scholarly journals Dual Operated Firefighting Robot

2021 ◽  
Author(s):  
Akash Sugathan ◽  
Manoj Balakrishnan ◽  
Abhijith A. Pillai ◽  
Arjun R ◽  
Athul P. Varghese
Keyword(s):  
Power Supply ◽  
Detection System ◽  
Fire Detection ◽  
Fire Extinguisher ◽  
Different Types ◽  
Fire Detection System ◽  
Overall Efficiency ◽  
Narrow Space ◽  
Set Up ◽  
Compact Design

Abstract In this study, a dual operated firefighting robot was designed, fabricated and analyzed. The robot can be operated in both RC and automatic modes. The robot consists of three different types of system units- fire detection system, extinguishing system and communication system. The fire detection system uses flame sensors for detection of fire. The extinguishing unit consists of fire extinguisher cylinder mounted on the robot which can be controlled by the user through a remote. . The fire extinguisher is operated with the help of a relay circuit. The whole set up is controlled and monitored by the main controller or microprocessor. Using a belt drive enables the robot to climb stairs. The main chassis consists of 400mm*390mm aluminum profile. Four motors, each of 60 rpm, are used which provides adequate thrust for motion. The power supply for the functioning of robot is provided by a 12V, 7 AH battery. The compact design of the firefighting robot enables it to enter small or narrow space with ease. More over the dual operation ability of the robot helps to increase the overall efficiency of the robot.

Design of Building Fire Detection System Based on ZigBee

2013 ◽  
Vol 427-429 ◽  
pp. 1432-1435
Author(s):  
Ning Du ◽  
Yao Wang ◽  
Zhi Long Liu
Keyword(s):  
Detection System ◽  
Fire Detection ◽  
Sensor Nodes ◽  
Fire Monitoring ◽  
Mesh Topology ◽  
Industrial Monitoring ◽  
Fire Detection System ◽  
Set Up ◽  
Cable Fire ◽  
Monitoring Application

Comparing with other fire detection system used in buildings, this paper proposes a design scheme based on ZigBee. ZigBee is a well known wireless communication technology which is wildly used in industrial monitoring. It solves the problem of difficulty to set up the network, high cost and restricted scope in existing cable fire detection monitoring system. This system mainly comprises sensor nodes, middleware and monitoring application. We adopt MESH topology in wireless sensor networks in order to enlarge the monitoring scope. To find the accurate fire position quickly, GPS is adopted in design of sensor node. The Device Cloud is used in middleware and monitoring application to provide open APIs for web, 3G and SMS. Using these technologies the system can provide real-time fire monitoring.

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 A TBB-CUDA Implementation for Background Removal in a Video-Based Fire Detection System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fan Wang ◽  
Xiao Jiang ◽  
Xiao Peng Hu
Keyword(s):  
Data Transfer ◽  
Detection System ◽  
Gaussian Model ◽  
Fire Detection ◽  
Distribution Model ◽  
Memory Bandwidth ◽  
Limited Memory ◽  
Background Removal ◽  
Fire Detection System ◽  
Gaussian Distribution Model

This paper presents a parallel TBB-CUDA implementation for the acceleration of single-Gaussian distribution model, which is effective for background removal in the video-based fire detection system. In this framework, TBB mainly deals with initializing work of the estimated Gaussian model running on CPU, and CUDA performs background removal and adaption of the model running on GPU. This implementation can exploit the combined computation power of TBB-CUDA, which can be applied to the real-time environment. Over 220 video sequences are utilized in the experiments. The experimental results illustrate that TBB+CUDA can achieve a higher speedup than both TBB and CUDA. The proposed framework can effectively overcome the disadvantages of limited memory bandwidth and few execution units of CPU, and it reduces data transfer latency and memory latency between CPU and GPU.

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