Statistical Characteristics of Scattering Ratio Based on Three Optical Wavelengths for Smoke Particles

Han, Kyuwon; Kim, Soocheol; Yang, Heesu; Cho, Kwang Soo; Lee, Kangbok; Han, Haoxue

doi:10.7731/kifse.4e23ccc1

Cited by 2 publications

(5 citation statements)

References 5 publications

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“…Subsequently, it can be observed that they reach a saturation point at a constant level and exhibit fluctuations. Note that this pattern is not limited to the experiments conducted in this paper but is observed in studies involving scattering signal collection through optical detectors, including similar fire tests [12,17]. Based on these shapes of signals, we extract data using three methods.…”

Section: Methodsmentioning

confidence: 90%

“…Focusing on the correlation between different wavelengths, it is possible to develop technologies for detecting non-fire sources or distinguishing smoke particles using light sources at various wavelengths. In [12], research focused on extracting characteristic values through the statistical analysis of correlations among multiple wavelengths, while [13] explored the discrimination of smoke particles using signals from multiple wavelengths. In the realm of products, both TI's TPS8802 [14] and Analog Devices' ADPD188BI [15] introduce modules that are based on the scattering efficiency between dual wavelengths, aiming not only for fire detection but also for distinguishing non-fire sources.…”

Section: Backgroundsmentioning

confidence: 99%

“…Depending on the smoke material defined in the UL268 document [2], each has its own diffusion speed and concentration as detected by optical detectors. Additionally, in [12], the research aimed to extract an effective range due to the instability of smoke particles in time. Furthermore, as presented in Analog Devices' research [16], it was demonstrated that classification is possible based on the difference in diffusion speeds between fire smoke and cooking smoke.…”

Section: Backgroundsmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 90%

Section: Backgroundsmentioning

confidence: 99%

Section: Backgroundsmentioning

confidence: 99%

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

Han,

Kim,

Yang

et al. 2023

Fire

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“…In previous studies, a smoke detector capable of detecting the scattering intensity of smoke particles was utilized [14,15]. In a previous experiment, a smoke detector was adopted to repeatedly measure the scattering intensity of light with multiple optical wavelengths [16]. The scattering intensity of smoke particles derived from each source indicates the unique characteristics of the sources.…”

Section: Multiple Optical Wavelengths-based Smoke Detectormentioning

confidence: 99%

“…In this study, a previous fire experimental dataset was collected. The fire experiment was conducted in the same manner as that in the experiment conducted by Han et al [16] in the same experiment chamber. The chamber is a carbon-steel square duct with dimensions of   ×      .…”

Section: Previous Experiments Dataset For Data Collectionmentioning

confidence: 99%

Smoke Particle-Source Prediction Model Based on Multiple Optical Wavelengths Using Deep Learning

Ahn¹,

Han²,

Hoe-Sung³

et al. 2023

Int J Fire Sci Eng

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Recently, installing smoke detectors has become crucial owing to the risk of fatal human damage that may be caused by inhaling smoke during a fire. Smoke detectors have been reported as highly efficient in detecting smoke particles from fire; however, they may generate false alarms because of their limitation in distinguishing the fire smoke from the smoke generated by daily activities. Despite the frequent occurrence of these false alarms, research on predicting the types of sources through smoke particles remains insufficient. This study involved the development process of an intelligent smoke detector for false alarm reduction that aims to predict the occurrence and type of fire and the evaluation of its performance using the light-scattering characteristics for fire/non-fire sources. First, a previous experimental dataset of fire-related conditions was collected from three fire sources and three non-fire sources to train the model with the light-scattering characteristics of the smoke generated from each source. In addition, to reduce the computing power, data preprocessing was performed on the collected dataset using the median and RobustScaler. Finally, we evaluated the prediction performance of the three deep learning models using three networks: RNN, LSTM, and CNN-LSTM. As a result, we confirmed that the scattering intensity of smoke particles has unique characteristics for each source. When the data preprocessing and prediction models were applied, all three models achieved an accuracy of 0.90 or higher. However, some errors occurred that appeared at similar scattering intensities. The proposed method differs from existing methods in that it presents the possibility of predicting fire and non-fire sources and can be used as an alternative for improving false alarms in the future.

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Statistical Characteristics of Scattering Ratio Based on Three Optical Wavelengths for Smoke Particles

Cited by 2 publications

References 5 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

Smoke Particle-Source Prediction Model Based on Multiple Optical Wavelengths Using Deep Learning

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