Abstract
A basic approach in adaptive modeling of data acquisition is based on the comparison of real time data with the data previously predicted from the adequate numerical model. Our research was initiated to find a better algorithm for prediction of fire growth in enclosed spaces by means of interpretation of signals from fire sensors. The data obtained from temperature sensors are compared in a real time with predicted data and are used to adjust the numerical model for prediction so that it matches the reality. This adaptive approach has the following primary goals: early warning of fire growth, better alarm decision making, realization of an adaptive threshold (a threshold changing in the time according to the input signal), very small false alarm rate, etc. In this paper we introduce a method that uses smooth spline polynomials for approximation of the collected data and the time sliding window principle. The length of time sliding window varies in the real time and depends on the calculated error.
Highlights
In the context of data acquisition adaptive modeling uses the comparison of collected data with data predicted from a model to determine behavior of the system during sometime period
Already in this stage of investigation, the experiments show that smoothing spline approximation for various lengths of the time sliding window leads to almost identical predictions
The problem how to predict the development of signal from temperature sensors is realized using smoothing spline polynomial in the time sliding window
Summary
A basic approach in adaptive modeling of data acquisition is based on the comparison of real time data with the data previously predicted from the adequate numerical model. Our research was initiated to find a better algorithm for prediction of fire growth in enclosed spaces by means of interpretation of signals from fire sensors. The data obtained from temperature sensors are compared in a real time with predicted data and are used to adjust the numerical model for prediction so that it matches the reality. This adaptive approach has the following primary goals: early warning of fire growth, better alarm decision making, realization of an adaptive threshold (a threshold changing in the time according to the input signal), very small false alarm rate, etc. The length of time sliding window varies in the real time and depends on the calculated error
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