• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.91-103
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• 2022

Purpose: After COVID-19, changes in the educational ecosystem take place and fire service academy education system have shifted from face-to-face into non fact-to-face. So, the educational effect of fire officials is decreased and the satisfaction level is also decreased. In this study, we want to examine the current status of non-face-to-face real-time remote education and supplement the problems to improve the educational methods, the educational environment, etc. Method: This study is an independent variable that affects non-face-to-face real-time remote education, consisting of education system environment, self-efficacy of computers, contents (education contents, structure, design, etc.), and proper interaction. A dependent variable was selected with satisfaction for non-face-to-face real-time remote education. In addition, it was selected and analyzed as an active property of learning motivation and learning attitude as control variables. Result: The better the content and the more active the learning motivation and the attitude toward learning, the higher the satisfaction of non-face-to-face real-time remote education, and the more active the learning motivation and the attitude toward learning, the more positive the computer self-efficacy and the satisfaction of learning Conclusion: In order to increase the satisfaction of non-face-to-face real-time education due to COVID-19, education designers or professors need to provide non-face-to-face education contents that can increase the aggressiveness of their learning motivation and learning attitude, and to increase the satisfaction of education for learners by increasing computer self-efficacy through pre-education of non-face-to-face education systems.

• The Korean Journal of Emergency Medical Services
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• v.8 no.1
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• pp.19-32
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• 2004

This study was conducted to identify an initial fire station experience of EMT students, so to better understand their' practice experience in fire station. The subjects were 28 EMT students of C department of Emergency Medical technology in C city, who were demonstrating at the fire station in C city. This study was approached by phenomenological method, collected data were analyzed by Colaizzi's method, the results were as a follows. From the protocol, 201 significant statements were organized into 93 formulated meanings. From the formulated meanings, 30 themes were identified, organized into 16 theme clusters, and then 7 categories. EMT students got experienced 'tension' in resulting from new training situation and at the field practice, 'comport and gratitude', in feeling of identity and a bond sympathy with senior EMT, in training environment and heartfelt care, 'stress' from lack of knowledge and skill, difficulties in field practice for 24 hours, in dealing with making interpersonal relationship with patient and staffs and from the insufficiency of instruction, 'confidence feeling' from the participation of field treatment, in improving of learning, in self-esteem of EMT job, 'confusing feeling' of conflict of the path in work, 'disappointment and doubt' by the discrepancy between learning and actuality, and disappointment of actuality, 'feeling of lack' based on the passive attitude. The results of this study are to use as basic data for students attending fire station experience for the first time.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.22-28
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• 2021

This is a basic study on the development of deep learning-based algorithms to detect smoke before the smoke detector operates in the event of a ship fire, analyze and utilize the detected data, and support fire suppression and evacuation activities by predicting the spread of smoke before it spreads to remote areas. Proposed algorithms were reviewed in accordance with the following procedures. As a first step, smoke images obtained through fire simulation were applied to the YOLO (You Only Look Once) model, which is a deep learning-based object detection algorithm. The mean average precision (mAP) of the trained YOLO model was measured to be 98.71%, and smoke was detected at a processing speed of 9 frames per second (FPS). The second step was to estimate the spread of smoke using the coordinates of the boundary box, from which was utilized to extract the smoke geometry from YOLO. This smoke geometry was then applied to the time series prediction algorithm, long short-term memory (LSTM). As a result, smoke spread data obtained from the coordinates of the boundary box between the estimated fire occurrence and 30 s were entered into the LSTM learning model to predict smoke spread data from 31 s to 90 s in the smoke image of a fast fire obtained from fire simulation. The average square root error between the estimated spread of smoke and its predicted value was 2.74.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.626-632
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• 2017

Since natural disaster occurs increasingly and becomes complicated, it causes deaths, disappearances, and damage to property. As a result, there is a growing interest in the development of ICT-based natural disaster response technology which can minimize economic and social losses. In this letter, we introduce the main functions of the forest fire management platform by using images from an UAV. In addition, we propose a disaster image analysis technology based on the deep learning which is a key element technology for disaster detection. The proposed deep learning based disaster image analysis learns repeatedly generated images from the past, then it is possible to detect the disaster situation of forest-fire similar to a person. The validity of the proposed method is verified through the experimental performance of the proposed disaster image analysis technique.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.77-80
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• 2023

Since fires in uncontrolled environments pose serious risks to society and individuals, many researchers have been investigating technologies for early detection of fires that occur in everyday life. Recently, with the development of deep learning vision technology, research on fire detection models using neural network backbones such as Transformer and Convolution Natural Network has been actively conducted. Vision-based fire detection systems can solve many problems with physical sensor-based fire detection systems. This paper proposes a fire detection method using the latest YOLOv8, which improves the existing fire detection method. The proposed method develops a system that detects sparks and smoke from input images by training the Yolov8 model using a universal fire detection dataset. We also demonstrate the superiority of the proposed method through experiments by comparing it with existing methods.

• Journal of Platform Technology
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• v.9 no.3
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• pp.70-77
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• 2021

The purpose of Serious games in immersive Metaverse platform to provide users both fun and intriguing learning experiences. We proposes a serious game for self-trainable fire evacuation drill with collaboration among avatars synchronized with multiple trainees and optionally with real-time supervising placed at different remote physical locations. The proposed system architecture is composed of wearable motion sensors and a Head Mounted Display to synchronize each user's intended motions to her/his avatar activities in a cyberspace in Metaverse environment. The proposed system provides immersive as well as inexpensive environments for easy-to-use user interface for cyber experience-based fire evacuation training system. The proposed configuration of the user-avatar interface, the collaborative learning environment, and the evaluation system on the VR serious game are expected to be applied to other serious games. The game was implemented only for the predefined fire scenario for buildings, but the platform can extend its configuration for various disaster situations that may happen to the public.

• Fire Science and Engineering
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• v.26 no.2
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• pp.32-39
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• 2012

This research focuses on finding the way of improving creativity in fire stations by scrutinizing the factors influencing and enhancing creativity in organizations. As environmental changes including the needs of citizens as well as the climate change are moving culminated today, fire stations should be changed appropriately to get the legitimacy of existence not to mention their own mission accomplishments. In this respect, the creativity can be expected as the main factor for fire stations' going concern. This research provided 5 hypotheses to find whether the chosen factors such as vision, learning/unlearning and positive feedback affect the creativity in fire stations. To prove hypotheses are valid, we employed survey as a method in which 155 firemen in Seoul responded, ending up with getting the result that the vision formulation and sharing, unlearning activities and positive feedback improve organizational creativity. In conclusion, this paper suggests that fire stations establish useful policies for the creativity based on these research findings.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.476-481
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• 2019

Early detection of fire is an important measure for minimizing the loss of life and property damage. However, fire and smoke need to be simultaneously detected. In this context, numerous studies have been conducted on image-based fire detection. Conventional fire detection methods are compute-intensive and comprise several algorithms for extracting the flame and smoke characteristics. Hence, deep learning algorithms and convolution neural networks can be alternatively employed for fire detection. In this study, recorded image data of fire in a ship engine room were analyzed. The flame and smoke characteristics were extracted from the outer box, and the YOLO (You Only Look Once) convolutional neural network algorithm was subsequently employed for learning and testing. Experimental results were evaluated with respect to three attributes, namely detection rate, error rate, and accuracy. The respective values of detection rate, error rate, and accuracy are found to be 0.994, 0.011, and 0.998 for the flame, 0.978, 0.021, and 0.978 for the smoke, and the calculation time is found to be 0.009 s.