scholarly journals A Comparison and Analysis of Domestic and Foreign Standards Related to Unwanted Fire Alarms

2020 ◽  
Vol 34 (6) ◽  
pp. 104-113
Author(s):  
Han-Bit Choi ◽  
Euy-Hong Hwang ◽  
Sung-Eun Lee ◽  
Don-Mook Choi
Keyword(s):  
Safety Management ◽  
Detection System ◽  
Fire Detection ◽  
Smoke Detector ◽  
System Construction ◽  
Fire Alarm ◽  
Fire Detection System ◽  
Comparison And Analysis ◽  
Testing Standards ◽  
Korean Type

As unwanted fire alarms within the automatic fire detection system increase, fire-fighting power gets wasted. This is recognized as important because it causes a decrease in the evacuation reliability of the occupancy. Therefore, in order to develop measures to reduce unwanted fire alarms, foreign (United States and, United Kingdom) and domestic standards related to unwanted fire alarms were compared and analyzed in the present study. Through the analysis the problems with the standards were identified as uncertainty regarding the detecting space and radius of the smoke detector, the absence of a statistical database (DB) for unwanted fire alarms, the absence of a protocol for managing unwanted fire alarms, and the absence of testing standards for unwanted fire alarms based on scenarios. Consequently, it is proposed that to deal with the problems, it is necessary to ascertain the detecting space and radius of the smoke detector, to propose unwanted fire alarm protection codes for database (DB) system construction, to supplement the fire safety management log by developing related manuals, and to develop unwanted fire alarm testing standards of Korean type based on scenarios.

Design of a Real-Time Video-Fire-Detection System Based on DSP

2011 ◽  
Vol 52-54 ◽  
pp. 1137-1141
Author(s):  
W. Yuan ◽  
J. Li ◽  
J. Fang ◽  
H.B. Hu ◽  
Y.M. Zhang
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Fire Detection ◽  
Circuit Board ◽  
Open Spaces ◽  
Fire Alarm ◽  
Video Acquisition ◽  
Portable System ◽  
Fire Detection System ◽  
Indoor And Outdoor

Many methods of fire detection in video have been carried out on PC. Generally it needs long cables to transmit images and processes videos on computers. That system is too dependent on computers, and costs a lot using cables to transmission. We would rather use a portable device to detect fire, which will bring us much more convenience. This paper designed a portable system to detect fire using DSP. The system integrates video acquisition, image processing and fire alarm together in a circuit board. Then we develop algorithms to recognize fire, and transplant them into DSP. Finally put it into use after commissioning test. This portable system can work independently. Users can put into their requirements through user-interface, and system will output the fire pictures and alarm by wireless communication or internet. This system is applicable to various places, such as indoor and outdoor, large and open spaces, forest, etc.

LoRa network based high performance forest fire detection system

2021 ◽  
Author(s):  
K Ram Prasanna ◽  
J.M. Mathana ◽  
T. Anne Ramya ◽  
R. Nirmala
Keyword(s):  
Forest Fire ◽  
High Performance ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System ◽  
Forest Fire Detection

scholarly journals Fire Detection System Using Machine Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012209
Author(s):  
A Arul ◽  
R S Hari Prakaash ◽  
R Gokul Raja ◽  
V Nandhalal ◽  
N Sathish Kumar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System

scholarly journals A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fan Wang ◽  
Xiao Jiang ◽  
Xiao Peng Hu
Keyword(s):  
Data Transfer ◽  
Detection System ◽  
Gaussian Model ◽  
Fire Detection ◽  
Distribution Model ◽  
Memory Bandwidth ◽  
Limited Memory ◽  
Background Removal ◽  
Fire Detection System ◽  
Gaussian Distribution Model

This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

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