Analysis of Multi-Criteria Fire Detection Data and Early Warning Fire Detection Prototype Selection

Abstract

This report describes the analysis of Fire/Nuisance Source data and the selection of sensors for 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
Sep 18, 2000
Accession Number
ADA382541

Entities

People

  • Daniel T. Gottuk
  • Jennifer T. Wong
  • Ronald E. Shaffer
  • Sean J. Hart
  • Susan L. Rose-Pehrsson

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Carbon Monoxide
  • Chemical Detectors
  • Chemical Synthesis
  • Chemistry
  • Data Acquisition
  • Data Analysis
  • Data Science
  • Detection
  • Detectors
  • Dielectric Gases
  • False Alarms
  • Information Science
  • Neural Networks
  • Pattern Recognition
  • Smoke Detectors
  • Warning Systems

Readers

  • Fire Suppression Systems Design.
  • Sensor Fusion and Tracking Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference