• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.737-746
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• 2009

In this study, we have developed one chip system IC for preventing the overload, detecting an abnormal conditions, and controlling the fire prevention in the intelligent home appliances. For the purpose, a circuit detectable an electric leak for preventing an electric shock, and a circuit detectable arc that has effect directly on the fire are designed. The circuits designed on every block are verified by comparing simulation with bread-boarding using a standard transistors. The system IC is fabricated by using 34 V 2 metal $1.5{\mu}m$ bipolar transistor process from evaluation results. The electrical performances of IC application circuits and the system IC equipped on PCB board are evaluated. It is confirmed that the system IC is well operated for arc and ground fault(GF) signal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.499-500
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• 2014

화재발생 시 인명 안전을 위하여 초기의 화재감지가 매우 중요한 요인이다. 특히 도로터널, 지하철역사 등 광범위 폐쇄공간에서 연기에 의한 질식사 등 2차 피해의 발생위험이 높다. 이에 최근 광범위 공간에서 적외선 레이저를 활용한 원거리 연기검출 화재탐지기에 대한 연구가 진행되고 있다. 이러한 레이저 기반 원거리 연기검출장치는 이격(100m 이상) 설치되는 레이저 발신기와 수신기의 레이저 포인트가 정확히 정합되어야 한다. 아울러, 레이저 발신기와 수신기 사이의 거리에 비례하여 레이저 초점의 이동거리가 매우 민감하게 변화하므로 이를 정확히 정합하기 위한 고정밀 제어장치가 필요하다. 따라서 본 논문에서는 복수개의 레이저 발신모듈과 복수개의 조도센서(CdS)를 통해 초점의 정합을 자동 추적할 수 있는 알고리즘을 설계, 구현하였다. 이는 초기 레이저 초점의 설정과 이후 외부환경에 따른 초점의 틀어짐을 자동 보정하여 다양한 레이저 인식 장치에 적용될 것으로 사료된다.

• Journal of IKEEE
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• v.22 no.4
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• pp.1079-1087
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• 2018

Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Fire Science and Engineering
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• v.26 no.6
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• pp.24-30
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• 2012

This study is on the method of the detection for flame and series arc which can be happened at poor contact point added a vibration in part of contact point of low voltage line. In general, the causes of electric fire are over current, short circuit, poor contact, ect. The over-current or short circuit among those causes is detected by measuring a instant current value, but poor contact is difficult to detect by measuring a excessive value of the voltage and current and a distortion of waveforms. And therefore, in this paper, it is studied on the optimal technique of the arc judgement using fuzzy logic and MDET (Multi Dimension Estimation Technique). And it carries out the simulation for arc detection and the experiment for controller and load test. In result, the controller and detection algoristhm, is classified with normal wave and abnormal arc wave without relation with each loads and so the controller can detect a series arc successfully.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.917-922
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• 2013

In this paper, we propose a smoke detection system in order to detect a fire in a wide space, such as tunnel, airports using infra-red and visible laser. The proposed smoke detection system is composed of infra-red laser transmitter and receiver, visible laser and Zigbee wireless communication network. A visible laser is used to match transmitter and receiver and Zigbee network is utilized to propagate warnings of fire. If smoke is appeared between transmitter and receiver, received signals are decreased and it can be considered as occurring smoke. As IR laser transmitter and receiver are separated by long distance, it is difficult to match due to large variations caused by small change of direction. In this paper, it is proposed to match effectively using visible laser. When smoke is detected, warning informations are propagated by Zigbee network in the developed smoke detection system.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.115-122
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• 2018

