• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.70.2-70.2
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• 2010

본 연구는 최근 발생한 숭례문 화재와 같은 목조문화재의 화재를 초기에 인지하고 확인하는 차원에서 고안된 시스템으로써 불꽃감지기와 같은 초기 화재 감지시스템의 오작동 여부를 확인하여 화재감지기의 오작동으로 인한 경제적 시간적인 손실을 예방하고 목조문화재를 화재로부터 보호하기 위한 시스템이다. 초기에 화재를 감지하는 불꽃감지기는 현재 목조문화재뿐만 아니라 다양한 곳에서 활용되고 있지만 감지기의 오작동 및 오류를 확인하는 시스템은 실제로 실효성 등의 문제로 인해 활용이 미비한 실정이다. 본 연구에서는 유비쿼터스 센서 네트워크(USN) 기술, 불꽃감지기, 이미지 센서, USN 기반 문화재 방재 응용사례, 오작동 확인시스템 구현 등에 대해서 살펴보고 유비쿼터스형 문화재 방재시스템을 제시해 본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.453-456
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• 2013

최근 스마트폰 보급률 증가 및 무선 센서 네트워크(Wireless Sensor Networks) 기술 발전에 따라 해당 기술을 화재감시, 침입 감지와 같은 응용에 융합하는 연구가 활발히 진행되고 있다. 하지만 기존 연구들은 주기를 기반으로 감지를 수행하기 때문에 화재 및 침입 판단이 지연되는 문제점이 존재한다. 이를 위해, 본 논문에서는 판단 주기를 동적으로 설정하는 조기 화재 판단 알고리즘을 통해 화재 판단 시간을 단축시켜 빠른 대처를 할 수 있도록 지원하는 새로운 화재감시 및 침입 감지 시스템을 개발한다. 아울러, 적외선 센서를 이용하여 무단 침입을 감지함으로써 도난 및 파손과 함께 방화로 인한 화재를 방지할 수 있다. 마지막으로 성능평가를 통해 제안하는 시스템이 화재 판단 측면에서 기존 연구보다 우수함을 입증한다.

• 방재와보험
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• s.115
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• pp.28-33
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• 2006

공장이나 일반 건물에서 과전류 및 기열에 의해 절연물이 응용되고 유독 가스를 배출하여 화재의 초기 단계로 발전하는 경우가 많다. 이같은 사고를 예방하기 위한 향 검지기와 향 캡슐을 조합한 이상열 감지 시스템 'CAN 열향'의 원리 및 현장 적용 예를 알아본다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.391-394
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• 2023

일반적으로 화재 감지 시스템은 정확하고 빠르게 화재를 감지하는 것은 어려운 문제 중 하나이다. 본 논문에서는 U-net을 활용하여 기존의 화재(불) 영역 추출 기법으로 Segmentation으로 보다 정밀하게 탐지하는 기법을 제안한다. 이 기법은 화재 이미지에서 연기제거 및 색상보정을 통해 이미지를 전처리하여 화염 영역을 추출한 뒤 U-Net으로 학습시켜 이미지를 입력하면 불 영역의 Segmentation을 추출하도록 한다.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.93-96
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• 2015

In this paper, we introduce a fire detection system using context-aware sensor. In existing weather and based on vision sensor of fire detection system case, acquired image through sensor of camera is extracting features about fire range as processing to convert HSI(Hue, Saturation, Intensity) model HSI which is color space can have durability in illumination changes. However, in this case, until a fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. Additionally, the fire detection in complex situations as well as difficult to separate continuous boundary is set for the required area is difficult. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire it. In addition, it is possible to differential management to intensive fire detection is required zone dividing the state of fire.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.712-721
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• 2010

This paper proposes a new fire-flame detection method using probabilistic membership function of visual features and Fuzzy Finite Automata (FFA). First, moving regions are detected by analyzing the background subtraction and candidate flame regions then identified by applying flame color models. Since flame regions generally have continuous and an irregular pattern continuously, membership functions of variance of intensity, wavelet energy and motion orientation are generated and applied to FFA. Since FFA combines the capabilities of automata with fuzzy logic, it not only provides a systemic approach to handle uncertainty in computational systems, but also can handle continuous spaces. The proposed algorithm is successfully applied to various fire videos and shows a better detection performance when compared with other methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.276-279
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• 2009

Is introducing ship automation system for the safety of ship and work environment mend recently. Is endeavoring for sea safety and fire at sea prevention solidifying control of standard technology and safety supervision aspect in IMO but sea accident and ship fire are happening continuously. Because using Robot in artistic talent of ship in this treatise, studied that correspond to Risk and manage. Attach fire perception sensor for Robot's Risk confrontation, and because using infrared rays sensor, TOUCH SWITCH, sound perception sensor, gas perception sensor, light perception sensor that is threaded in Robot and is achieved, controlled Robot, and establish Low-High value the speed of sound output use and DC MOTOR and COM SEN of when indicate Risk confrontation to Robot and establish Robot's Risk confrontation administration action.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.