Development of an Early Warning Multi-criteria Fire Detection System: Analysis of Transient Fire Signatures Using a Probabilistic Neural Network.

Abstract

This report describes the progress made in developing an early warning, multi-criteria, fire detection system for the Office of Naval Research (ONR) program on Damage Control: Automation for Reduced Manning (DC-ARM). In this work, the analysis of transient fire signatures is studied using a probabilistic neural network (PNN). Experiments are described to study the effects of various PNN training parameters and to determine the optimal sensor suite combination, which enables both early fire detection and high nuisance source rejection. Comparisons are made between the candidate sensor arrays, commercial fire detection systems, and sensor arrays proposed in previous reports. Recommendations and directions for future research are also given.

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Document Details

Document Type
Technical Report
Publication Date
Feb 16, 2000
Accession Number
ADA373837

Entities

People

  • Colin Barry
  • Daniel T. Gottuk
  • Frederic W. Williams
  • Ronald E. Shaffer
  • Susan L. Rose-Pehrsson

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Carbon Monoxide
  • Chemical Synthesis
  • Chemistry
  • Data Science
  • Databases
  • Detection
  • Detectors
  • Dielectric Gases
  • False Alarms
  • Feature Selection
  • Fire Detectors
  • Information Science
  • Neural Networks
  • Pattern Recognition
  • Smoke Detectors
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Fire Suppression Systems Design.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference