• 2018

본 산업의 발달 및 화석연료 사용 증가로 인하여 지구온난화 및 기후변화가 가속화되어 기존보다 강도 높은 자연재해가 빈번하게 발생하고 있다. 전기시설물은 옥외에 시설된 경우가 많아 자연재해에 큰 영향을 받아 전기설비 관련 사고가 증가하는 추세이다. 본 논문에서는 국내의 기후변화에 따른 전기화재, 감전사고 및 전기설비사고의 통계 현황을 분석하여 기후변화와 연계한 위험도를 제시한다. 또한, 다양한 지역 별(광역시) 기후조건(온도, 습도)과 연계한 전기재해 데이터 분석을 통하여 각 지역의 월별 전기화재 위험도 분석 모델을 제시하고, 저압, 고압 설비의 자연재해에 대한 사고 위험도를 분석한다. 이러한 지역별, 설비별 위험도 분석 모델을 통하여 기초적인 전기재해 예측 모델을 제시하였다. 따라서 제시한 분석 데이터를 활용하여 향후 각 지역 및 전기설비를 대상으로 전기재해 위험도 예측 맵을 웹사이트나 스마트폰 앱을 통하여 전기안전 서비스를 제안할 수 있으며, 기후변화의 따른 자연재해에 대한 전기사고를 미연에 방지하기 위한 내성기준이나 전기설비의 내구성을 증가시키기 위한 노력이 필요하다.