scholarly journals Numerical Analysis of Smoke Behavior and Detection of Solid Combustible Fire Developed in Manned Exploration Module Based on Exploration Gravity

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 85
Author(s):  
Ter-Ki Hong ◽  
Seul-Hyun Park
Keyword(s):  
Numerical Analysis ◽  
Detection System ◽  
Early Stage ◽  
Space Exploration ◽  
Fire Detection ◽  
Measurement Technology ◽  
Smoke Detector ◽  
Smoke Particle ◽  
Manned Space ◽  
Detection Characteristics

A fire during manned space exploration can cause serious casualties and disrupt the mission if the initial response is delayed. Therefore, measurement technology that can detect fire in the early stage of ignition is important. There have been a number of works that investigate the smoke behaviors in microgravity as the foundation for a reliable method for sensing a fire during spaceflight. For space missions to the outer planets, however, a strategy of detecting smoke as an indicator of fire should be adjusted to cover the fire scenario that can be greatly affected by changes in gravity (microgravity, lunar, Mars, and Earth gravity). Therefore, as a preliminary study on fire detectors of the manned pressurized module, the present study examined the smoke particle behavior and detection characteristics with respect to changes in gravity using numerical analysis. In particular, the effects of the combination of buoyancy and ventilation flow on the smoke particle movement pattern was investigated to further improve the understanding of the fire detection characteristics of the smoke detector, assuming that a fire occurred in different gravity environments inside the pressurized module. To this end, we modeled the internal shape of Destiny and performed a series of numerical analysis using the Fire Dynamics Simulator (FDS). The findings of this study can provide basic data for the design of a fire detection system for manned space exploration modules.

scholarly journals Computer Vision Application for Early Stage Smoke Detection on Ships

2016 ◽  
Vol 52 (1) ◽  
pp. 63-80
Author(s):  
Miroslav Bistrović ◽  
Jasmin Čelić ◽  
Domagoj Komorčec
Keyword(s):  
Detection System ◽  
Early Stage ◽  
Fire Detection ◽  
Water Mist ◽  
Smoke Detector ◽  
Smoke Detection ◽  
Engine Room ◽  
Computer Image Processing ◽  
Flame Detector ◽  
Safety Features

Nowadays, ship’s engine room is fire protected by automatic fire fighting systems, usually controlled from a place located outside the engine room. In order to activate the water mist extinguishing system automatically, at least two different fire detectors have to be activated. One of these detectors is a flame detector that is not hampered by various air flows caused by ventilation or draft and is rapidly activated and the other is smoke detector which is hampered by these flows causing its activation to be delayed. As a consequence, the automatic water mist extinguishing system is also delayed, allowing for fire expansion and its transfer to surrounding rooms. In addition to reliability of the ship’s fire detection system as one of the crucial safety features for the ship, cargo, crew and passengers, using a systematic approach in this research the emphasis is placed on the application of new methods in smoke detection such as the computer image processing and analysis, in order to achieve this goal. This paper describes the research carried out on board ship using the existing marine CCTV systems in early stages of smoke detection inside ship’s engine room, which could be seen as a significant contribution to accelerated suppression of unwanted consequences.

scholarly journals Sistem Pemantauan dan Pendeteksi Kebakaran berbasis Logika Fuzzy dan Real-time Database

2021 ◽  
Vol 9 (3) ◽  
pp. 577
Author(s):  
EVA AISAH HW ◽  
ROHMAT TULLOH ◽  
SUGONDO HADIYOSO ◽  
DADAN NUR RAMADAN
Keyword(s):  
Fuzzy Logic ◽  
Internet Of Things ◽  
Real Time ◽  
Human Error ◽  
Detection System ◽  
Environmental Parameters ◽  
Fire Detection ◽  
Smoke Detector ◽  
Web Interface ◽  
Average Transmission

ABSTRAKKebakaran rumah seringkali disebabkan oleh kelalaian manusia. Oleh karena itu diperlukan sebuah sistem yang dapat mendeteksi kebakaran secara online realtime. Pada studi ini, dirancang dan diimplementasikan sebuah sistem pendeteksi kebakaran dengan sejumlah sensor untuk mengukur beberapa parameter lingkungan. Sistem ini dilengkapi dengan pengambil keputusan menggunakan metode fuzzy logic. Parameter lingkungan yang diukur mencakup suhu ruangan, asap dan api yang kemudian dapat dimonitor secara real-time melalui web interface menggunakan Internet of Things platform. Pengujian menunjukkan bahwa detektor dapat mendeteksi api dengan jarak hingga 100 cm dengan akurasi mencapai 100%. Pengujian sensor suhu menunjukkan akurasi 98.79%, sementara itu detektor asap memperoleh akurasi 77.81%. Sistem ini mampu mengirimkan data dengan rata-rata delay transmisi 0.62 detik. Sistem usulan ini diharapkan dapat menyediakan pemantauan kondisi suatu ruangan secara real-time.Kata kunci: Kebakaran, Real-Time, Deteksi, Fuzzy, Internet Of Things ABSTRACTHouse fires are often caused by human error. Therefore, we need a system that can detect fires online real-time. In this study, a fire detection system with a number of sensors is designed and implemented to measure several environmental parameters. This system is equipped with a decision maker using the fuzzy logic method. The environmental parameters measured include room temperature, smoke and fire which can then be monitored in real time via a web interface using the Internet of Things platform. Tests show that the detector can detect fires with a distance of up to 100 cm with an accuracy of up to 100%. The temperature sensor test shows an accuracy of 98.79%, while the smoke detector generates an accuracy of 77.81%. This system is capable of sending data with an average transmission delay of 0.62 seconds. This proposed system is expected to provide realtime monitoring of the condition of a room.Keywords: Fire, Real-time, detection, Fuzzy, internet of things

