{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:20:31Z","timestamp":1782750031410,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFC3001600"],"award-info":[{"award-number":["2021YFC3001600"]}]},{"name":"National Key Research and Development Program of China","award":["62071189"],"award-info":[{"award-number":["62071189"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFC3001600"],"award-info":[{"award-number":["2021YFC3001600"]}]},{"name":"National Natural Science Foundation of China","award":["62071189"],"award-info":[{"award-number":["62071189"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial\u2013temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial\u2013temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.<\/jats:p>","DOI":"10.3390\/s24030778","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T08:44:07Z","timestamp":1706172247000},"page":"778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Embedded Spatial\u2013Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification"],"prefix":"10.3390","volume":"24","author":[{"given":"Fang","family":"Xu","sequence":"first","affiliation":[{"name":"Shenyang Fire Research Institute of M.E.M., Shenyang 110034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"National Engineering Research Center of Fire and Emergency Rescue, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1288-5101","authenticated-orcid":false,"given":"Mengxue","family":"Lin","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"National Engineering Research Center of Fire and Emergency Rescue, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maosen","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"National Engineering Research Center of Fire and Emergency Rescue, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"National Engineering Research Center of Fire and Emergency Rescue, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103364","DOI":"10.1016\/j.firesaf.2021.103364","article-title":"Real-time fire detection system based on dynamic time warping of multichannel sensor networks","volume":"123","author":"Baek","year":"2021","journal-title":"Fire Saf. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.firesaf.2017.08.004","article-title":"Deep domain adaptation based video smoke detection using synthetic smoke images","volume":"93","author":"Xu","year":"2017","journal-title":"Fire Saf. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.firesaf.2019.03.004","article-title":"Video smoke detection based on deep saliency network","volume":"105","author":"Xu","year":"2019","journal-title":"Fire Saf. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103184","DOI":"10.1016\/j.firesaf.2020.103184","article-title":"Real-time video-based smoke detection with high accuracy and efficiency","volume":"117","author":"Li","year":"2020","journal-title":"Fire Saf. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103690","DOI":"10.1016\/j.firesaf.2022.103690","article-title":"3DVSD: An end-to-end 3D convolutional object detection network for video smoke detection","volume":"134","author":"Huo","year":"2022","journal-title":"Fire Saf. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jang, H., and Hwang, C. (2020). Obscuration threshold database construction of smoke detectors for various combustibles. Sensors, 20.","DOI":"10.3390\/s20216272"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1007\/s10694-020-01061-2","article-title":"Evaluation of empirical evidence against zone models for smoke detector activation prediction","volume":"59","author":"Cleary","year":"2023","journal-title":"Fire Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1007\/s10694-019-00872-2","article-title":"Dual deep learning model for image-based smoke detection","volume":"55","author":"Pundir","year":"2019","journal-title":"Fire Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, T., Cheng, J., Du, X., Luo, X., Zhang, L., Cheng, B., and Wang, Y. (2019). Video smoke detection method based on change-cumulative image and fusion deep network. Sensors, 19.","DOI":"10.3390\/s19235060"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zheng, X., Chen, F., Lou, L., Cheng, P., and Huang, Y. (2022). Real-time detection of full-scale forest fire smoke based on deep convolution neural network. Remote Sens., 14.","DOI":"10.3390\/rs14030536"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116805","DOI":"10.1016\/j.indcrop.2023.116805","article-title":"Eco-friendly functional cellulose paper as a fire alarming via wireless warning transmission for indoor fireproofing","volume":"200","author":"Li","year":"2023","journal-title":"Ind. Crop. Prod."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.firesaf.2015.11.010","article-title":"False alarm ratio of fire detection and fire alarm systems in Germany-A meta analysis","volume":"79","author":"Festag","year":"2016","journal-title":"Fire Saf. