• Fire Science and Engineering
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• v.24 no.3
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• pp.78-85
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• 2010

Currently, there are various institutions performing fire investigation and identification, such as fire stations and police stations as well as institutes related to fire safety, etc. And the manpower working at the institutions reaches a large number of persons. But there is no objective index on the expertise of the persons. In this paper, we suggested the examination criteria through job analysis and the enforcement method of the exam system. And we developed suitable exam subjects and exam content specifications for qualification of fire identification and estimation that investigate a fire cause, combustion, escape circumstances and fire facilities at the scene of a fire, survey the fire damage and analysis fire cause, etc. It will increase the public trust to develop national technical qualification items of the fire identification and estimation engineer.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.93-104
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• 2008

A successful fire risk assessment is depends on identification of risk, the analytical process of potential risk, on estimation of likelihood and the width and depth of consequence. Take the influence on enterprise into consideration, Fire risk assessment could carry out along the evaluation of the risk importance, the risk level and the risk acceptance. A large part of the limitation of choosing the risk assessment techniques impose restrictions on expense and time. If it is unnecessary high level risk assessment or Probabilistic Risk Assessment of buildings, in compliance with the Relative Ranking Method, Fire risk indexing and assessing is possible. As working-level technique, AHP method is useful with practical technique.

• Fire Science and Engineering
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• v.23 no.4
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• pp.120-129
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• 2009

Identification of the exact cause of a building fire is generally difficult, because the source and initially ignited objects are often severely damaged or even lost during the early stages of the fire. We made an experimental attempt to reasonably estimate the burning during the very early stages of a fire, and identify its source and causes. The case we studied was an apartment fire, which occurred in Tokyo, in July 2002. The fire was extinguished just after flashover, and the on-site investigations suggested the fire started from the TV and TV stand, which had been damaged so severely that it was difficult to conclude that the TV was the ultimate cause of the fire, simply from the on-site investigation. We conducted a series of burn tests using a TV and other products identical to those actually used in the apartment. Tests were set-up and procedures were carefully studied to recreate the conditions of the articles that remained, and of the room itself. The tests demonstrated that the conditions in the apartment could be recreated only when the fire started inside the TV and came into close contact with dresser.

• The Korean Journal of Emergency Medical Services
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• v.6 no.1
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• pp.77-86
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• 2002

The purposes of this research which was conducted by surveying lost/added unit hours reports, unit hour demand analysis worksheets from prehospital care reports of two squads in Kyonggi Provincial Fire and Disaster Headquarters for 20 weeks (January 1, 2002 - May 20, 2002) are to get Unit Hour Utilizations. Call Demands such as Unit Hour Demand, Simple Average Demand, High Average Demand, Peak Average Demand, the High Actual Demand. The conclusions from this analysis were summarized as follows: (1) By revealing Unit Hour Produced 3223.9, Call Volume 964, Unit Hour Utilization 0.299 at the Squad A and Unit Hour Produced 3328.4, Call Volume 901, Unit Hour Utilization 0.271 at the Squad B induced Korean Squads to chance identification, definition, direction of Unit Hour Utilization. (2) By revealing Simple Average Demand 7.4 on Monday Tuesday, High Average Demand 9.6 on Tuesday Friday. Peak Average Demand 11.5 on Tuesday, the High Actual Demand 12 on Tuesday Wednesday at the Squad A and Simple Average Demand 6.8 on Sunday, High Average Demand 10.4 on Monday, Peak Average Demand 11.5 on Monday, the High Actual Demand 13 on Monday at the Squad B enabled Korean Squads to utilize System Status Management. (3) The Maximum Calls per Unit Hour were 115 for 23:00~23:59, the Minimum Calls per Unit Hour were 46 for 05:00~05:49 in two squads. The Maximum Calls per Unit Hour were 7.4 on Tuesday Saturday, the Minimum Calls per Unit Hour were 6.1 on Thursday at the Squad A. The Maximum Calls per Unit Hour were 7.3 on Monday Saturday, the Minimum Calls per Unit Hour were 5.6 on Thursday at the Squad B. (4) Analyzing demand for EMTs in the optimum emergency medical service of Korea, we have been able to utilize this Unit Hour Utilization in company with the established estimation methods such as international comparisons or the number of ambulances for scientific reasonable estimation. (5) These Call Demands which were limited to the demand time in this study will make us expect some following studies including demand time, demand time, demand map for Strategic Deployment.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.