• Fire Science and Engineering
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• v.32 no.4
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• pp.7-16
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• 2018

In this study, the prediction performance of design fire curves (DF) was evaluated for gas fires in a compartment by using CFAST. The CFAST simulations adopted the 2-stage DF suggested by the previous study and the Quadratic and Exponential DF suggested by Ingason. It was found by comparing the simulation and experimental results that the overall prediction performance of the design fire cures for the spatially-averaged temperature and concentrations of $O_2$ and $CO_2$ was, from the most reasonable to the most inaccurate, 2-stage DF > Quadratic DF > Exponential DF. The CFAST simulation could not predict for the difference in the spatially-averaged temperature and concentrations of $O_2$ and $CO_2$ at door and inner side locations in a compartment. The CFAST simulations also showed a limitation in the prediction of the spatially-averaged temperature at lower layer and the concentration of CO.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.7-7
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• 2016

인도네시아 산불에 의한 연무는 동남아시아 인접한 국가들에 있어서 심각한 환경문제 중 하나이다. 국제적으로 심각한 문제를 야기하는 인도네시아의 산불은 건조기에 강수량이 적게 내리는 극심한 가뭄 조건에서 발생한다. 건조기 강수량을 모니터링 하는 것은 산불 발생 가능성을 예측하기 위해 중요하지만 산불을 사전에 예방하고 영향을 최소화하기에는 부족하다. 따라서 산불에 대한 선제적 사전예방을 위해서는 수개월의 선행예측 기간을 갖는 조기경보 시스템이 절실하다. 따라서 본 연구는 인도네시아 산불에 대한 선제적 대응을 위한 강수량 예측시스템을 개발하고 예측성을 평가하여 동남아시아 지역의 화재 연무 조기경보 시스템의 시제품(Prototype)을 개발하는데 있다. 강수량 예측을 위해서 APEC 기후센터의 계절예측정보의 활용 정도에 따라서 4가지 서로 다른 방법을 통합하여 사용하였다. 예측정보 기반의 방법들로는 대상지역의 강수량 예측을 위해서 대상 지역 상공의 계절예측 강수자료를 보정을 통해 직접적으로 사용하는 SBC (Simple Bias Correction) 방법과 대상 지역 상공의 강수 예측자료를 사용하는 대신에 지역 강수량과 높은 상관 관계를 보이는 다른 지역의 대리변수를 예측인자로 사용하는 MWR (Moving Window Regression) 방법이 있다. 또한 예측자료의 사용 없이 과거자료 기반의 기후지수(Climate Index) 중에서 지체시간을 고려하여 지역 강수량과 높은 상관관계를 갖는 경우 예측에 활용하는 관측자료 기반의 CIR (Climate Index Regression) 방법과 예측기반 MWR과 관측기반의 CIR 방법에서 선정된 예측인자를 동시에 활용하는 ITR (Integrated Time Regression) 방법이 사용되었다. 장기 강수량 예측은 보르네오 섬의 4개 지역에서 3개월 이하의 선행예측기간에 대하여 0.5 이상의 TCC (Temporal Correlation Coefficient)의 값을 보여 양호한 예측성능을 보였다. 예측된 강수량 자료는 위성기반 관측 강수량 및 관측 탄소 배출량 관계에서 결정된 강수량의 임계값과의 비교를 통해 산불발생 가능성으로 환산하였다. 개발된 조기경보 시스템은 산불 발생에 가장 취약한 해당지역의 건조기(8월~10월) 강수량을 4월부터 예측해 산불 연무에 대한 조기경보를 수행한다. 개발된 화재 연무조기경보 시스템은 지속적인 개선을 통해 현장 실효성을 높여 동남아국가 정부의 화재 및 산림관리자들에게 보급함으로써 동남아의 화재 연무로 인한 환경문제 해결에 기여할 수 있으리라 판단된다.

• Fire Science and Engineering
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• v.33 no.6
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• pp.20-27
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• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• Fire Science and Engineering
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• v.24 no.5
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• pp.60-67
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• 2010

Fire model shall be verified and validated to reliably predict the consequences of fires within its limitations. Generally the verification and validation procedures are conducted by comparison with experimental test data. This study aims to evaluate predictive capabilities of FDS in the pool fire with two rooms and the sensitivity between input parameters such as heat release rate and ventilation rate and the output values like temperature, concentration, and heat flux. The predictive capabilities of FDS will be evaluated by comparing FDS simulation results with PRISME experimental data which result from the international fire test project. The sensitivity analysis will be conducted to decide which one of input parameters affects outcomes by comparison of FDS results with ${\pm}$ 10% changes of input parameter. From this study, the FDS predictive capabilities are within 20% error range. Heat release rate as input parameter affects most of outcomes and flow rate only has relation with concentration of oxygen and combustion products.

