Characterization of Typical Fire and Non-fire Aerosols by Polarized Light Scattering for Reliable Optical Smoke Detection

Zhang, Qixing; Liu, Jia; Luo, Jie; Feng, Wang; Wang, Jinjun; Zhang, Yongming

doi:10.1007/978-981-32-9139-3_58

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…A correlation method of selecting an optimal set of wavelengths and scattering angles was presented in [3], in which Q. Zhang et al, extracted an indicator for discrimination between fire and non-fire aerosols using scattering matrix analysis in polarized light scattering [4]. T. Deng et al, designed a sensor to measure surface area concentration with dual wavelength sources [5].…”

Section: Introductionmentioning

confidence: 99%

Time Series Classification with Multiple Wavelength Scattering Signals for Nuisance Alarm Mitigation

Han,

Kim,

Yang

et al. 2023

Fire

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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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Section: Introductionmentioning

confidence: 99%

Time Series Classification with Multiple Wavelength Scattering Signals for Nuisance Alarm Mitigation

Han,

Kim,

Yang

et al. 2023

Fire

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From ashes to answers: decoding acoustically agglomerated soot particle signatures

Ko,

Li,

Mozaffari

et al. 2024

J Nanopart Res

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This study investigated the possibility of extending the soot morphology analyses to acoustically agglomerated soot deposited on the surface of smoke alarms and of applying the validity of soot analysis for unique chemical signatures in the field of fire investigations. Through collecting soot samples, including agglomerated soot acquired from smoke alarms, this research presents a pioneering stride in soot morphology data analyses conducted by leveraging advanced deep learning methodologies. Preliminary outcomes underline that the proposed convolutional neural network model has the potential to decode intricate soot characteristics and to distinguish soot particle images between diverse fuel types and burning conditions. In particular, for the acoustically agglomerated soot collected by smoke alarms, it was also found possible to decode their intricate morphology by applying the proposed data-driven approach.

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Comprehensive thematic T-matrix reference database: a 2017–2019 update

Mishchenko

2020

Journal of Quantitative Spectroscopy and Radiative Transfer

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Characterization of Typical Fire and Non-fire Aerosols by Polarized Light Scattering for Reliable Optical Smoke Detection

Cited by 3 publications

References 9 publications

Time Series Classification with Multiple Wavelength Scattering Signals for Nuisance Alarm Mitigation

Time Series Classification with Multiple Wavelength Scattering Signals for Nuisance Alarm Mitigation

From ashes to answers: decoding acoustically agglomerated soot particle signatures

Comprehensive thematic T-matrix reference database: a 2017–2019 update

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