{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:28:27Z","timestamp":1732040907893},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1016\/j.knosys.2021.107664","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T16:43:08Z","timestamp":1635784988000},"page":"107664","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":29,"special_numbering":"C","title":["Subspace alignment based on an extreme learning machine for electronic nose drift compensation"],"prefix":"10.1016","volume":"235","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-8012-5097","authenticated-orcid":false,"given":"Jia","family":"Yan","sequence":"first","affiliation":[]},{"given":"Feiyue","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuelin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoyan","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Danhong","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Shukai","family":"Duan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.knosys.2021.107664_b1","doi-asserted-by":"crossref","first-page":"19979","DOI":"10.3390\/s141119979","article-title":"Electronic noses for environmental monitoring applications","volume":"14","author":"Capelli","year":"2014","journal-title":"Sensors"},{"key":"10.1016\/j.knosys.2021.107664_b2","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.snb.2016.12.026","article-title":"Gas sensors based on membrane diffusion for environmental monitoring","volume":"243","author":"Tian","year":"2017","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b3","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.snb.2016.05.114","article-title":"Graphene based sensor for environmental monitoring of no2","volume":"236","author":"Novikov","year":"2016","journal-title":"Sensors Actuators B"},{"issue":"2","key":"10.1016\/j.knosys.2021.107664_b4","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/S0925-4005(03)00367-8","article-title":"Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach","volume":"94","author":"Dutta","year":"2003","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodres.2019.108605","article-title":"Evaluating aroma quality of black tea by an olfactory visualization system: Selection of feature sensor using particle swarm optimization","volume":"126","author":"Jiang","year":"2019","journal-title":"Food Res. Int."},{"issue":"1","key":"10.1016\/j.knosys.2021.107664_b6","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.snb.2007.09.044","article-title":"Olfactory systems for medical applications","volume":"130","author":"Amico","year":"2008","journal-title":"Sensors Actuators B"},{"issue":"23","key":"10.1016\/j.knosys.2021.107664_b7","doi-asserted-by":"crossref","first-page":"5333","DOI":"10.3390\/s19235333","article-title":"A novel framework with high diagnostic sensitivity for lung cancer detection by electronic nose","volume":"19","author":"Lu","year":"2019","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.knosys.2021.107664_b8","first-page":"57","article-title":"Feature extraction from sensor data for detection of wound pathogen based on electronic nose","volume":"24","author":"Yan","year":"2012","journal-title":"Sens. Mater."},{"key":"10.1016\/j.knosys.2021.107664_b9","doi-asserted-by":"crossref","first-page":"2172","DOI":"10.1039\/C6TA08253J","article-title":"An array of wo3 and CTO heterojunction semiconducting metal oxide gas sensors used as a tool for explosive detection","volume":"5","author":"Horsfall","year":"2017","journal-title":"J. Mater. Chem. A"},{"issue":"23","key":"10.1016\/j.knosys.2021.107664_b10","doi-asserted-by":"crossref","first-page":"5207","DOI":"10.3390\/s19235207","article-title":"Improving the chemical selectivity of an electronic nose to TNT, DNT and RDX using machine learning","volume":"19","author":"Gradi\u0161ek","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.knosys.2021.107664_b11","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.snb.2015.11.058","article-title":"Calibration transfer and drift compensation of e-noses via coupled task learning","volume":"225","author":"Yan","year":"2016","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b12","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.snb.2013.03.003","article-title":"Chaotic time series prediction of E-nose sensor drift in embedded phase space","volume":"182","author":"Zhang","year":"2013","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b13","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.compag.2015.10.005","article-title":"Study of fish species discrimination via electronic nose","volume":"119","author":"G\u00fcney","year":"2015","journal-title":"Comput. Electron. Agric."