{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T14:44:02Z","timestamp":1784126642690,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T00:00:00Z","timestamp":1556236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research &amp; Development Program of China","award":["2018YFC0807900\uff1b2018YFC0807903"],"award-info":[{"award-number":["2018YFC0807900\uff1b2018YFC0807903"]}]},{"name":"National Nature Science Foundation under Grant","award":["61702020"],"award-info":[{"award-number":["61702020"]}]},{"name":"Beijing Natural Science Foundation Grant","award":["4172013"],"award-info":[{"award-number":["4172013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks\u2014VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50\u2014were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.<\/jats:p>","DOI":"10.3390\/s19091960","type":"journal-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T07:52:59Z","timestamp":1556265179000},"page":"1960","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification"],"prefix":"10.3390","volume":"19","author":[{"given":"Lu","family":"Han","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongchong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaitai","family":"Xiao","sequence":"additional","affiliation":[{"name":"Shenyang Research Institute of China Coal Technology and Engineering Group, Fushun 113122, China"},{"name":"State Key Laboratory of Coal Mine Safety Technology, Shenyang Branch of China Coal Research Institute, Shenyang 110016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3390\/s7030267","article-title":"Gas Sensors Based on Conducting Polymers","volume":"7","author":"Bai","year":"2007","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Szulczy\u0144ski, B., Armi\u0144ski, K., Namie\u015bnik, J., and G\u0119bicki, J. 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