Virtual sample generation for small sample learning: a survey, recent developments and future prospects
J Wen, A Su, X Wang, H Xu, J Ma, K Chen, X Ge, Z Xu… - Neurocomputing, 2024 - Elsevier
Virtual sample generation (VSG) technology aims to generate virtual samples based on real
samples, in order to expand the size of the datasets and improve model performance …
samples, in order to expand the size of the datasets and improve model performance …
[HTML][HTML] Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge
B Zong, J Li, T Yuan, J Wang, R Yuan - Journal of Materiomics, 2024 - Elsevier
One key challenge in materials informatics is how to effectively use the material data of small
size to search for desired materials from a huge unexplored material space. We review the …
size to search for desired materials from a huge unexplored material space. We review the …
Product quality prediction method in small sample data environment
F Liu, Y Dai - Advanced Engineering Informatics, 2023 - Elsevier
The low degree of enterprise digitization and the existence of personalized customization
and small batch production manufacturing modes lead to the characteristics of small …
and small batch production manufacturing modes lead to the characteristics of small …
A multiphase information fusion strategy for data-driven quality prediction of industrial batch processes
As one of the most important modes of industrial production, the batch process often
involves complex and continuous physicochemical reactions, making it challenging to …
involves complex and continuous physicochemical reactions, making it challenging to …
A multimode mechanism-guided product quality estimation approach for multi-rate industrial processes
Discrete and delayed laboratory analyses of product quality restrict the operational
optimization of industrial processes. However, it is challenging to build an accurate online …
optimization of industrial processes. However, it is challenging to build an accurate online …
[HTML][HTML] A feature restoration for machine learning on anti-corrosion materials
Materials informatics often struggles with small datasets. Our study introduces the Gaussian
Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature …
Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature …
A multimode structured prediction model based on dynamic attribution graph attention network for complex industrial processes
Complex industrial processes with dynamic and time-varying characteristics, as well as
diverse operating conditions, pose challenges in developing accurate real-time online …
diverse operating conditions, pose challenges in developing accurate real-time online …
Augmented industrial data-driven modeling under the curse of dimensionality
The curse of dimensionality refers to the problem of increased sparsity and computational
complexity when dealing with high-dimensional data. In recent years, the types and …
complexity when dealing with high-dimensional data. In recent years, the types and …
A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors
This paper presents a quantitative structure–property relationship (QSPR)-based machine
learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values …
learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values …
[HTML][HTML] Machine learning for industrial sensing and control: A survey and practical perspective
With the rise of deep learning, there has been renewed interest within the process industries
to utilize data on large-scale nonlinear sensing and control problems. We identify key …
to utilize data on large-scale nonlinear sensing and control problems. We identify key …