The Use of Artificial Intelligence Techniques for Signal Discrimination in Fire Detection Systems

1994 ◽  
Vol 6 (3) ◽  
pp. 125-136 ◽  
Author(s):  
B. J. Meacham
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.


Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.


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

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.


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.


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