• Fire Science and Engineering
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• v.31 no.2
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• pp.44-51
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• 2017

Accurate predictions of the activation time for smoke detectors using a fire simulation is are required to ensure the reliability of the RSET (Required Safe Egress Time) calculation in the process of PBD (Performance-Based Design). The objective of this study was to enhance the accuracy of input parameters for the numerical models of smoke detector based on the FDS. To this end, a Fire Detector Evaluator (FDE) developed in previous studies was improved. The uniformities of flow and smoke inside the FDE were improved and accurate measurements of the obscuration per meter (OPM) related to detector operation were also performed through a decrease in the forward scattering of smoke particles. The input parameters using the improved FDE showed a significant difference from the previous FDE quantitatively. In particular, a larger difference was found in a photoelectric detector compared to an ionization detector. Considering that the operating conditions of smoke detectors are affected by the detector type, combustibles, smoke particulars, and color, the database (DB) on the input parameters for various detectors and combustibles should be built to improve the reliability of PBD in future studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.235-238
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• 2020

In the smart home era, fire safety is very important for human life and facility safety. Casualties and property damage from the fire would be a huge national loss. In this paper, we propose to predict the risk by determining the operating time of the fire detector according to the fire in the smart home. Among IoT fire detectors, heat detectors and smoke detectors, the risk can be predicted due to the difference in the operating time depending on the fire. Based on the results of this experiment, the ion-type smoke detector shows very fast characteristics, so it would be good to use the results in future fire prevention facility.

• Smart Media Journal
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• v.13 no.4
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• pp.23-32
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• 2024

Large-scale fires and their consequential damages are becoming increasingly common, but confidence in fire detection systems is waning. Recently, widely-used chemical fire detectors frequently generate lots of false alarms, while video-based deep learning fire detection is hampered by its time-consuming and expensive nature. To tackle these issues, this study proposes a fire detection model utilizing an autoencoder approach. The objective is to minimize false alarms while achieving swift and precise fire detection. The proposed model, employing an autoencoder methodology, can exclusively learn from normal data without the need for fire-related data, thus enhancing its adaptability to diverse environments. By amalgamating data from five distinct sensors, it facilitates rapid and accurate fire detection. Through experiments with various hyperparameter combinations, the proposed model demonstrated that out of 14 scenarios, only one encountered false alarm issues. Experimental results underscore its potential to curtail fire-related losses and bolster the reliability of fire detection systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.343-345
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• 2020

사물인터넷 시대에 스마트 홈들의 출현으로 엣지 지역에서의 정확한 화재 감지 및 대응 기술에 대한 필요성이 요구되고 있다. 그러나, 화재 감지 동작에서 오작동으로 인한 잘못된 정보를 제공하거나, 감지 기기로부터의 불충분한 정보 제공은 화재 감지/예방을 효과적으로 수행하기 어렵게 한다. 본 논문에서는 사물인터넷 엣지 지역에서 기존의 다양한 센서들을 통해 데이터를 수집하여 온톨로지에서 추론과정을 진행하는 엣지형 온톨로지 시스템을 구현한다. 본 논문에서는 추론된 정보를 바탕으로 사물인터넷 엣지에서 발생되는 화재 사건에 대한 정확한 판단할 수 있게 한다. 제안된 화재 추론 온톨로지를 이용해 기존의 화재감지기가 제공하는 정보 뿐 만 아니라 화재 정보에 대한 확장된 정보를 사용자에게 제공하므로 사용자 유용성을 개선될 수 있음을 보인다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.427-428
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• 2014

화재발생 시 인명 안전을 위하여 초기의 화재감지가 매우 중요한 요인이다. 기존 연기감지기의 경우 일정한 조건만 맞으면 작동하기 때문에 비화재보의 우려가 높다. 특히 차량 매연도 연료가 연소되어 나오는 연기이므로 차량정체 시 트럭 등에서 발생하는 심한 매연에도 반응하여 오작동의 가능성이 높다. 이러한 기존 화재감지기의 문제점을 해결하기 위해 다양한 IR 레이저 기반 연기검출장치가 활용되고 있다. 하지만 IR 레이저 기반 연기검출장치는 100m 거리가 이격된 레이저 발광부와 수광부 구조에 따라 발광부 레이저광선의 각도변화에 따른 수광부 레이저 포인트 위치가 매우 민감하게 변화함에 따라 초기 레이저 포인트의 초점을 정확히 정합하고, 이후 보정하기 위한 고정밀 자동 초점 정합장치가 필요하다. 이에 본 논문에서는 레이저 투광부와 수광부를 분리하여 레이저 송신기, 수신기로 구성되는 고정밀 자동초점 정합장치를 설계하고자 한다.

• Fire Science and Engineering
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• v.17 no.4
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• pp.86-91
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• 2003

Since the air stream in the room controlled by HVAC system affects on he expected response of conventional detectors which are designed in accordance with normal characteristics of air stream in the fire incident, unexpected operation time delay may occur in fire. In order to solve this problem and to improve sensitivity so that to initiate fire in its early stages for minimizing damage and protecting people, we studied and developed Air Sampling Smoke Detector. The Air Sampling Smoke Detector is a kind of active-type fire detection system. it draws air continuously from the protected area through an air sampling pipe network to the smoke density analyzer. This study presents smoke density analysing technique and air intake balancing technique through an air sampling pipe network. As a result of evaluating, Air Sampling Smoke Detector was much more sensitive than conventional smoke detectors that passively wait for smoke to reach them and was not affected by ambient airflow in the room by means of balanced air intake through the sampling holes.

• Fire Science and Engineering
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• v.24 no.1
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• pp.15-23
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• 2010

Placement method for fire detectors prescribed in current fire safety regulation is just about placing a prescribed number of detectors according to the areas. However, this regulation has no scientific basis and standards from foreign countries are just introduced and fire detectors are installed complying with them. There are two standards in designing fire protection systems; Prescriptive-Based Design that follows stipulated regulations like fire safety standards and Performance-Based Design based on engineering knowledge such as fire dynamics, structural dynamics, mechanics of materials, fluid mechanics, and thermo dynamics. Recently, Fire Protection System Construction Business Act was revised so that fire protection systems can be designed using Performance-Based Design method ('05. 8. 4), though the method has not activated until now. In addition, the enforcement decree defines the range for specific objects of fire protection to which Performance-Based Design is applied ('07, 1. 24). At the moment, by manufacturing simulator so that formulas can be introduced and calculated with software in order to install fire detector of automatic fire detection systems keeping optimized distance, comparing the results with the state of fire detector placed according to Performance-Based Design and analyzing them, this study was intended to settle Performance-Based Design method in the future.

• 방재와보험
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• s.53
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• pp.28-30
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• 1992

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.49-54
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• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• 재활복지
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• v.21 no.3
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• pp.129-146
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• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.