• Fire Science and Engineering
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• v.12 no.1
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• pp.15-21
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• 1998

In this research, we studied reach the conclusion with more probability for predicting the severity which based on fire cases in domestic department stores for last 30 years. Considering the number of yearly fire cases in department stores and the cost of damage, we set the risk level. Moreover, this research shows the severity of fire in department stores through its scenario applying to FPETOOL program which NIST in USA has developed. By the result of FPETOOL program operation, we could acquired information about the time reaching the point where people are in danger in temperature, smoke layer and gas concentration. When a fire breaks out in a department store, a great loss of property and life is significant, as well as the potential risk is awfully considerable. Therefore, we should prevent a five from occuring.

• Fire Science and Engineering
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• v.11 no.2
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• pp.25-33
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• 1997

Fire hazards in an apartment building that represents the average households in Korean were investigated by conducting a full-scale experiment. This experiment attempts to analyze fire hazards using materials, and furnishings common to Korean housing stock. Experimental results are compared to the predictions of the C-FAST and smoke transport computer model. Comparisons between experimental data and C-FAST data are performed only to a living-room fire. Flashover occurred at approximately 380 seconds in a fire experinent, and at approximately 420 seconds in Zone-Model. Based on all of data between experimental data between experimental data and Zone-Model data, it is concluded that the safe egress time is at least 250 seconds.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.278-281
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• 2012

사무공간의 화재성상을 예측하기 위해 화재하중 $25kg/m^2$ 값을 적용하여 $2.4(L){\times}3.6(W){\times}2.4(H)\;m$ 크기의 Mock-up 화재실험을 진행하였다. 화재실험은 실물화재실험 장비인 LSC(Large Scale Calorimeter)에서 실시하였으며, 열방출률 및 질량감소율을 측정하였다. 실물화재실험 시작 후 약 1110 초에 플래시오버가 발생하였으며, 최대 열발출률은 1241.1 KW로 측정되었고 질량은 초기 219 kg에서 102 kg로 감소하였다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2011.04a
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• pp.165-169
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• 2011

실제 화재의 위험성을 평가하는 가장 좋은 방법은 직접 실재 화재 조건을 모사 또는 구현하여 평가하는 방법이다. 그러나, 비용과 시간 및 환경 문제를 생각한다면 쉬운 일은 아니다. 따라서 단위 재료를 태우거나, 전산 시뮬레이션을 활용하여 화재를 예측하는 방법을 활용한다. 본 연구에서는 콘칼로리미터 실험 결과를 기초 데이터로 실제 화재 실험인 room corner test의 총열방출량을 추정하였다. 그 결과 가연물의 부피 및 밀도와 보정상수를 활용하여 실제 총열방출량에 근접한 결과값을 얻을 수 있었으며, 산출된 총열방출량을 근거로 전산시뮬레이션을 수행, 시뮬레이션 결과값을 통해 재실자의 화재 및 연기에 대한 위험성 평가를 할 수 있었다. 본 결과를 통해 화재 시뮬레이션 수행 시 가연물에 따른 화재에 의한 위험성 평가를 할 수 있을 것으로 보인다.

• Fire Science and Engineering
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• v.24 no.3
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• pp.131-138
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• 2010

Experimental and numerical studies were conducted to investigate the thermal and chemical characteristics of heptane fires in a full-scale ISO 9705 room. Representative fire conditions were considered for over-ventilated fire (OVF) and under-ventilated fire (UVF). Fuel flow rate and doorway width were changed to create OVF and UVF conditions. Detailed comparisons of temperature and species concentrations between experimental and numerical data were presented in order to validate the predictive performance of FDS (Fire Dynamic Simulator). The OVF and UVF were explicitly characterized with distributions of temperature and product formation measured in the upper layer, as well as combustion efficiency and global equivalence ratio. It was shown that the numerical results provided a quantitatively realistic prediction of the experimental results observed in the OVF conditions. For the UVF, the numerically predicted temperature showed reasonable agreement with the measured temperature. The predicted steady-state volume fractions of $O_2$, $CO_2$, CO and THC also agreed quantitatively with the experimental data. Although there were some limitations to predict accurately the transient behavior in terms of CO production/consumption in the UVF condition, it was concluded that the current FDS was very useful tool to predict the fire characteristics inside the compartment for the OVF and UVF.