{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:16:35Z","timestamp":1780316195745,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer\u2014a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model\u2019s generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques.<\/jats:p>","DOI":"10.3390\/s24041319","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T03:18:38Z","timestamp":1708312718000},"page":"1319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Huichao","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijie","family":"Song","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3051-135X","authenticated-orcid":false,"given":"Jianwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,18]]},"reference":[{"key":"ref_1","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":"ref_2","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.snb.2009.10.006","article-title":"Compensation for the drift-like terms caused by environmental fluctuations in the responses of chemoresistive gas sensors","volume":"143","author":"Ghafarinia","year":"2010","journal-title":"Sens. Actuators Chem."},{"key":"ref_3","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":"Sens. Actuators B"},{"key":"ref_4","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":"Damico","year":"2008","journal-title":"Sens. Actuators B Chem."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lu, B., Fu, L., Nie, B., Peng, Z., and Liu, H. (2019). A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose. Sensors, 19.","DOI":"10.3390\/s19235333"},{"key":"ref_6","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":"ref_7","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":"Sens. Actuators B Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108605","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."},{"key":"ref_9","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":"2016","journal-title":"J. Mater. Chem. A"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gradi\u0161ek, A., van Midden, M., Koterle, M., Prezelj, V., Strle, D., \u0160tefane, B., Brodnik, H., Trifkovi\u010d, M., Kvasi\u0107, I., and Zupani\u010d, E. (2019). Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning. Sensors, 19.","DOI":"10.3390\/s19235207"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1109\/TIM.2003.814362","article-title":"Tin oxide gas sensing: Comparison among different measurement techniques for gas mixture classification","volume":"52","author":"Fort","year":"2003","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/TIM.2004.831500","article-title":"A low-cost interface to high-value resistive sensors varying over a wide range","volume":"53","author":"Flammini","year":"2004","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/TIM.2008.917189","article-title":"Electronic nose for black tea classification and correlation ofmeasurements with tea taster marks","volume":"57","author":"Bhattacharyya","year":"2008","journal-title":"IEEE Trans. Instrum."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, S., Cheng, X., Cheng, H., and Zhang, H. (2017, January 10\u201311). Gas Sensor Drift Compensation by an Optimal Linear Transformation. Proceedings of the 3rd International Conference on Big Data Computing and Communications (BIGCOM), Chengdu, China.","DOI":"10.1109\/BIGCOM.2017.64"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/JSEN.2004.837495","article-title":"Evaluation of an electronic nose to assess fruit ripeness","volume":"5","author":"Brezmes","year":"2005","journal-title":"IEEE Sens. J."},{"key":"ref_16","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. Chemom."},{"key":"ref_17","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":"Chemom. Intell. Lab. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cai, X., Wang, X., Huang, Z., and Wang, F. (2016). Performance Analysis of ICA in Sensor Array. Sensors, 16.","DOI":"10.3390\/s16050637"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.snb.2009.11.034","article-title":"Drift compensation of gas sensor array data by common principal component analysis","volume":"146","author":"Ziyatdinov","year":"2010","journal-title":"Sens. Actuators B Chem."},{"key":"ref_20","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":"Sens. Actuators B Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/TSMC.2020.2997922","article-title":"Local Discriminant Subspace Learning for Gas Sensor Drift Problem","volume":"52","author":"Yi","year":"2020","journal-title":"IEEE Trans. Syst. Man. Cybern. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Se, H., Song, K., Liu, H., Zhang, W., Wang, X., and Liu, J. (2023). A dual drift compensation framework based on subspace learning and cross-domain adaptive extreme learning machine for gas sensors. Knowl. Syst., 259.","DOI":"10.1016\/j.knosys.2022.110024"},{"key":"ref_23","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\u2013167","author":"Vergara","year":"2012","journal-title":"Sens. Actuators B Chem."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TIM.2014.2367775","article-title":"Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems","volume":"64","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"128065","DOI":"10.1016\/j.snb.2020.128065","article-title":"Online Drift Compensation by Adaptive Active Learning on Mixed Kernel for Electronic Noses","volume":"316","author":"Liu","year":"2020","journal-title":"Sens. Actuators B Chem."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, T., Li, D., Chen, J., Chen, Y., Yang, T., and Cao, J. (2018). Gas-Sensor Drift Counteraction with Adaptive Active Learning for an Electronic Nose. Sensors, 18.","DOI":"10.3390\/s18114028"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cao, J., Liu, T., Chen, J., Yang, T., Zhu, X., and Wang, H. (2021). Drift Compensation on Massive Online Electronic-Nose Responses. Chemosensors, 9.","DOI":"10.3390\/chemosensors9040078"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TNNLS.2020.3028503","article-title":"A Review of Single-Source Deep Unsupervised Visual Domain Adaptation","volume":"33","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","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":"Sens. Actuators B Chem."},{"key":"ref_30","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":"Sens. Actuators B Chem."},{"key":"ref_31","unstructured":"Gong, B., Shi, Y., Sha, F., and Grauman, K. (2012, January 16\u201321). Geodesic flow kernel for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_32","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":"2010","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., and Yu, P.S. (2013, January 1\u20138). Transfer feature learning with joint distribution adaptation. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref_34","first-page":"1","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","unstructured":"Yang, T., Kewei, Z., and Zhifang, L. (2020, January 9\u201311). Drift compensation algorithm based on TimeWasserstein dynamic distribution alignment. Proceedings of the IEEE\/CIC International Conference on Communications in China (ICCC), Virtual Conference."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T. (2017, January 21\u201326). Adversarial Discriminative Domain Adaptation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.316"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:01:44Z","timestamp":1760104904000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,18]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041319"],"URL":"https:\/\/doi.org\/10.3390\/s24041319","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,18]]}}}