The intensive research and development efforts directed towards large-scale complex
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …

Surface defect detection is a complicated task to achieve both specific class and precise
location of each defect. Specifically for industrial scenario, realizing efficient and accuracy …

Industrial process data have the characteristics of less label, multimode, high dimension,
containing noise, and mixing with outliers, which increase the difficulty of mode identification …

In this paper, a self-organizing fuzzy neural network with hierarchical pruning scheme
(SOFNN-HPS) is proposed for nonlinear systems modelling in industrial processes. In …

In order to improve the production efficiency and reduce the production cost of copper
removal process (CRP), it is necessary to control the addition rate of zinc powder in CRP. In …

Data-driven soft modeling has been extensively used for industrial processes to estimate
key quality indicators which are hard to measure by some physical devices. However, the …

Machine learning techniques have been successfully utilized as effective soft sensing for
industrial processes. Unfortunately, as modern industrial processes become increasingly …

Deep learning models are increasingly being used as effective techniques for industrial
process modeling. However, decisions generated from deep learning models can hardly to …

The aluminum fluoride (AF) addition in aluminum electrolysis process (AEP) can directly
influence the current efficiency, energy consumption, and stability of the process. This paper …

The analytical structure of fuzzy controller is important for better understanding and effective
design of the fuzzy control system, especially for explaining why fuzzy control system work …