One-Class Drift Compensation for an Electronic Nose
暂无分享,去创建一个
[1] Tao Liu,et al. Drift Compensation for an Electronic Nose by Adaptive Subspace Learning , 2020, IEEE Sensors Journal.
[2] Cheng Li,et al. Anti-Drift in Electronic Nose via Dimensionality Reduction: A Discriminative Subspace Projection Approach , 2018, IEEE Access.
[3] Changjian Deng,et al. Drift Compensation for E-Nose Using QPSO-Based Domain Adaptation Kernel ELM , 2018, ISNN.
[4] Qing Li,et al. A multi-task learning framework for gas detection and concentration estimation , 2020, Neurocomputing.
[5] Jinyong Xu,et al. Electronic nose for volatile organic compounds analysis in rice aging , 2021 .
[6] Gaëlle Lissorgues,et al. Development of Diamond and Silicon MEMS Sensor Arrays with Integrated Readout for Vapor Detection , 2017, Sensors.
[7] Robert Rusinek,et al. Application of an electronic nose for determination of pre‐pressing treatment of rapeseed based on the analysis of volatile compounds contained in pressed oil , 2019, International Journal of Food Science & Technology.
[8] Alphus D. Wilson,et al. Application of Electronic-Nose Technologies and VOC-Biomarkers for the Noninvasive Early Diagnosis of Gastrointestinal Diseases , 2018, Sensors.
[9] Lei Zhang,et al. Anti-drift in E-nose: A subspace projection approach with drift reduction , 2017 .
[10] David Zhang,et al. Calibration transfer and drift compensation of e-noses via coupled task learning , 2016 .
[11] Ricardo Izquierdo,et al. Ionization Gas Sensor Using Suspended Carbon Nanotube Beams , 2020, Sensors.
[12] Guangyin Xu,et al. Independent Component Analysis-Based Baseline Drift Interference Suppression of Portable Spectrometer for Optical Electronic Nose of Internet of Things , 2020, IEEE Transactions on Industrial Informatics.
[13] M. K. Tabatabaei,et al. High-performance immunosensor for urine albumin using hybrid architectures of ZnO nanowire/carbon nanotube. , 2020, IET nanobiotechnology.
[14] Udo Weimar,et al. On-line novelty detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions , 2006 .
[15] Tao Yang,et al. Online Drift Compensation by Adaptive Active Learning on Mixed Kernel for Electronic Noses , 2020 .
[16] Shankar Vembu,et al. Chemical gas sensor drift compensation using classifier ensembles , 2012 .
[17] David Zhang,et al. Learning Classification and Regression Models Based on Transfer Samples , 2017 .
[18] Alexandre Perera,et al. Drift compensation of gas sensor array data by Orthogonal Signal Correction , 2010 .
[20] Jiewen Zhao,et al. Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion , 2015 .
[21] Zhi-Hua Zhou,et al. Multilabel dimensionality reduction via dependence maximization , 2008, TKDD.
[22] Tao Liu,et al. Study on Interference Suppression Algorithms for Electronic Noses: A Review , 2018, Sensors.
[23] Xiaoran Wang,et al. A Multitask Learning Framework for Multi-Property Detection of Wine , 2019, IEEE Access.
[24] M. Sjöström,et al. Drift correction for gas sensors using multivariate methods , 2000 .
[25] Zijian Wang,et al. Improving the performance of drifted/shifted electronic nose systems by cross-domain transfer using common transfer samples , 2021 .
[26] Xiang Wang,et al. Performance Analysis of ICA in Sensor Array , 2016, Sensors.
[27] David Zhang,et al. Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems , 2015, IEEE Transactions on Instrumentation and Measurement.