利用多波长散射信号进行时间序列分类以缓解骚扰警报

Fire Pub Date : 2023-12-29 DOI:10.3390/fire7010014
Kyuwon Han, Soocheol Kim, Hoesung Yang, Kwangsoo Cho, Kangbok Lee
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引用次数: 0

摘要

烟雾探测器因其灵敏度高而成为使用最广泛的火灾探测器。然而,由于烟雾探测器无法分辨烟雾颗粒,而且其反应能力因颗粒大小和浓度而异,因此一直存在误报(即骚扰警报)问题。尽管区分烟雾颗粒的技术已经取得了可喜的成果,但由于烟雾探测器的硬件限制,有必要采用智能方法来分析各种波长的散射信号及其时间变化。在本文中,我们提出了一种基于各种波长的散射信号作为输入的烟雾颗粒分辨管道。在数据提取阶段,我们提出了从时间序列数据中提取数据集的方法。我们提出了一种结合传统方法、早期检测方法和动态时间扭曲技术的方法,该技术只利用信号的形状,无需预处理。在学习模型和分类阶段,我们提出了一种选择和比较各种架构和超参数的方法,以创建一个能实现时间序列数据最佳分类性能的模型。我们在提出的传感器和烟雾粒子测试环境中创建了六个不同目标的数据集,并训练分类模型。通过性能比较,我们确定了准确率高达 98.7% 的架构和参数组合。各种条件下的消融研究证明了所选架构的有效性和其他模型的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Series Classification with Multiple Wavelength Scattering Signals for Nuisance Alarm Mitigation
Smoke detectors are the most widely used fire detectors due to their high sensitivity. However, they have persistently faced issues with false alarms, known as nuisance alarms, as they cannot distinguish smoke particles, and their responsiveness varies depending on the particle size and concentration. Although technologies for distinguishing smoke particles have shown promising results, the hardware limitations of smoke detectors necessitate an intelligent approach to analyze scattering signals of various wavelengths and their temporal changes. In this paper, we propose a pipeline that can distinguish smoke particles based on scattering signals of various wavelengths as input. In the data extraction phase, we propose methods for extracting datasets from time series data. We propose a method that combines traditional approaches, early detection methods, and a Dynamic Time Warping technique that utilizes only the shape of the signal without preprocessing. In the learning model and classification phase, we present a method to select and compare various architectures and hyperparameters to create a model that achieves the best classification performance for time series data. We create datasets for six different targets in our presented sensor and smoke particle test environment and train classification models. Through performance comparisons, we identify architecture and parameter combinations that achieve up to 98.7% accuracy. Ablation studies under various conditions demonstrate the validity of the chosen architecture and the potential of other models.
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