• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2010.04a
• /
• pp.536-541
• /
• 2010

최근 도시화, 산업화 등으로 인하여 인구 및 도시시설이 밀집되어 자연적 재해 및 인위적 재난에 취약한 구조를 가지고 있으며, 오래된 시가지와 고지대주거지를 중심으로 재난에 무방비한 장소들이 존재한다. 이는 급속한 도시화 과정에서 비롯된 것으로 도시기반시설이 정비되지 않은 상황에서 무질서한 도시팽창이 이루어낸 결과라고 할 수 있다. 따라서 우리나라의 도시화재 발생 위험성을 평가하기 위한 도시화재의 물리적 연소성상 예측모델을 구축하기 위해 건물간격에 따른 유풍시에 개구분출화염의 성상에 대한 실험을 실시하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.100-103
• /
• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• Journal of Korean Society of societal Security
• /
• v.1 no.3
• /
• pp.51-57
• /
• 2008

The long-term behaviors of prestressed concrete cable-stayed bridges are considerably influenced by the time dependant material characteristics such as creep and shrinkage. This study investigated the influences of the change of relative humidity by application of the CEB-FIP model and ACI model, which are generally used in the prediction of long-term behavior of concrete structures. In case of the moment of girder, CEB-FIP model predicted a bigger effect of relative humidity change than the ACI model. Furthermore, the effect was significant. Also, the long-term behaviors between these models were different each other even under the same material condition. Therefore, the prediction of the long-term behavior should be compensated after comparative analysis with the results of material tests of each construction site and between the different models.

• Journal of Distribution Science
• /
• v.7 no.2
• /
• pp.5-28
• /
• 2009

As a result of this study only a few Korean firms have a certain management methods designed to predict the possibility of risk occurrence and establishment of systematic countermeasure. Besides, the Korean firms do not have enough data on the risk of Chinese Market. The risk management department inside the firm does not function efficiently, and when it comes to investigation of risk, it heavily depends on that of local branches. Accordingly, in order to accurately recognize and manage, the firms need to not only specialize risk management department but also outsource by using a consulting firm.

• The Transactions of the Korea Information Processing Society
• /
• v.13 no.6
• /
• pp.260-268
• /
• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.

• Bulletin of the Korean Space Science Society
• /
• 2003.10a
• /
• pp.66-66
• /
• 2003

우주 공간이라는 극한 상황에서 운용되는 인공위성을 개발하기 위해서는 실제 제작 공간인 지상에서 가능한 모든 우주 공간에서의 위험을 예측하여 원하지 않는 재난을 방지할 수 있는 설계를 수행함이 요망된다. 위성의 기동 및 자세 제어에 사용되는 하이 드라진 추진시스템의 경우 예상되는 가장 큰 재난은 추진제의 동결로 인한 추진시스템의 작동 불능이다. 본 연구에서는 추진시스템의 안정적 작동을 위해 요구되는 추진제의 동결 방지를 위해 사용되는 히터 사양을 결정하며 이를 위해 위성 추진시스템의 열ㆍ수학적 모델을 개발한다. 개발된 열ㆍ수학적 모델의 타당성을 검증하기 위해 수치적으로 계산된 결과를 열진공 시험의 결과와 비교 연구한다 이론적 해석 모델과 열진공 시험조건 사이의 다소의 불일치성에도 불구하고 두 결과는 정성적으로 잘 부합된다. 따라서 본 연구를 통해 위성 추진시스템의 히터가 적절히 설계되었으며 개발된 열ㆍ수학적 모델은 인공위성 추진시스템의 주요한 설계 수단으로 사용될 수 있음을 검증한다.

• Proceedings of the KIEE Conference
• /
• 2004.05a
• /
• pp.74-76
• /
• 2004

현재 주로 사용되는 네트웍 침입 탐지 기법은, 사람에 의해 분석되고 룰의 형태로 저장된 침입 시그너쳐를 기반으로 침입을 판별하는 것이다. 이러한 방법은 아직 알려지지 않은 침입에 대해 무력하다는 한계를 가진다. 이에 본 연구에서는 사람의 분석과 지식에 의존하지 않는 방법을 제안하여 그러한 한계를 극복하고자 하였다. 침입은 호스트의 컴퓨팅과 네트워킹 자원을 사용할 수 없게 되는 것이라고 볼 때, 네트웍 트래픽과 관련하여 호스트의 자원 사용 상태가 어떻게 변화하는지 미리 알 수 있다면, 해당 침입에 대한 사전지식 없이도 위험에 대비할 수 있다. 본 논문에서는 자원의 가용성 측면에서 호스트 상태를 설명하는 모델을 설계하여, 이 모델이 네트웍 트래픽의 진행에 따라 변화하는 추이를 예측하는 데 사통될 수 있도록 하였다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.4
• /
• pp.603-610
• /
• 2018

The development of industry and the increase in the use of fossil fuels have accelerated the process of global warming and climate change, resulting in more frequent and intense natural disasters than ever before. Since electricity facilities are often installed outdoors, they are heavily influenced by natural disasters and the number of related accidents is increasing. In this paper, we analyzed the statistical status of domestic electrical fires, electric shock accidents, and electrical equipment accidents and hence analyzed the risk associated with climate change. Through the analysis of the electrical accidental data in connection with the various regional (metropolitan) climatic conditions (temperature, humidity), the risk rating and charts for each region and each equipment were produced. Based on this analysis, a basic electric risk prediction model is presented and a method of displaying an electric hazard prediction map for each region and each type of electric facilities through a website or smart phone app was developed using the proposed analysis data. In addition, efforts should be made to increase the durability of the electrical equipment and improve the resistance standards to prevent future disasters.

• Fire Science and Engineering
• /
• v.30 no.6
• /
• pp.37-42
• /
• 2016

The present study has been performed to examine the feasibility of a flame spread model on the design fire scenario for fire risk analysis. Thermo-Gravimetric analysis and sample burning test were conducted to obtain the material properties of a single couch covered with synthetic leather material and a series of FDS calculations applying with the measured material properties were performed for different grid sizes. The overall fire growth characteristics predicted by the fire model were quite different from the results of a real scale fire test and the initial peak value of the HRR and total released energy showed the results within a 30% discrepancy for the computational grids used in the present study. The current model has some limitations in predicting the fire growth characteristics, such as fire growth rate and the time to the maximum HRR. This study shows that the fire model may be applicable to creating the design fire scenario through continuous model improvement and detailed material properties.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.11
• /
• pp.433-440
• /
• 2019

Recently, machine learning and data mining have been used for many disease prediction and diagnosis. Chronic diseases account for about 80% of the total mortality rate and are increasing gradually. In previous studies, the predictive model for chronic diseases use data such as blood glucose, blood pressure, and insulin levels. In this paper, world's first research, verifies the relationship between dyslipidemia and facial characteristics, and develops the predictive model using machine learning based facial characteristics. Clinical data were obtained from 5390 adult Korean men, and using hypertriglyceridemia and facial characteristics data. Hypertriglyceridemia is a measure of dyslipidemia. The result of this study, find the facial characteristics that highly correlated with hypertriglyceridemia. FD_43_143_aD (p<0.0001, Area Under the receiver operating characteristics Curve(AUC)=0.652) is the best indicator of this study. FD_43_143_aD means distance between mandibular. The model based on this result obtained AUC value of 0.662. These results will provide a basis for predicting various diseases with only facial characteristics in the screening stage of disease epidemiology and public health in the future.