Application of Artificial Neural Network to Building Compartment Design for Fire Safety

2006 ◽  
pp. 265-274 ◽  
Author(s):  
Eric Wai Ming Lee ◽  
Po Chi Lau ◽  
Kitty Kit Yan Yuen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fire Safety ◽  
Artificial Neural

scholarly journals MARINE FIRE-DANGEROUS SITUATIONS FACTORS’ VALUES FORECASTING BASED ON ARTIFICIAL NEURAL NETWORK

2020 ◽  
pp. 34-40
Author(s):  
A. E. Romanov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fire Safety ◽  
Learning Algorithm ◽  
Air Density ◽  
Algorithmic Support ◽  
Information Management Systems ◽  
Artificial Neural ◽  
Perceptron Learning ◽  
Perceptron Learning Algorithm

The article describes the procedure of marine fire-dangerous situations factors’ values forecasting based on artificial neural network. These factors are temperature, optical air density, aerosol concentration. Given procedure is flexible and can be expanded for other factors of fire-safety state of monitored object. Artificial neural network with architecture of three-layer perceptron is used for forecasting. The article gives a common scheme for realization of fire-dangerous situations factors’ values forecasting, substantiates the choice of used artificial neural network’s architecture, gives perceptron learning algorithm. As a result of given procedure execution factors’ values forecasting is implemented for prevention of fire-dangerous situation and the adoption of early actions. In case of integration of the developed procedure inside ship information management systems’ algorithmic support is capable of dramatically raise effectiveness of decisions made while providing fire safety on ships.

Using an Artificial Neural Network to Predict Flame Spread across Electrical Wires

2021 ◽  
pp. 1-28
Author(s):  
Lauren Gagnon ◽  
Van P. Carey ◽  
Carlos Fernandez-Pello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flame Spread ◽  
Fire Safety ◽  
Spread Rate ◽  
Space Applications ◽  
Energy Technologies ◽  
Flame Spread Rate ◽  
Data Points ◽  
Artificial Neural

Abstract There is currently a global-scale transition from fossil fuel energy technologies towards increasing use of electrically driven energy technologies, especially transportation and heat, fueled by renewable energy sources, which is making fire safety in electrically powered systems increasingly important. The work presented here provides a coherent understanding of flame spread parametric trends and associated fire safety issues in electrical systems for structural, transportation, and space applications. This understanding was obtained through use of an artificial neural network (ANN) that was trained to predict the flame spread rate along “laboratory” wires of different sizes and compositions (copper, nichrome, iron, and stainless-steel tube cores and HDPE, LDPE, and ETFE insulation sheaths) and exposed to different ambient conditions (varying flows, pressure, oxygen concentration, orientation, and gravitational strength). For these predictions, a comprehensive data base of 1200 data points was created by incorporating flame spread rate results from both in-house experiments (400 data points) as well external experiments from other sources (800 data points). The predictions from the ANN showed that it is possible to merge together various data sets, including results from horizontal, inclined, vertical, and microgravity experiments, and obtain unified predictive results. While these initial results are very encouraging with an overall average error rate of 14%, they also show that future improvements to the ANN could still be made to increase prediction accuracy.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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