scholarly journals Decision algorithms in fire detection systems

2011 ◽  
Vol 8 (2) ◽  
pp. 155-161 ◽  
Author(s):  
Jovan Ristic ◽  
Dragana Radosavljevic

Analogue (and addressable) fire detection systems enables a new quality in improving sensitivity to real fires and reducing susceptibility to nuisance alarm sources. Different decision algorithms types were developed with intention to improve sensitivity and reduce false alarm occurrence. At the beginning, it was free alarm level adjustment based on preset level. Majority of multi-criteria decision work was based on multi-sensor (multi-signature) decision algorithms - using different type of sensors on the same location or, rather, using different aspects (level and rise) of one sensor measured value. Our idea is to improve sensitivity and reduce false alarm occurrence by forming groups of sensors that work in similar conditions (same world side in the building, same or similar technology or working time). Original multi-criteria decision algorithms based on level, rise and difference of level and rise from group average are discussed in this paper.

2021 ◽  
pp. 118-123
Author(s):  
В.Н. Круглеевский ◽  
В.В. Вислогузов ◽  
A.A. Таранцев ◽  
С.Н. Турусов

В настоящей статье рассматриваются вопросы развития пожарных извещателей, контролирующих появление дыма, превышение заданного значения температуры и скорости ее роста, наличие угарного газа и использующих мультикритериальные алгоритмы для оценки обоснованности сигналов тревоги. Анализируются результаты проведенных отечественными организациями сравнительных испытаний мультикритериальных и традиционных «пороговых» пожарных извещателей и возможности их применения на судах в составе систем пожарной сигнализации. Определено, что при повторении одних и тех же модельных очагов пожаров зафиксированные значения контролируемых параметров отличались незначительно. При этом для каждого модельного очага можно было обнаружить свои характерные черты. Сделан вывод о том, что внедрение мультикритериальных алгоритмов обработки информации в судовые системы обнаружения пожаров не только сокращает время обнаружения пожара, но и позволяет расширить функциональные возможности системы. Используя мультикритериальные пожарные извещатели в системах пожарной сигнализации можно будет распознавать, что именно горит: дизельное топливо, ветошь, изоляция электрического кабеля или что-либо другое. Отмечается, что требования к судовым мультикритериальным системам сигнализации обнаружения пожара нашли свое отражение в Правилах классификации и постройки морских судов Российского морского регистра судоходства. This article discusses the development of fire detectors that control the appearance of smoke, the excess of a given temperature and the rate of its growth, the presence of carbon monoxide and use multicriteria algorithms to assess the validity of alarm signals. The results of comparative tests of multicriteria and traditional fire detectors conducted by domestic organizations and the possibility of their use on ships as part of fire alarm systems are analyzed. It was determined that when the same model fires were repeated, the recorded values of the controlled parameters differed slightly. At the same time, for each model focus, it was possible to detect its own characteristic features. It is concluded that the introduction of multicriteria algorithms for information processing in ship fire detection systems not only reduces the time of fire detection, but also allows you to expand the functionality of the system. Using multi-criteria fire detectors in fire alarm systems,it will be possible to recognize what exactly is burning: diesel fuel, rags, electrical cable insulation, or anything else. It is noted that the requirements for ship multicriteria fire detection alarm systems are reflected in the Rules for the Classification and Construction of Marine Vessels of the Russian Maritime Register of Shipping.


2012 ◽  
Vol 605-607 ◽  
pp. 1851-1854
Author(s):  
Yan Qiu Wang ◽  
Yan Wen Wang ◽  
Chun Mei Pei ◽  
Xiu Qing Yang ◽  
Hai Rong Ye

Characteristics of fire detection signal are proposed that, in fire case non-fire signals caused by other factors can not be separated from fire signals and in non-fire case non-fire signals may produce changes similar to fire signals. An intelligent algorithm is pointed out to reduce false alarm rate and miss alarm rate, it can improve fire alarm accuracy. The intelligent algorithm includes digital filter, sensitivity autoregulation, drift aotocompensation and rising rate analysis, and it is useful in practical engineering.


Author(s):  
Mumuh Muharam ◽  
Melda Latif ◽  
Baharuddin Baharuddin ◽  
Ibnum Richaflor

False alarm in fire detection can cause a huge loss. False alarm is generated by unwanted signal of smoke detector such as outdoor smoke or smoking. Therefore, it is designed a system that can reduce false alarm. The purposed system is built based on three components, those are sensors, actuators and data communication.  Sensors are smoke, flame and camera sensor. Smoke sensor is used as the first thing to sense a signal from the system that warns the system there is a fire. Flame sensor and camera are used to confirm that a signal of fire whether false alarm or not. Internet of Things (IoT) is applied to control the system. The result show that the system is applicable.


2001 ◽  
Vol 42 (1) ◽  
pp. 23-30 ◽  
Author(s):  
M.F. Ugarte ◽  
R.I. Zequeira ◽  
F. López

1997 ◽  
Vol 29 (2-3) ◽  
pp. 205-215 ◽  
Author(s):  
H. Fissan ◽  
E. Otto ◽  
J. Dixkens

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6519
Author(s):  
Akmalbek Abdusalomov ◽  
Nodirbek Baratov ◽  
Alpamis Kutlimuratov ◽  
Taeg Keun Whangbo

Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new special convolutional neural network was developed to detect fire regions using the existing YOLOv3 algorithm. Due to the fact that our real-time fire detector cameras were built on a Banana Pi M3 board, we adapted the YOLOv3 network to the board level. Firstly, we tested the latest versions of YOLO algorithms to select the appropriate algorithm and used it in our study for fire detection. The default versions of the YOLO approach have very low accuracy after training and testing in fire detection cases. We selected the YOLOv3 network to improve and use it for the successful detection and warning of fire disasters. By modifying the algorithm, we recorded the results of a rapid and high-precision detection of fire, during both day and night, irrespective of the shape and size. Another advantage is that the algorithm is capable of detecting fires that are 1 m long and 0.3 m wide at a distance of 50 m. Experimental results showed that the proposed method successfully detected fire candidate areas and achieved a seamless classification performance compared to other conventional fire detection frameworks.


2019 ◽  
Vol 101 (4) ◽  
pp. 616-630 ◽  
Author(s):  
Marcos A. Rangel ◽  
Tom S. Vogl

Fire has long served as a tool in agriculture, but the practice's link with economic activity has made its health consequences difficult to study. Drawing on data from satellite-based fire detection systems, air monitors, and vital records in Brazil, we study how in utero exposure to smoke from sugarcane harvest fires affects health at birth. Exploiting daily changes in fire location and wind direction for identification, we find that late-pregnancy smoke exposure decreases birthweight, gestational length, and in utero survival. Fires less associated with smoke exposure predict improved health, highlighting the importance of disentangling pollution from its economic correlates.


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