{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T03:09:15Z","timestamp":1730257755744,"version":"3.28.0"},"reference-count":17,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/icsens.2018.8589870","type":"proceedings-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T22:13:44Z","timestamp":1547849624000},"page":"1-4","source":"Crossref","is-referenced-by-count":2,"title":["Study on an Improved LeNet-5 Gas Identification Structure for Electronic Noses"],"prefix":"10.1109","author":[{"given":"Guangfen","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1097","article-title":"Image net classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems 25"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/72.554195"},{"key":"ref13","first-page":"90","article-title":"An eye feature detector based on convolutional neural network,&#x201D; [C]","author":"tivive","year":"2005","journal-title":"Proc 8th Int Symp Signal Process Applic"},{"key":"ref14","first-page":"224","article-title":"Pedestrian detection with convolutional neural networks,&#x201D; [C]","author":"mate","year":"2005","journal-title":"IEEE Intelligent Vehicles Symposium Proceedings"},{"key":"ref15","article-title":"Off-road obstacle avoidance through end-to-end learning,&#x201D; [M]","author":"cun","year":"2005","journal-title":"Advances in neural information processing systems"},{"key":"ref16","first-page":"1233","article-title":"A Survey of Convolution Networks [J]","author":"zhou","year":"2017","journal-title":"Chinese Journal of Computers"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126555"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2009.12.027"},{"key":"ref3","first-page":"1036","article-title":"Electronic noses for food quality","volume":"144","author":"loutfi","year":"2015","journal-title":"A Review Journal of Food Engineering"},{"key":"ref6","article-title":"Estimating Gas Concentration using Artificial Neural Network for Electronic Nose","volume":"11","author":"sabilla","year":"2017","journal-title":"[J] Science Direct"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.3390\/s16122069","article-title":"Fault detection using the clustering-kNN rule for gas sensor arrays","volume":"28","author":"yang","year":"2016","journal-title":"SENSORS"},{"journal-title":"Research progress and prospects of deep learning in image recognition [D]","year":"2015","author":"wang","key":"ref8"},{"key":"ref7","first-page":"171","article-title":"Mixed Odors Classification by Neural Networks","author":"omatul","year":"2015","journal-title":"Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems Technology and Applications (IDAACS)"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jarmap.2015.12.002"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2015.06.050"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"}],"event":{"name":"2018 IEEE Sensors","start":{"date-parts":[[2018,10,28]]},"location":"New Delhi","end":{"date-parts":[[2018,10,31]]}},"container-title":["2018 IEEE SENSORS"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8572682\/8589503\/08589870.pdf?arnumber=8589870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T01:43:20Z","timestamp":1598233400000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8589870\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/icsens.2018.8589870","relation":{},"subject":[],"published":{"date-parts":[[2018,10]]}}}