12 Papers
3 Citations
Li Tian is an academic researcher from East China University of Science and Technology. The author has contributed to research in topics: Computer science & Autoencoder. The author has an hindex of 3, co-authored 6 publications.
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Papers
Distributed-ensemble stacked autoencoder model for non-linear process monitoring
TL;DR: The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information, and can explain the monitoring results better.
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Fault Diagnostic Method Based on Deep Learning and Multimodel Feature Fusion for Complex Industrial Processes
TL;DR: Fault diagnostic methods based on deep learning for industrial processes are becoming a research hotspot and efforts are being made to establish a single standard for this type of analysis.
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Differential evolution algorithm directed by individual difference information between generations and current individual information
Li Tian,Zhichao Li,Xuefeng Yan +2 more
TL;DR: DI-DE is compared with 28 excellent algorithms on three well-known benchmark sets of low dimensionality and one large scale benchmarks set (CEC LSGO 2013) and experimental results demonstrate the competitive performance of DI-DE.
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Dynamic nonlinear process monitoring based on dynamic correlation variable selection and kernel principal component regression
TL;DR: In this paper , a dynamic nonlinear process monitoring method based on dynamic non-linear feature selection and kernel principal component regression (KPCR) is proposed to further improve the monitoring performance for dynamic non linear processes, establishing a nonlinear filtering model for each variable is necessary.
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An ensemble framework based on multivariate statistical analysis for process monitoring
Zhichao Li,Li Tian,Xuefeng Yan +2 more
TL;DR: In this article , an ensemble monitoring framework is proposed to automatically determine the local models and the optimal monitoring variables, where multiple models that describe the complex characteristics of process data from different aspects can be automatically determined to establish an Ensemble monitoring model based on the various characteristics of the process data.
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