},{"issue":"1\u20132","key":"10.1016\/j.knosys.2021.107664_b14","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/S0925-4005(03)00569-0","article-title":"Drift reduction of gas sensor by wavelet and principal component analysis","volume":"96","author":"Hui","year":"2003","journal-title":"Sensors Actuators B"},{"issue":"1","key":"10.1016\/j.knosys.2021.107664_b15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.chemolab.2009.10.002","article-title":"Drift compensation of gas sensor array data by orthogonal signal correction","volume":"100","author":"Padilla","year":"2010","journal-title":"Chemometr. Intell. Lab."},{"issue":"5\u20136","key":"10.1016\/j.knosys.2021.107664_b16","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1002\/1099-128X(200009\/12)14:5\/6<711::AID-CEM607>3.0.CO;2-4","article-title":"Drift correction for gas sensors using multivariate methods","volume":"14","author":"Artursson","year":"2000","journal-title":"J. Chemometr."},{"key":"10.1016\/j.knosys.2021.107664_b17","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.snb.2016.02.131","article-title":"Calibration transfer in temperature modulated gas sensor arrays","volume":"231","author":"Fernandez","year":"2016","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b18","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.snb.2016.05.089","article-title":"Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization","volume":"236","author":"Fonollosa","year":"2016","journal-title":"Sensors Actuators B"},{"issue":"10","key":"10.1016\/j.knosys.2021.107664_b19","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Tans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2021.107664_b20","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.snb.2012.01.074","article-title":"Chemical gas sensor drift compensation using classifier ensembles","volume":"166-167","author":"Vergara","year":"2012","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b21","doi-asserted-by":"crossref","first-page":"143947","DOI":"10.1109\/ACCESS.2019.2943188","article-title":"Domain transfer broad learning system for long-term drift compensation in electronic nose systems","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2021.107664_b22","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.snb.2017.06.156","article-title":"Anti-drift in E-nose: A subspace projection approach with drift reduction","volume":"253","author":"Zhang","year":"2017","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b23","doi-asserted-by":"crossref","first-page":"170087","DOI":"10.1109\/ACCESS.2019.2955712","article-title":"Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach","volume":"7","author":"Yi","year":"2019","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.knosys.2021.107664_b24","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3390\/s18103209","article-title":"Domain correction based on kernel transformation for drift compensation in the E-nose system","volume":"18","author":"Tao","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.knosys.2021.107664_b25","doi-asserted-by":"crossref","unstructured":"Swarup. Chandra, Ahsanul. Haque, Latifur. Khan, Charu. Aggarwal, An adaptive framework for multistream classification, in: 25th ACM International on Conference on Information and Knowledge Management (CIKM 16), New York, NY, USA, 2016, pp. 1181\u20131190.","DOI":"10.1145\/2983323.2983842"},{"key":"10.1016\/j.knosys.2021.107664_b26","doi-asserted-by":"crossref","unstructured":"A. Haque, Z. Wang, S. Chandra, B. Dong, L. Khan, K.W. Hamlen, Fusion: An online method for multistream classification, in: 2017 ACM on Conference on Information and Knowledge Management (CIKM 17). New York, NY, USA, 2017, pp. 919\u2013928.","DOI":"10.1145\/3132847.3132886"},{"key":"10.1016\/j.knosys.2021.107664_b27","series-title":"20th Annual Conference on Neural Information Processing Systems","first-page":"1433","article-title":"Direct importance estimation with model selection and its application to covariate shift adaptation","author":"Sugiyama","year":"2008"},{"key":"10.1016\/j.knosys.2021.107664_b28","unstructured":"M. Pratama, M. de\u00a0Carvalho, R. Xie, E. Lughofer, J. Lu, ATL: Autonomous knowledge transfer from many streaming processes, in: 28th ACM International Conference on Information and Knowledge Management (CIKM 2019). New York, NY, USA, 2019, pp. 269\u2013278."