scholarly journals Design of fire Design of Fire Detection System Based on Artificial Intelligence Technology

2022 ◽  
Vol 2146 (1) ◽  
pp. 012032
Author(s):  
Wei Shi
Keyword(s):  
Detection System ◽  
Rapid Development ◽  
Fire Detection ◽  
Real Data ◽  
Management Models ◽  
Public Data ◽  
Artificial Intelligence Technology ◽  
Fire Detection System ◽  
Important Position ◽  
Fire Design

Abstract In this era of rapid development of network and technology, data has become the most important part of companies and people. In fact, the software and system series are just the framework for storing data, and real data occupies an important position in the entire communication. This paper focuses on data mining and management models of public data resources. Starting from how to mine useful information from public data resources and how to manage such data, it puts forward several classifications of big data management models and their respective advantages.

scholarly journals Digital Print Synthesis Based on Image Processing and Interactive Technology

2022 ◽  
Vol 2146 (1) ◽  
pp. 012028
Author(s):  
Lijun Sun
Keyword(s):  
Artificial Intelligence ◽  
Error Rate ◽  
Detection System ◽  
Human Life ◽  
Fire Detection ◽  
Detection Technology ◽  
Artificial Intelligence Technology ◽  
Internal Mechanism ◽  
Fire Detection System ◽  
The Many

Abstract Fire is a common disaster, which causes major threats and losses to human life and property. Countries around the world have been committed to the study of the mechanism and internal mechanism of fires, with the goal of preventing fires from occurring and minimizing the losses caused by fires. Among the many methods, fire detection technology is an effective method to prevent and reduce the occurrence of fire. This article focuses on the research of the fire detection system based on artificial intelligence technology, improves the accuracy of the fire detection system by introducing artificial intelligence technology into the fire detection system, and uses experiments to verify the error rate of the artificial intelligence technology fire detection system. The experimental results show that the system’s detection of fire is not very different from the actual situation, and the error rate is within 10%. Then compared with the traditional detection system, the detection performance is relatively high, and the error rate can be reduced by one time.

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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