Multi-Sensor Fire Detector based on Trend Predictive Neural Network

Mert Nakıp, C. Güzelı̇ş
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引用次数: 7

Abstract

In this paper, we propose a Trend Predictive Neural Network (TPNN) model, which uses the sensor data and the trend of that data in order to classify the fire situation. We implemented TPNN for data of multi-sensor fire detector with 6 sensors to detect 7 inputs. We test the performance of the TPNN model by using the multi-sensor dataset, which is collected within this study. Our results show that the TPNN model is a fast and accurate model, whose execution time is 0.0132 seconds. Furthermore, TPNN decreases both the false positive and false negative alarm rates to half of the results of the multi-layer perceptron model.
基于趋势预测神经网络的多传感器火灾探测器
本文提出了一种趋势预测神经网络(TPNN)模型,该模型利用传感器数据及其趋势来对火灾情况进行分类。我们对多传感器火灾探测器的数据实现了TPNN,其中6个传感器检测7个输入。我们通过使用本研究中收集的多传感器数据集来测试TPNN模型的性能。结果表明,TPNN模型是一种快速准确的模型,其执行时间为0.0132秒。此外,TPNN将假阳性和假阴性报警率降低到多层感知器模型结果的一半。
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