Study Of Pool Fire Heat Release Rate Using Video Fire Detection

To provide fire safety for high performance buildings, various types of fire/smoke detection systems are developed. Video fire detection is one of the important aspects in the development of fire detection system. It is particularly useful in large spaces with high headroom and buildings with cross ventilation design where traditional spot type smoke detection methods may not be effective. For the development of video fire detection system, spatial, spectral and temporal parameters are used to identify the fire source. One of the parameters captured by the video fire detection system is the flame height. With the information of flame height, real time heat release rate of fire can be estimated which is a very important parameter in determining the smoke generation rate and fire severity. Such information is very important in assisting evacuation and smoke control. In this study, experiments of pool fires with different pool diameters of 100mm, 200mm, 300mm and 400mm are conducted in the fire chamber of the laboratory in Department of Building Services Engineering, The Hong Kong Polytechnic University. The flame images, room temperatures and mass loss rates of the fuel are measured. The flame images are segmented using multi – threshold algorithm in a modified Otsu method and Rayleigh distribution analysis (modified segmentation algorithm). The algorithm use the optimum threshold values calculated to extract the pool fire images from a video sequence. After segmentation, flame height information can be obtained. In addition, other flame characteristics are also used for recognizing the flame region including flame color, flame light intensity, flame shape, and flicker frequency. Once the flame height is identified by the system, the heat release rate can be estimated using the equation developed by McCaffrey. The calculated heat release rates are then compared with measured heat release rate data. The results show that using flame height image for estimating real time heat release rate is promising

Index to NASA Tech Briefs, 1975

This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

Deep Learning approach applied to drone imagery for the automatic detection of forest fire

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWildfires are one of the world's most costly and deadly natural disasters, damaging millions of hectares of vegetation and threatening the lives of people and animals. The risks to civilian agents and task forces are particularly high, which emphasizes the value of leveraging technology to minimize their impacts on nature and people. The use of drone imagery coupled with deep learning for automated fire detection can provide new solutions to this problem, limiting the damage that result. In this context, our work aims to implement a solution for the automatic detection of forest fires in real time by exploiting convolutional neural networks (CNN) on drone images based on classification and segmentation models. The methodological approach followed in this study can be broken down into three main steps: First, the comparison of two models, namely Xception Network and EfficientNetB2, for the classification of images captured during a forest burn into 'Fire' or 'No_Fire' classes. Then we will proceed to the segmentation of the images belonging to the 'Fire' class by comparing the U-Net architecture with Attention U-Net and Trans U-Net in order to choose the best performing model. The EfficientNetB2 architecture for classification gave satisfactory results with an accuracy of 71.72%. Concerning segmentation, we adopted the U-Net model which offers a segmentation accuracy that reaches 98%. As for the deployment, a fire detection application was designed using Android Studio software by assimilating the drone's camera

Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021info:eu-repo/semantics/publishedVersio

Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for finan cial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio

NASA Tech Briefs Index, 1977, volume 2, numbers 1-4

Announcements of new technology derived from the research and development activities of NASA are presented. Abstracts, and indexes for subject, personal author, originating center, and Tech Brief number are presented for 1977