},{"key":"10.1016\/j.knosys.2021.107664_b29","series-title":"2005 16th International Conference on Algorithmic Learning Theory","first-page":"63","article-title":"Measuring statistical dependence with Hilbert\u2013Schmidt norms","author":"Gretton","year":"2005"},{"issue":"6","key":"10.1016\/j.knosys.2021.107664_b30","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/72.471375","article-title":"Stochastic choice of basis functions in adaptive function approximation and the functional-link net","volume":"6","author":"Igelnik","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"issue":"10","key":"10.1016\/j.knosys.2021.107664_b31","doi-asserted-by":"crossref","first-page":"3466","DOI":"10.1109\/TCYB.2017.2734043","article-title":"Stochastic configuration networks: Fundamentals and algorithms","volume":"47","author":"Wang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.knosys.2021.107664_b32","series-title":"11th IAPR International Conference on Pattern Recognition","first-page":"1","article-title":"Feedforward neural networks with random weights","author":"Schmidt","year":"1992"},{"issue":"1\u20133","key":"10.1016\/j.knosys.2021.107664_b33","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"issue":"2","key":"10.1016\/j.knosys.2021.107664_b34","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.knosys.2021.107664_b35","series-title":"2013 IEEE International Conference on Computer Vision","first-page":"2200","article-title":"Transfer feature learning with joint distribution adaptation","author":"Long","year":"2013"},{"issue":"7","key":"10.1016\/j.knosys.2021.107664_b36","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1109\/TIM.2017.2669818","article-title":"Odor recognition in multiple E-nose systems with cross-domain discriminative subspace learning","volume":"66","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.knosys.2021.107664_b37","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1109\/JSEN.2017.2778012","article-title":"Improving the robustness of prediction model by transfer learning for interference suppression of electronic nose","volume":"18","author":"Liang","year":"2018","journal-title":"IEEE Sens. J."},{"issue":"1","key":"10.1016\/j.knosys.2021.107664_b38","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/JSEN.2019.2941993","article-title":"Drift compensation for an electronic nose by adaptive subspace learning","volume":"20","author":"Liu","year":"2020","journal-title":"IEEE Sens. J."},{"issue":"11","key":"10.1016\/j.knosys.2021.107664_b39","article-title":"Improving the performance of drifted\/shifted electronic nose systems by cross-domain transfer using common transfer samples","volume":"329","author":"Yi","year":"2021","journal-title":"Sensors Actuators B"},{"key":"10.1016\/j.knosys.2021.107664_b40","first-page":"1","article-title":"Neighborhood preserving and weighted subspace learning method for drift compensation in gas sensor","volume":"99","author":"Yi","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.knosys.2021.107664_b41","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s12559-017-9473-5","article-title":"Common subspace learning via cross-domain extreme learning machine","volume":"9","author":"Liu","year":"2017","journal-title":"Cogn. Comput."},{"issue":"12","key":"10.1016\/j.knosys.2021.107664_b42","first-page":"105","article-title":"Drift compensation for electronic nose based on sample distribution weighting cross domain extreme learning machine","volume":"48","author":"Yan","year":"2020","journal-title":"J. South China Univ. Technol. (Natl. Sci. Ed)."},{"issue":"15","key":"10.1016\/j.knosys.2021.107664_b43","doi-asserted-by":"crossref","first-page":"17144","DOI":"10.1109\/JSEN.2021.3081923","article-title":"Sensor drift compensation of E-nose systems with discriminative domain reconstruction based on an extreme learning machine","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.knosys.2021.107664_b44","series-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"Gong","year":"2012"},{"issue":"7","key":"10.1016\/j.knosys.2021.107664_b45","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1109\/TIM.2014.2298691","article-title":"Performance study of multilayer perceptrons in a low-cost electronic nose","volume":"63","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705121009266?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705121009266?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T16:20:05Z","timestamp":1678551605000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705121009266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":45,"alternative-id":["S0950705121009266"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2021.107664","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Subspace alignment based on an extreme learning machine for electronic nose drift compensation","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2021.107664","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107664"}}