Παρακολούθηση
Le Yao
Le Yao
Assoc. Prof. of Hangzhou Normal University, Zhejiang University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα zju.edu.cn - Αρχική σελίδα
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application
L Yao, Z Ge
IEEE Transactions on Industrial Electronics 65 (2), 1490-1498, 2017
2952017
Big data quality prediction in the process industry: A distributed parallel modeling framework
L Yao, Z Ge
Journal of Process Control 68, 1-13, 2018
1082018
Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure
B Shen, L Yao, Z Ge
Control Engineering Practice 94, 104198, 2020
1022020
Scalable semisupervised GMM for big data quality prediction in multimode processes
L Yao, Z Ge
IEEE Transactions on Industrial Electronics 66 (5), 3681-3692, 2018
902018
Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data
L Yao, Z Ge
IEEE Transactions on Automation Science and Engineering 14 (1), 126-138, 2016
802016
Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data
L Yao, Z Ge
Engineering Applications of Artificial Intelligence 81, 450-465, 2019
622019
Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis
L Yao, Z Ge
Control Engineering Practice 61, 72-80, 2017
622017
Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction
L Yao, Z Ge
IEEE Transactions on Industrial Informatics 17 (6), 3782-3792, 2020
612020
Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach
L Yao, Z Ge
IEEE Transactions on Instrumentation and Measurement 66 (8), 1985-1993, 2017
602017
Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data
W Shao, L Yao, Z Ge, Z Song
IEEE Transactions on Industrial Electronics 66 (8), 6362-6373, 2018
532018
Industrial big data modeling and monitoring framework for plant-wide processes
L Yao, Z Ge
IEEE Transactions on Industrial Informatics 17 (9), 6399-6408, 2020
502020
Virtual sensing f-CaO content of cement clinker based on incremental deep dynamic features extracting and transferring model
L Yao, X Jiang, G Huang, J Qian, B Shen, L Xu, Z Ge
IEEE Transactions on Instrumentation and Measurement 70, 1-10, 2020
472020
Refining data-driven soft sensor modeling framework with variable time reconstruction
L Yao, Z Ge
Journal of Process Control 87, 91-107, 2020
452020
Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection
H Zhang, G Qiao, S Lu, L Yao, X Chen
Textile Research Journal 93 (5-6), 1178-1195, 2023
442023
Predictive modeling with multiresolution pyramid VAE and industrial soft sensor applications
B Shen, L Yao, Z Ge
IEEE Transactions on Cybernetics 53 (8), 4867-4879, 2022
402022
Semi-supervised deep dynamic probabilistic latent variable model for multimode process soft sensor application
L Yao, B Shen, L Cui, J Zheng, Z Ge
IEEE Transactions on Industrial Informatics 19 (4), 6056-6068, 2022
372022
Hierarchical quality monitoring for large-scale industrial plants with big process data
L Yao, W Shao, Z Ge
IEEE Transactions on Neural Networks and Learning Systems 32 (8), 3330-3341, 2019
372019
Causal variable selection for industrial process quality prediction via attention-based GRU network
L Yao, Z Ge
Engineering Applications of Artificial Intelligence 118, 105658, 2023
362023
Dynamic features incorporated locally weighted deep learning model for soft sensor development
L Yao, Z Ge
IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021
362021
Bayesian nonlinear Gaussian mixture regression and its application to virtual sensing for multimode industrial processes
W Shao, Z Ge, L Yao, Z Song
IEEE Transactions on Automation Science and Engineering 17 (2), 871-885, 2019
332019
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