scholarly journals Early Fire Detection System in Tanzania Markets

2020 ◽  
Vol 2 (1) ◽  
pp. 50
Author(s):  
Rambo Hilary ◽  
Philemon Rotich ◽  
Anna Geofrey ◽  
Anael Sam
Keyword(s):  
Data Processing ◽  
Mobile Application ◽  
Detection System ◽  
Early Stage ◽  
Fire Detection ◽  
Wireless Sensor ◽  
Multiple Sensors ◽  
Fire Detection System ◽  
Processing Subsystem ◽  
Fire Outbreak

Application of wireless sensor networks (WSN) and Internet of Things (IoT) used to provide real-time monitoring of fire outbreak in markets. The system integrates three subsystems namely; sensing subsystem which uses multiple sensors for detecting fire outbreaks. Data processing subsystem which collects data from the sensing subsystem through Xbee, analyses, and uploads data to the cloud. If values exceed the sensor threshold, an alarm is triggered and notification is sent to stakeholders via mobile application subsystem. The integration between sensing, data processing, and mobile application subsystems pave a new way for the mitigation of fire outbreaks at its early stage.

scholarly journals Comparative Analysis of Rules in Five Leading Standards for Smoke Detectors Siting in the Presence of a Ceiling Irregularity

2017 ◽  
Vol 12 (2) ◽  
pp. 23-29 ◽  
Author(s):  
Milan Blagojević ◽  
Radoje Jevtić ◽  
Dejan Ristić
Keyword(s):  
Detection System ◽  
Fire Detection ◽  
Combustion Products ◽  
Point Of View ◽  
Smoke Detector ◽  
Main Question ◽  
European Norm ◽  
Length Width ◽  
Smoke Detectors ◽  
Formed Structure

Abstract Subdividing elements and different structures on the ceiling like beams or similar, significantly affect the location of the smoke detector, because they change the flow of combustion products. From point of view of fire detection system, designers it is very interesting how to arrange and distribute smoke detectors in applications when beams are formed structure like a “honeycomb” The European norm 54-14 is mandatory, but in practice, a main question appears: “Do we have the explanations detailed enough for all of the situations that could occur related to length, width and depth of honeycomb cells”? The main goal of this paper is to show the differences between the rules and the instructions in five standards: EN 54-14, VDE 0833-2, BS 5839-1, NPB 88, NFPA 72, and to find the best solution for various situations in practice.

scholarly journals Proposed System of Enhancement in Accuracy for Fire Detection System using Machine Learning Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2093-2097
Keyword(s):  
Video Surveillance ◽  
Video Sequence ◽  
Learning Algorithm ◽  
Detection System ◽  
Early Stage ◽  
Fire Detection ◽  
Early Event ◽  
Generation System ◽  
Fire Event ◽  
Better Than

The video surveillance system has become a important part in the security and protection of cities. The Video surveillance has become an important factor in the cities, since smart monitoring cameras mounted with intelligent video analytics techniques can monitor and pre-alert system by capturing abnormal activity such as fire events. The current world is completely under CCTV for make the various areas secure. The video recorded is unable to find out fire detection at early stage of fire event. After event happened this video sequence is used to find out causes of an event/fire but problem is after event happened system are unable to save loss by that event or accident, so there is need to such system is able to help us in early event detection and pre-alert generation system. Motive behind this proposed work is to invent pre-alert generation system without any hardware as well as sensor. Accuracy of this proposed system may be approx.85-90% or more which is better than existing system.

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.

Simulation Study on the Aspirating Smoke Detection System in Large Space Buildings

2011 ◽  
Vol 255-260 ◽  
pp. 1404-1408 ◽  
Author(s):  
Zhen Na Zhang ◽  
Hong Bing Chen
Keyword(s):  
Simulation Study ◽  
Detection System ◽  
Early Stage ◽  
Side Wall ◽  
Fire Detection ◽  
Smoke Detection ◽  
Large Space ◽  
Pipe Length ◽  
Response Performance ◽  
Fire Location

The aspirating smoke detection (ASD) system has been widely used in large space buildings. It plays an important role on fire detection at its early stage. In this paper, the model of large space building was established for the simulation study on the response performance of the ASD system. The investigation on the effect of fire location, sampling hole space and pipe length on the responding time, was carried out. The results showed that the responding time of FIRE B (fire close to side wall) is much delayed than that of FIRE A (fire in centre); the increase of sampling hole space and pipe length leads to the delay of the responding time.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

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