Journal ArticleDOI
Semi-supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach
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This article is published in Aiche Journal.The article was published on 2019-03-01. It has received 29 citations till now. The article focuses on the topics: Latent variable model & Probabilistic logic.read more
Citations
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Evolutionary optimization based pseudo labeling for semi-supervised soft sensor development of industrial processes
TL;DR: Two novel semi-supervised soft sensor methods, namely evolutionary optimization based pseudo labeling method (EOPL) and ensemble EOPL method (EnE OPL), are proposed, which are superior to traditional pseudo-labeling style semi- supervised methods.
Journal ArticleDOI
Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis
Xinrui Gao,Yuri A.W. Shardt +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a long-term dependency slow feature analysis (LTSFA) to understand the longer-term dynamics by an explicit expression of latent states of the process.
Journal ArticleDOI
Semi-supervised data modeling and analytics in the process industry: Current research status and challenges
TL;DR: The semi-supervised data structure is introduced, including the causes, main feature, and its effects on data modeling and applications in the process industry and several challenges and promising issues on modeling and application of semi- supervised data are discussed.
Journal ArticleDOI
A Holistic Probabilistic Framework for Monitoring Nonstationary Dynamic Industrial Processes
TL;DR: A novel nonstationary probabilistic slow feature analysis algorithm is developed to comprehensively describe both non stationary and stationary variations that underlie process measurements during routine operations and forms a holistic and pragmatic monitoring framework for industrial processes.
Journal ArticleDOI
A Generalized Probabilistic Monitoring Model with Both Random and Sequential Data
Wan Yu,Min Wu,Biao Huang,Chen Lu +3 more
TL;DR: Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions.
References
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Maximum likelihood from incomplete data via the EM algorithm
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Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.
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Independent component analysis, a new concept?
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).
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PLS-regression: a basic tool of chemometrics
TL;DR: PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.