Electronic nose feature extraction methods: A review J Yan, X Guo, S Duan, P Jia, L Wang, C Peng, S Zhang Sensors 15 (11), 27804-27831, 2015 | 285 | 2015 |
Volatile and nonvolatile memristive devices for neuromorphic computing G Zhou, Z Wang, B Sun, F Zhou, L Sun, H Zhao, X Hu, X Peng, J Yan, ... Advanced Electronic Materials 8 (7), 2101127, 2022 | 120 | 2022 |
A background elimination method based on wavelet transform in wound infection detection by electronic nose J Feng, F Tian, J Yan, Q He, Y Shen, L Pan Sensors and Actuators B: Chemical 157 (2), 395-400, 2011 | 49 | 2011 |
Feature extraction from sensor data for detection of wound pathogen based on electronic nose J Yan, F Tian, Q He, Y Shen, S Xu, J Feng, K Chaibou Sensors and Materials 24 (2), 57-73, 2012 | 44 | 2012 |
Improving the performance of drifted/shifted electronic nose systems by cross-domain transfer using common transfer samples R Yi, J Yan, D Shi, Y Tian, F Chen, Z Wang, S Duan Sensors and Actuators B: Chemical 329, 129162, 2021 | 38 | 2021 |
Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection J Yan, S Duan, T Huang, L Wang Sensor Review 36 (1), 23-33, 2016 | 33 | 2016 |
Detection of wound pathogen by an intelligent electronic nose F Tian, X Xu, Y Shen, J Yan, Q He, J Ma, T Liu Sens. Mater 21 (3), 155-166, 2009 | 33 | 2009 |
TDACNN: Target-domain-free domain adaptation convolutional neural network for drift compensation in gas sensors Y Zhang, S Xiang, Z Wang, X Peng, Y Tian, S Duan, J Yan Sensors and Actuators B: Chemical 361, 131739, 2022 | 31 | 2022 |
A novel extreme learning machine classification model for e-nose application based on the multiple kernel approach Y Jian, D Huang, J Yan, K Lu, Y Huang, T Wen, T Zeng, S Zhong, Q Xie Sensors 17 (6), 1434, 2017 | 31 | 2017 |
Subspace alignment based on an extreme learning machine for electronic nose drift compensation J Yan, F Chen, T Liu, Y Zhang, X Peng, D Yi, S Duan Knowledge-Based Systems 235, 107664, 2022 | 29 | 2022 |
A novel electronic nose learning technique based on active learning: EQBC-RBFNN X Jiang, P Jia, R Luo, B Deng, S Duan, J Yan Sensors and Actuators B: Chemical 249, 533-541, 2017 | 29 | 2017 |
Enhancing electronic nose performance based on a novel QPSO-KELM model C Peng, J Yan, S Duan, L Wang, P Jia, S Zhang Sensors 16 (4), 520, 2016 | 29 | 2016 |
Enhancing the discrimination ability of a gas sensor array based on a novel feature selection and fusion framework C Deng, K Lv, D Shi, B Yang, S Yu, Z He, J Yan Sensors 18 (6), 1909, 2018 | 26 | 2018 |
Classification of electronic nose data in wound infection detection based on PSO-SVM combined with wavelet transform Q He, J Yan, Y Shen, Y Bi, G Ye, F Tian, Z Wang Intelligent Automation & Soft Computing 18 (7), 967-979, 2012 | 26 | 2012 |
Sensor Drift Compensation of E-nose Systems with Discriminative Domain Reconstruction Based on an Extreme Learning Machine Z Wang, J Yan, F Chen, X Peng, Y Zhang, Z Wang, S Duan IEEE Sensors Journal 21 (15), 17144-17153, 2021 | 24 | 2021 |
A novel feature extraction approach using window function capturing and QPSO-SVM for enhancing electronic nose performance X Guo, C Peng, S Zhang, J Yan, S Duan, L Wang, P Jia, F Tian Sensors 15 (7), 15198-15217, 2015 | 24 | 2015 |
An enhanced quantum-behaved particle swarm optimization based on a novel computing way of local attractor P Jia, S Duan, J Yan Information 6 (4), 633-649, 2015 | 22 | 2015 |
Local Manifold Embedding Cross-Domain Subspace Learning for Drift Compensation of Electronic Nose Data Y Tian, J Yan, D Yi, Y Zhang, Z Wang, T Yu, X Peng, S Duan IEEE Transactions on Instrumentation and Measurement 70 (8), 2513312, 2021 | 21 | 2021 |
A drift-compensating novel deep belief classification network to improve gas recognition of electronic noses Y Tian, J Yan, Y Zhang, T Yu, P Wang, D Shi, S Duan IEEE Access 8, 121385-121397, 2020 | 20 | 2020 |
A solid trap and thermal desorption system with application to a medical electronic nose X Xu, F Tian, SX Yang, Q Li, J Yan, J Ma Sensors 8 (11), 6885-6898, 2008 | 20 | 2008 |