Fire dynamics in the attics

2021 ◽  
pp. 65-72
Author(s):  
Евгений Юрьевич Полищук
Keyword(s):  
Detection System ◽  
Fire Department ◽  
Fire Detection ◽  
High Rate ◽  
Federal Law ◽  
Fire Dynamics ◽  
Fire Propagation ◽  
Safety Requirements ◽  
Long Time ◽  
Combustible Materials

Произведена экспериментальная оценка развития пожаров в объеме чердачных помещений в зависимости от типа кровельного покрытия. По результатам исследования отмечается, что применение кровли из горючих материалов способствует более быстрому образованию сквозных прогаров и выходу пламени за пределы помещения, в то время как в случае использования сплошных настилов из негорючих материалов горение в течение длительного времени развивается в объеме, а после появления общей вспышки быстро распространяется от очага в объеме. Представлены рекомендации по осуществлению мероприятий в области обеспечения требований Федерального закона в части ограничений последствий пожара. The article presents the results of an experimental assessment of fire development dynamics in the volume of attics, depending on the type of roofing. According to the results of the study, it is noted that the use of a roof made of combustible materials contributes to the faster formation of through burnouts and the exit of the flame outside the room, when solid decking made of non-combustible materials is used, fire develops for a long time in the volume, and after the formation of a general flash it quickly spreads from the hearth all over the volume. Open fire time over a roof made of combustible materials is more than twice less than when testing a roof with a solid flooring made of non-combustible materials and makes 5.5 minutes, compared to 13.5 minutes. In a real fire with a large amount of fire load in the volume of the attic the temporary gap may be sufficient to ensure that the fire, in the absence or failure of the fire detection system, covers the entire construction volume before it is detected by random witnesses and information about it is received by the fire department. When using a roof system made of combustible materials, it is noted that despite the high rate of initial fire development, the dynamics of subsequent fire propagation in the roof volume is lower, even in the absence of fire-resistant treatment of wooden structures. Based on the results of the study there are presented recommendations for the implementation of measures to meet the requirements of the Federal Law No. 123-FZ dated 22.07.2008 “Technical Regulations on Fire safety requirements” in terms of limiting the fire consequences.

scholarly journals A smart fire detection system using iot technology with automatic water sprinkler

2021 ◽  
Vol 11 (4) ◽  
pp. 2994
Author(s):  
Hamood Alqourabah ◽  
Amgad Muneer ◽  
Suliman Mohamed Fati
Keyword(s):  
Data Exchange ◽  
Emergency Services ◽  
Detection System ◽  
Real Life ◽  
Fire Department ◽  
Fire Detection ◽  
False Alarms ◽  
Real Life Data ◽  
Long Time ◽  
Fire Detection System

House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable.

Smart Fire Detection System with Early Notifications Using Machine Learning

2017 ◽  
Vol 16 (02) ◽  
pp. 1750009 ◽  
Author(s):  
Mohammad Sultan Mahmud ◽  
Md. Shohidul Islam ◽  
Md. Ashiqur Rahman
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Department ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
False Alarms ◽  
Police Station ◽  
Local Police ◽  
Long Time ◽  
Fire Detection System

House fire is one of the major concerns for designers, builders, and residents of property. In the case of detecting fire, individual sensors have been used for a long time, but they cannot detect the level of fire and notify the emergency response units. To solve this problem, this study attempts to propose an intelligent early fire detection system that would not only detect the fire by using integrated sensors but also notify the appropriate authorities including fire department, ambulance services, and local police station simultaneously to protect valuable lives and properties. Signals from the integrated detectors e.g., heat, smoke, and flame go through the machine learning algorithms to check the potentiality of the fire as well as broadcast the predicted result to various parties using a GSM modem. To consolidate the predicted output, structured forest for fast edge detection has also been applied. The final outcome of this development also minimized false alarms, thus making this system more reliable.

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 Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rohit Kundu ◽  
Hritam Basak ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Ct Scan ◽  
Detection System ◽  
Network Models ◽  
Test Cases ◽  
Gompertz Function ◽  
Neural Network Models ◽  
Source Codes ◽  
Ensemble Strategy ◽  
The World ◽  
Long Time

AbstractCOVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

Modelling the effects of distance on the probability of fire detection from lookouts

2006 ◽  
Vol 15 (2) ◽  
pp. 197 ◽  
Author(s):  
Francisco Castro Rego ◽  
Filipe Xavier Catry
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Fast Response ◽  
Fire Loss ◽  
Detection Function ◽  
Related Factors ◽  
New Approach ◽  
Forest Fire Management ◽  
In Fire

In the management of forest fires, early detection and fast response are known to be the two major actions that limit both fire loss and fire-associated costs. There are several inter-related factors that are crucial in producing an efficient fire detection system: the strategic placement and networking of lookout towers, the knowledge of the fire detection radius for lookout observers at a given location and the ability to produce visibility maps. This study proposes a new methodology in the field of forest fire management, using the widely accepted Fire Detection Function Model to evaluate the effect of distance and other variables on the probability that an object is detected by an observer. In spite of the known variability, the model seems robust when applied to a wide variety of situations, and the results obtained for the effective detection radius (13.4 km for poor conditions and 20.6 km for good conditions) are in general agreement with those proposed by other authors. We encourage the application of the new approach in the evaluation or planning of lookout networks, in addition to other integrated systems used in fire detection.

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