As the production demand and external environment change, the same production process
may have multiple stable working conditions, ie, multimode process. The traditional process …

This review article focuses on applying operation state detection and performance
optimization techniques in industrial electrical systems. A comprehensive literature review …

The multistep ahead prediction of crucial quality indicators is the cornerstone for optimizing
and controlling industrial processes. The accurate multistep ahead prediction over long …

Data-driven models have shown broad application prospects in soft sensor modeling.
However, numerous challenges persist. On the one hand, data-driven soft sensor methods …

Process shift of multimode process involving data distribution and dynamic relation makes
traditional transfer learning methods be intractable and even result in negative transfer. To …

Forecasting governing key factors in industrial bioprocesses is crucial for ensuring stability
and efficiency in production. However, the accurate prediction is challenged by the strong …

Modern industrial data has become increasingly nonlinear and exhibits multimode
characteristics as the scale and complexity of the industry increase, significantly impacting …

In actual industrial scenarios, different operating modes and workloads can lead to multiple
modes of working conditions, resulting in significantly diverse feature spaces. However, the …

In industrial processes, data are frequently gathered at various sampling rates, influenced by
factors such as the diverse characteristics of process variables and the use of different …

Accurate measurement of key quality variables is of great significance for evaluating product
quality and ensuring production safety. How to extract useful dynamic latent features from …