• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Journal of the Korea Institute of Building Construction
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• v.24 no.2
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• pp.193-202
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• 2024

This research delves into the evaluation of the suitability of ultrasonic pulse velocity as a diagnostic tool for early detection of frost damage in concrete. The investigation involves the measurement of compressive strength and ultrasonic pulse velocity concerning the depth of freezing for individual mortar specimens, followed by an analysis of their microstructure and their interrelation. The findings indicate a consistent decrease in both compressive strength and ultrasonic pulse velocity with increasing freezing depth. Furthermore, a correlation between compressive strength and ultrasonic pulse velocity concerning the depth of early frost damage is established. Consequently, the study asserts the potential of utilizing the ultrasonic pulse velocity method for early detection of frost damage in concrete, with prospects for quantifying the depth of damage through further research endeavors.

• Fire Science and Engineering
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• v.30 no.5
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• pp.100-107
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• 2016

Today, high-rise buildings expected to meet various needs and improve the quality of frequency of fire and the potential risks are increasing. In particular, the fire spread risk in the vertical direction is increasing. As a result there is a problem with delays in the evacuation time of occupants. To overcome this problem, there is a need to consider the structure of the building and develop a system for the early detection of fire by applying a fire alarm system according to the risk ranking. Therefore, this paper describes the vertical fire spread characteristics of a multistory double-skin and stairs structure with risk. The data were compared with that from the national and international fire alarms as well as with. smoke density, smoke detectors, visibility, and CO concentration using FDS. A fire alarm system for each structure is proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• Fire Science and Engineering
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• v.32 no.5
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• pp.40-45
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• 2018

This paper is an analysis of the influence of ceiling air conditioner airflow on fire detector response. In order to analyze the response characteristics of fire detector while forming air flow of a ceiling-type air conditioner, fire tests were carried out in accordance with ISO standard. This experiment was carried out in a fire test site of 10 m (width) ${\times}$ 7 m (length) ${\times}$ 4 m (height). As a result of the experiment, the response of fire detector shows a normal pattern that is delayed as the distance from the fire source is increased in the absence of the air conditioner, but it is confirmed that the pattern is not maintained in the strong air flow. When the air flow of air conditioner was strong, the response time was increased by 121% in the smoke detector and by 39% in the heat detector. In the case of ceiling type air conditioners, it is considered that the number of fire detectors should be increased, or a detector with high sensitivity should be installed for early detection of fire.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.521-525
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• 2009

In this paper, we introduce fire detection system and software learning algorithm that recognize fire patterns. Flame patterns means that periodical and consistent pattern about general conception of fire, and to process it with the definition. Learning algorithm for flame pattern recognition that we propose is the method which is faster and more exactly than existing algorithm. Also, we trying to elicit the method through experiment result and by applying it, we show the validity of an early fire warning system.

• Fire Science and Engineering
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• v.22 no.4
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• pp.61-64
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• 2008

This paper studied on the response characteristics depending on service life of ionization smoke detector. The experimental samples used ionization smoke detectors (360 EA) for over 5 years which were influenced by environment set up with fire objects. The experimental method performed operate and non-operate test according to 'Type Approval and Technical Regulation of Detector (KOFEIS 0301)', for estimate the response characteristics of ionization smoke detector depending on service life. The results showed that their response characteristics were rapidly decreasing when the longer their using period. Accordingly, it is desirable that ionization smoke detector has to be changed for early fire detection when passed their service life.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.26-31
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• 2014

In this paper, we present performance analysis of video smoke detection based on BPN-Network that is using multi-smoke feature, and Neural Network. Conventional smoke detection method consist of simple or mixed functions using color, temporal, spatial characteristics. However, most of all, they don't consider the early fire conditions. In this paper, we analysis the smoke color and motion characteristics, and revised distinguish the candidate smoke region. Smoke diffusion, transparency and shape features are used for detection stage. Then it apply the BPN-Network (Back-Propagation Neural Network). The simulation results showed 91.31% accuracy and 2.62% of false detection rate.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.75-93
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• 2007

This paper presents real-time online early fire warning systems developed for preserving cultural properties of Bulguksa which is a world heritage designated by UNESCO. The system is based on the ubiquitous sensor network employing 900MHz and 2.4GHz bands. In this paper, we analyze requirements that should be considered in building effective management systems of cultural heritages by using wireless sensor network. Finally, we introduce the architecture, sensor and network design, and software design of the fire warning systems which is an initial version of U-Bulguksa. The current version of systems has been operating in Bukguksa for a few months. U-Bukguksa project sponsored by National Information Society Agency is ultimately aimed at developing an integrated system of U-cultural heritage management and U-tourism. The former aims to conserve and manage intangible cultural properties by providing a variety of environmental information such as erosion, crack, and gradient as well as fire which are important causes of loss and damage in real-time and online. The latter refers to the intelligent tourism information and guidance systems allowing tourists to get the personalized content on cultural heritages and help guidance with mobile devices in Bulguksa.