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103547","DOI":"10.1016\/j.firesaf.2022.103547","article-title":"A self-attention network for smoke detection","volume":"129","author":"Jiang","year":"2022","journal-title":"Fire Saf. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108791","DOI":"10.1016\/j.optlastec.2022.108791","article-title":"Optical sensor for combustion aerosol particle size distribution measurement based on embedded chip with low-complexity Mie scattering algorithm","volume":"158","author":"Lin","year":"2023","journal-title":"Opt. Laser Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3490","DOI":"10.1364\/OE.477231","article-title":"Aerosol Sauter mean diameter measurement based on the light scattering response of the combined particle volume-surface area","volume":"31","author":"Lin","year":"2023","journal-title":"Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113963","DOI":"10.1016\/j.sna.2022.113963","article-title":"In situ optical sensor for aerosol ovality and size","volume":"347","author":"Lin","year":"2022","journal-title":"Sens. Actuators A Phys."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0379-7112(96)00048-3","article-title":"Optical properties of fire and non-fire aerosols","volume":"29","author":"Loepfe","year":"1997","journal-title":"Fire Saf. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.firesaf.2017.08.001","article-title":"Method of identifying burning material from its smoke using attenuation of light","volume":"93","author":"Chaudhry","year":"2017","journal-title":"Fire Saf. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103541","DOI":"10.1016\/j.firesaf.2022.103541","article-title":"Multi-parameter fire detection method based on feature depth extraction and stacking ensemble learning model","volume":"128","author":"Qu","year":"2022","journal-title":"Fire Saf. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103724","DOI":"10.1016\/j.firesaf.2022.103724","article-title":"Research on multi-detector real-time fire alarm technology based on signal similarity","volume":"136","author":"Yu","year":"2023","journal-title":"Fire Saf. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103733","DOI":"10.1016\/j.firesaf.2023.103733","article-title":"A fire alarm judgment method using multiple smoke alarms based on Bayesian estimation","volume":"136","author":"Liu","year":"2023","journal-title":"Fire Saf. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103574","DOI":"10.1016\/j.firesaf.2022.103574","article-title":"Research on the aerosol identification method for the fire smoke detection in aircraft cargo compartment","volume":"130","author":"Zheng","year":"2022","journal-title":"Fire Saf. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1002\/andp.19083300302","article-title":"Beitr\u00e4ge zur optik tr\u00fcber medien, speziell kolloidaler metall\u00f6sungen","volume":"330","author":"Mie","year":"1908","journal-title":"Ann. Phys."},{"key":"ref_24","first-page":"504","article-title":"Experimental study on false alarms of smoke detectors caused by kitchen oil fume","volume":"22","author":"Xie","year":"2003","journal-title":"Fire Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/0379-7112(94)90072-8","article-title":"Gas sensing for fire detection: Measurements of CO, CO2, H2, O2, and smoke density in European standard fire tests","volume":"22","author":"Jackson","year":"1994","journal-title":"Fire Saf. J."},{"key":"ref_26","unstructured":"(2012). Fire Detection and Alarm Systems\u2014Part 9: Test Fires for Fire Detectors (Standard No. ISO 7240-9)."},{"key":"ref_27","first-page":"25","article-title":"Research on multi-information data of cooking fumes in standard combustion room","volume":"17","author":"Shen","year":"2008","journal-title":"Fire Saf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Coffey, E.R., Pfotenhauer, D., and Mukherjee, A. (2019). Kitchen area air quality measurements in northern Ghana: Evaluating the performance of a low-cost particulate sensor within a household energy study. Atmosphere, 400.","DOI":"10.3390\/atmos10070400"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103043","DOI":"10.1016\/j.firesaf.2020.103043","article-title":"Prevention of cooktop ignition using detection and multi-step machine learning algorithms","volume":"120","author":"Tam","year":"2021","journal-title":"Fire Saf. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/778\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:57Z","timestamp":1760104137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"references-count":29,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030778"],"URL":"https:\/\/doi.org\/10.3390\/s24030778","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}