• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.110-116
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• 2024

Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.504-514
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• 2009

Recently, fire monitoring application systems have been an active research area due to the safety of industries, historical monuments and so on. The fire monitoring application systems can reduce the damage of properties by providing earlier warning for possible fire situation. However, the existing systems have a drawback that they detect fire with delay due to their uniform epoch in fire detection algorithm. Moreover, they do not provide user-friendly graphical user interfaces in their fire monitoring systems. To resolve the problems, First, we propose a new fire detection algorithm (Early Fire Detection Fire Algorithm) which uses the distribution of sensing data for early fire detection. Our fire detection algorithm is better in terms of fire detection time than the existing work because it can set a start time of fire detection epoch dynamically based on data distribution. Second, we develop a fire monitoring application system which provides users with both a user-friendly graphical user interface and a fire alarm message when fire occurs. Finally, we show from our experiment that our developed system is effectively used for early fire warning in a variety of fire situations.

• Journal of the Korean Society of Safety
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• v.32 no.2
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• pp.38-45
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• 2017

This paper is an experimental study to find an optimal detection method for detecting fire early in a rack-type warehouse stored with goods. In this study, we constructed rack-type structure with the fourth floor of 13.5 m high and conducted fire experiments which were to measure flow of heat/smoke in rack-type structure and response time of fire detectors. The detectors used at experiments were fixed temperature type detectors, rate of rise detectors, photoelectric smoke detectors, air sampling smoke detectors and flame detectors. The used ignition sources are n-heptane fire for response of heat detection and cotton fire for response of smoke detection. The fixed temperature type detectors, rate of rise detectors and photoelectric detectors were installed to every rack level respectively. The results show that the rate of rise detector should be installed every 2 levels and photoelectric smoke detector should be installed every 4 levels for the early stage fire detection. Air sampling smoke detectors can detect fire early in response to control of sensitivity, but there is a problem in false alarm. The fixed temperature detector is not suitable for early stage fire detection in warehouse and flame detector not worked if flame is not visible, so it need to install combination with other detector.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.9
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• pp.847-854
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• 2014

The paper presented a method of early fire detection based on the environmental characteristics inside the nacelle of wind turbine generator system(WTGS). The rising rates of the temperature and smoke density were used as the parameters for early fire detection. By considering the characteristics of temperature and smoke density of a nacelle, this method is very reliable and can minimize the possibility of a malfunction of fire detection. The performance of the method was tested through sets of experiments by using nacelle simulator.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.135-138
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• 2008

Many Victims and property damage are caused in fires. In this paper, an flame detection algorithm is proposed to early alarm fires. The proposed flame detection algorithm is based on 2-stage decision strategy of video processing. The first decision is to check with color distribution of input vidoe. In the second, the candidated region is settled as fire region with activity. As a result of simulation, it is shown that the proposed algorithm is useful for fire recognition.

• Fire Science and Engineering
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• v.33 no.5
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• pp.55-60
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• 2019

The occurrence of fire in rack-type warehouses may either lead to the warehouses getting entirely burned up or collapsing. This can be attrubuted to the high height of rack-type warehouses, in which combustibles are generally vertically stacked. These characteristics make it difficult to detect a fire early; because detectors are installed on the ceiling, these fires cannot be extinguished at an early stage. In this study, the flow of heat and smoke generated by a fire in a rack-type warehouse was analyzed using a fire dynamic simulator. Through this analysis, the optimal installation conditions of fire detectors for the early detection of fire in rack-type warehouses were confirmed. The analysis results confirmed that complex detection of heat and smoke is required for the early detection of fire in rack type warehouses. Furthermore, it was found that fixed temperature detectors are not suitable for these warehouses, resulting in the need to install heat-smoke hybrid detectors at every three rack levels.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.12
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• pp.935-943
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• 2015

This paper presents a method for early fire detection and fire position determination inside the nacelle of wind turbine generator system. The rising temperature and obscuration rates inside the nacelle were used as parameters for fire detection, which can minimize the possibility of a fire detection malfunction because these rising rates do not depend on the absolute values of temperature and obscuration. The fire position was determined using the time difference among various sensor positions for fire detection. The performance of the method was tasted using sets of experiments in a nacelle simulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1734-1737
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• 1997

The conventional fire detection devices are operated after a processed fire phase, which are sensing only a high density of somke level or high temperature heat. They are not so precision to detect a fire in the early phase to protect the facility form the fire. We need to develope a new high precision smoke detection system to keep expensive industrial facilities most reliably form fire. A new optical precision smoke detection system was developed. It monitors very low level density of smoke particles in the air. It is operated continously through many years without a stop or any malfunction. The developed precision smoke detection system will be installed in important industrial facilities, such as power plants, underground common tunnel, main control rooms, computer rooms etc.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2164.1_2165.1
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• 2009

In this study we suggest fire detection algorithm using fuzzy inference with input variables of temperature and smoke density to detect electrical fire of early stage. The algorithm consists of membership function of temperature and smoke density and fire probability. The antecedent part of the algorithm consists of temperature and smoke density, and the consequent part consists of fire possibility. The inference rules of the algorithm is estimated to input temperature and smoke density obtained by real fire. With the help of algorithms using fuzzy inference we may be diagnose electrical fire precisely.