[HTML][HTML] The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review
With the predicted depletion of natural resources and alarming environmental issues,
sustainable development has become a popular as well as a much-needed concept in …
sustainable development has become a popular as well as a much-needed concept in …
[HTML][HTML] Latent variable models in the era of industrial big data: Extension and beyond
A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …
technique in modern industry. Among various data-driven methods, latent variable models …
An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process
L Hua, C Zhang, W Sun, Y Li, J ** accurate real-time online …
Ensemble of 2D residual neural networks integrated with atrous spatial pyramid pooling module for myocardium segmentation of left ventricle cardiac MRI
Cardiac disease diagnosis and identification is problematic mostly by inaccurate
segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging …
segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging …
Deep nonlinear dynamic feature extraction for quality prediction based on spatiotemporal neighborhood preserving SAE
Complex industrial process data often exhibit nonlinear static and dynamic characteristics.
Traditional deep learning methods such as stacked autoencoder (SAE) have excellent …
Traditional deep learning methods such as stacked autoencoder (SAE) have excellent …
Multi-models and dual-sampling periods quality prediction with time-dimensional K-means and state transition-LSTM network
In most of industrial processes, there are mainly two issues: 1. working models will be
different at different time (multi-models); 2. different variables have different sampling …
different at different time (multi-models); 2. different variables have different sampling …