• Fire Science and Engineering
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• v.20 no.4 s.64
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• pp.105-109
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• 2006

Fire station is a response agency of disaster management. Its various field experience and materials could build up to database to support fire prevention and fire fighting, but it has not been worked out efficiently. To overcome this inefficiency, National Emergency Management Agency(NEMA) has made total improvement in "National Fire Investigation Data Classification System" mainly done by its Fire Investigation and Analysis Team. This study reviews existing fire investigation and data accumulation and analysis process so that it could be used as a basic data for "National Fire Investigation Data Classification System" operation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.139-140
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• 2022

This study aims to present basic data for fire risk assessment. In the existing fire risk assessment, the operation of fire safety facilities is not considered. In addition, there is a lack of data on the fire growth rate to predict the spread of fire. Therefore, this study intends to build a fire scenario using fire statistics data. In addition, the fire growth rate is to be derived in consideration of the floor area of burnout and the cause of fire.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.139-144
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• 2006

Forest fire is due to difficulty in approaching the forest fire at the time of forest fire and quite a long of time required for post-fire investigation, accurate analysis of damages to the forest area caused by forest fire is difficult to obtain. Recently, In attempt to overcome such difficulty, many researches are using satellite images. Nevertheless, it is not easy for everyone to obtain the satellite image data, and additional researches in order to verify accuracy of such data are also required. Therefore, in this study for satellite image to about damages to the forest areas caused by forest fire using tile selected two data of spectral reflectance of the vegetation, gained by using a spectrometer. That is we wished to search about mistake that is apt to happen by one time eyesight observation by analyzing two datas that is used spectral radiometer 3 months and 6 months later and gets.

• Steel and Composite Structures
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• v.12 no.4
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• pp.325-350
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• 2012

Concrete filled steel hollow structural sections (CFSs) are an efficient, sustainable, and attractive option for both ambient temperature and fire resistance design of columns in multi-storey buildings and are becoming increasingly common in modern construction practice around the world. Whilst the design of these sections at ambient temperatures is reasonably well understood, and models to predict the strength and failure modes of these elements at ambient temperatures correlate well with observations from tests, this appears not to be true in the case of fire resistant design. This paper reviews available data from furnace tests on CFS columns and assesses the statistical confidence in available fire resistance design models/approaches used in North America and Europe. This is done using a meta-analysis comparing the available experimental data from large-scale standard fire tests performed around the world against fire resistance predictions from design codes. It is shown that available design approaches carry a very large uncertainty of prediction, suggesting that they fail to properly account for fundamental aspects of the underlying thermal response and/or structural mechanics during fire. Current North American fire resistance design approaches for CFS columns are shown to be considerably less conservative, on average, than those used in Europe.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.685-689
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• 2023

The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

• Fire Science and Engineering
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• v.18 no.3
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• pp.74-94
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• 2004

Fire trends in Korea, Japan, and the United States were analyzed in this paper. The number of fires in the U.S. tends to decrease greatly from 3,264,500 cases in 1977 to 1,687,500 in 2002. There have been an estimated 60,000 fires annually more than 20 years in Japan, at the peak of 73,072 cases in 1973. In case of Korea, 5438 fires occurred in 1980. However, fires increased rapidly up to 36,169 cases in 2001 and 31,372 cases in 2003. In addition, fire statistics processing methods and fire statistics reports were analyzed. The result was that Korea was behind most of the three countries. Therefore I confirmed that we need not only to study systematical processing methods of fire statistics in order to get confidence on those like accurate estimating fire data analysis, but also to publish variable and specific reports on fires for the purpose of analyzing fire data.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.186-188
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• 2005

Because forest fire changes the direction according to the environmental elements, it is difficult to predict the direction of it. Currently, though some researchers have been studied to which predict the forest fire occurrence and the direction of it, using the remote detection technique, it is not enough and efficient. And recently because of the development of the sensor technique, a lot of In-Situ sensors are being developed. These kinds of In-Situ sensor data are used to collect the environmental elements such as temperature, humidity, and the velocity of the wind. Accordingly we need the prediction technique about the environmental elements analysis and the direction of the forest fire, using the In-Situ sensor data. In this paper, as a technique for predicting the direction of the forest fire, we propose the correlation analysis technique about In-Situ sensor data such as temperature, humidity, the velocity of the wind. The proposed technique is based on the clustering method and clusters the In-Situ sensor data. And then it analyzes the correlation of the multivariate correlations among clusters. These kinds of prediction information not only helps to predict the direction of the forest fire, but also finds the solution after predicting the environmental elements of the forest fire. Accordingly, this technique is expected to reduce the damage by the forest fire which occurs frequently these days.

• Journal of the Korean Institute of Gas
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• v.24 no.5
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• pp.47-55
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• 2020

Hazardous materials accidents are not limited to the leakage of the material, but if the early response is not appropriate, it can lead to a fire or an explosion, which increases the scale of the damage. However, as the 4th industrial revolution and the rise of the big data era are being discussed, systematic analysis of hazardous materials accidents based on new techniques has not been attempted, but simple statistics are being collected. In this study, we perform the systematic analysis, using machine learning, on the fire accident data for the past 11 years (2008 ~ 2018), accumulated by the National Fire Service. The analysis results are visualized and presented through text mining analysis, and the possibility of developing a damage-scale prediction model is explored by applying the regression analysis method, using the main factors present in the hazardous materials fire accident data.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.319-326
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• 1997

The fire accident cases of several countries such as Korea, Japan, United States, etc., were collected and compared statistically. The trends of fire accidents in several countries will help us establish detailed plans for fire protection and reduce the possible fire accidents in the future. For construction of data base system, the program FADS was developed, which is operable in Windows environment.