More than 70% of the electric fire cases happen in this country are caused by arc. This paper aims to prepare measures of preventing electric fires resulted from arc by analyzing the technical standards and related regulations on the existing circuit breaker. Based on the study, we concluded that the technical and legal standards of the arc detector should be separately regulated from those of existing circuit breaker, considering the characteristics of arc detectors. Accordingly, we suggested in this paper that the arc detector related regulations should either be added to the existing circuit breaker related regulations, or to be handled separately. For effective prevention of electric fires caused by arc, it is urgently required to install arc detector and it is, therefore, appropriate to make it mandatory to install an arc detector. Under the given circumstance, it is suggested that the arc detector related regulations should be added to the existing regulations for the circuit breaker. The technical standards of arc detectors should reflect the arc judgement specification, breaking threshold of arc and the range of unnecessary signal that breaker should not react to respond.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.111-116
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• 2019

The fire detectors used in the engine rooms of ships offer only a slow response to emergencies because smoke or heat must reach detectors installed on ceilings, but the air flow in engine rooms can be very fluid depending on the use of equipment. In order to overcome these disadvantages, much research on video-based fire detection has been conducted in recent years. Video-based fire detection is effective for initial detection of fire because it is not affected by air flow and transmission speed is fast. In this paper, experiments were performed using images of smoke from a smoke generator in an engine room. Data generated using LBP and GLCM operators that extract the textural features of smoke was classified using SVM, which is a machine learning classifier. Even if smoke did not rise to the ceiling, where detectors were installed, smoke detection was confirmed using the image-based technique.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.85-90
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• 2013

Currently, the electric fire prediction system uses PIC(Peripheral Interface Controller) for controller microprocessor. PIC has a slower computing speed than DSP does, so its real-time computing ability is inadequate. So with the basic characteristics waveform during arc generation as the standard reference, the comparison to this reference is used to predict and alarm electric fire from arc. While such alarm can be detected and taken care of from a remote central server, that prediction error rate is high and remote control in mobile environment is not available. In this article, the arc detection of time domain and frequency domain and wavelet-based adaptation algorithm executing the adaptation algorithm in conversion domain were applied to develop an electric fire prediction system loaded with new real-time arc detection algorithm using DSP. Also, remote control was made available through iPhone environment-based app development which enabled remote monitoring for arc's electric signal and power quality, and its utility was verified.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2009.04a
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• pp.358-364
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• 2009

본 연구에서는 우리나라 주요 침엽수종인 소나무(Pinus densiflora)와 활엽수종인 굴참나무(Quercus variabilis)의 낙엽에 대해 FTIR(Fourier Transform Infrared) 분광계를 이용하여 배출 연소가스 종류 및 농도를 측정하였다. 실험결과 소나무와 굴참나무 낙엽에서 Carbon monoxide, Carbon dioxide, Acetic acid, Butyl acetate, Ethylene, Methane, Methanol, Nitrogen dioxide, Ammonia, Hydrogen Fluoride, Sulfur dioxide, Hydrogen bromide 등 13개 연소가스가 검출되었고 굴참나무 낙엽에서는 Nitrogen monoxide가 추가로 검출되었다. 방출된 연소가스의 전체 농도는 소나무 낙엽이 굴참나무 낙엽에 비해 4.5배 많이 검출되었다. 특히, 시간가중평균가스농도(TWA : Time-weighted average, ppm) 기준을 초과하는 연소가스는 Carbon monoxide, Carbon dioxide, Butyl acetate가 검출되었고 단시간노출기준(STEL : Short Term Exposure Limit, ppm) 기준을 초과하는 연소가스는 Carbon monoxide, Carbon dioxide로 소나무 및 굴참나무 모두에서 나타났다. 이에 산불에서의 낙엽층 지표화 연소시 전체 가스 방출량의 99% 이상을 차지하고 있는 Carbon monoxide, Carbon dioxide의 건강 위험성이 높은 것으로 나타났다. 하지만, 검출된 다른 건강 위험성 가스의 경우에도 연소물질의 양이 증가할수록 연소가스의 농도가 높아져 건강안정성에 해가 있을 것으로 판단되며 또한 검출된 연소가스 중 나무의 주요구성 원소가 아닌 Bromide, Fluoride 화합물에 대해서는 토양으로부터의 오염 또는 분석과정에서의 노이즈로 인한 검출 등에 대한 보다 면밀한 검토가 필요할 것으로 판단된다.