• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1371-1376
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• 2019

In case of fire in a structure, it is difficult to suppress fire because it can not accurately grasp the location of fire in case of fire. In this paper, we propose a system algorithm that can guide the optimal evacuation route in case of deep learning-based (RNN) structure disaster. The present invention provides a service to transmit data detected by sensors to a server in real time by using installed sensor, to transmit and analyze information such as temperature, heat, smoke, toxic gas around the sensor, to identify the safest moving path within a set threshold, to transmit information to LED guide lights and direction indicators in a structure in real time to avoid risk factors. This is because the information of temperature, heat, smoke, and toxic gas in each area of the structure can be grasped, and it is considered that the optimal evacuation route can be guided in case of structure disaster.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.617-623
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• 2023

Among the fire incidents in Korea over the past decade, building fires are the most common, and property and human casualties are the most common. However, the existing fire fighting system does not only inform the location of emergency exits and guide safe routes to help casualties evacuate smoothly. A system was proposed to help successful evacuation by distinguishing vertical and horizontal characteristics using spatial characteristics. In this study, an effective evacuation system was proposed by predicting fires using temperature detection sensors and smoke sensor values, and calculating the optimal evacuation path through the Dijkstra algorithm.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.236-244
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• 2021

Purpose: The purpose of this study is to develop IoT multi-fire detectors combined smoke/carbon monoxide/heat and wireless IoT communication and to confirm the detect performance by smoke generator fire test and cotton wicks fire test. Method: The IoT multi-fire detector combined smoke and CO and combined smoke and heat were experimented the detect performance by smoke generator test and fire test of cotton wicks. And the case of fire alarm was checked. Result: The IoT multi-fire detector combined smoke and CO rung the alarm at the fire test of cotton wicks, did not ring the alarm at the smoke generator test. In comparison, the IoT multi-fire detector combined smoke and heat did not ring the alarm both at the smoke generator test and the fire test of cotton wicks. Conclusion: The IoT multi-fire detector combined smoke and CO detected the only smoke including the carbon monoxide and the IoT multi-fire detector combined smoke and heat did not ring the alarm for lack of heat. As a result, when the developed IoT multi-fire detector was detected the signal more than the set point, the fire alarm was sounded through cotton wicks fire test and smoke generator.

• Fire Science and Engineering
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• v.28 no.2
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• pp.9-13
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• 2014

This paper is basic study for development of the photoelectric-type smoke detector that is able to distinguish fire source as well as fire detection. For this subject, Light source and sensor which is normally used for the conventional smoke detector are assembled for the optical chamber. Using 3 type of the test fires (the paper fire, the wood fire, the flammable liquid fire) this paper attempts to find optical properties of each fire. These 3 type of fire are used in the testing of smoke detector according to UL 268 standard. As the result, there are disambiguated between the paper fire and the wood fire in scattering and reason of extinction in the flammable liquid fire is different from that of the paper and the wood fire.

• Fire Science and Engineering
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• v.23 no.5
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• pp.125-132
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• 2009

In this study we could contribute to the development of a precise checking tool with which the reliability of the automatic fire detection systems was enhanced and the safety of the people was ensured. In the same way as the domestic technical standard, the portable smoke sensitivity tester, which was developed in this work, could be used to check the capability of the smoke detectors installed in the field. Its heater inside was warmed up to $400^{\circ}C$ in 40 seconds and the paper as a smoke source was burned to produce smoke for the test. With the Photoelectric smoke detector it was possible to measure and control the smoke concentration in a range from 0%/m to 25%/m. With the adjustment of rpm of the fan, it was possible to keep a constant wind velocity in a range from 20cm/sec to 40cm/sec.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.495-498
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• 2009

유비쿼터스 시대에서 요구하는 가장 핵심적인 요소기술 즉 인프라이며 이를 이용한다면 다양한 시설물에서의 침입자에 대한 감시, RFID를 이용한 효율적인 인력배치에 응용이 가능하고 또한 온도, 조도, 연기 센서 등을 활용하면 실시간으로 화재 및 테러에 대해 조기에 인지하여 빠르게 대처할 수 있다. 본 논문에서는 USN이 적용되는 대상은 침입탐지를 감시해야 하는 구역, 보안감시 대상이 되는 구역, 위험물 관리와 같은 구역으로 분류하여 구역의 특성마다 센서와 카메라, Tag와 센서(Hybrid Reader)기술 등을 하나의 센서노드로 융합하여 관리대상에 이상유무가 있을 경우 빠르게 대처하거나, Tag와 센서(Hybrid Reader)기술을 이용한 효율적인 인력배치 및 관리할 수 있도록 USN을 구성한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.139-146
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• 2008

Wireless sensor network-related application system can perform functions such as environmental pollution monitoring, building control, home automation in future. In this paper, we present wireless sensor network based system for subway station in order to reduce the damage of the people and the subway station due to fire. Sensor nodes in this system can sense temperature, illumination, smoke, and human body in real time and detect the accident in the subway station. These real-time sensing and wireless networking minimize casualties and damage to property.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.629-634
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• 2016

In this study, we investigated the effect of factors that influence the measurement of smoke density using synthetic rubber flooring. The characteristics of rolling stock in an enclosed environment can cause enormous loss of life by smoke inhalation during fires inside passenger cars. The amount of smoke generation from interior materials for rolling stock is strictly restricted domestically and in other countries. Precise measurement of smoke density is therefore required to assess the fire performance of interior materials. Major factors that influence the measurement of smoke density include the uniformity of the specimen, the variations in conditions and instruments, and the operational and maintenance environment of the instruments. The contribution of factors was analyzed by estimating the uncertainty to investigate the contribution ratios of the major factors. The results show a contribution ratio of about 86% for the variation from the measurement of light transmission using a photomultiplier tube. Thus, this factor was the most representative for the measurement of smoke density. The contribution ratio of the other factors was low at about 11%, including irradiant flux conditions (${\pm}0.5 kW/m^2$) and the influence of the operational and maintenance environment of the instrument. These results were obtained using specimens with high uniformity.

• 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.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.70-74
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• 2015

In this paper, satellite communication based video surveillance system that consisted of ultra-small aperture terminals with small-size smart vision sensor is proposed. The events such as forest fire, smoke, intruder movement are detected automatically in field and false alarms are minimized by using intelligent and high-reliable video analysis algorithms. The smart vision sensor is necessary to achieve high-confidence, high hardware endurance, seamless communication and easy maintenance requirements. To satisfy these requirements, real-time digital signal processor, camera module and satellite transceiver are integrated as a smart vision sensor-based ultra-small aperture terminal. Also, high-performance video analysis and image coding algorithms are embedded. The video analysis functions and performances were verified and confirmed practicality through computer simulation and vision sensor prototype test.