ArticleACS omega2026
Random Attention Mechanism for Deep Quality Modeling and Online Prediction in Chemical Processes.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Many deep learning methods have recently been introduced for quality prediction in industrial processes, among which the deep latent variable model should be one of the most promising methods due to its simple and explainable model structure. However, how to efficiently extract features from the latent space for quality prediction remains unclear. This paper develops a random attention mechanism upon the basic deep latent variable regression modeling framework. After the latent variables have been extracted from different layers of the deep model, various random attentions are made from the pool of latent variables, based on which different submodels are constructed for the same quality prediction purpose. Then, an information fusion strategy is designed to combine the quality prediction results of different submodels. Results on a debutanizer column and a CO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.