Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
A systematic review on imbalanced learning methods in intelligent fault diagnosis
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
[HTML][HTML] Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data
X Meng, Y Bao, J Liu, H Liu, X Zhang, Y Zhang… - International Journal of …, 2020 - Elsevier
Most studies have the achieved rapid and accurate determination of soil organic carbon
(SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial …
(SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial …
A novel deep metric learning model for imbalanced fault diagnosis and toward open-set classification
C Wang, C **n, Z Xu - Knowledge-Based Systems, 2021 - Elsevier
Intelligent fault diagnosis based on deep neural networks and big data has been an
attractive field and shows great prospects for applications. However, applications in practice …
attractive field and shows great prospects for applications. However, applications in practice …
Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects
Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …
strong feature representation capability in recent years. Nevertheless, in engineering …
Data augmentation classifier for imbalanced fault classification
The problem of fault classification in industry has been studied extensively. Most
classification algorithms are modeled on the premise of data balance. However, the difficulty …
classification algorithms are modeled on the premise of data balance. However, the difficulty …
Adaptive multimode process monitoring based on mode-matching and similarity-preserving dictionary learning
In real industrial processes, factors, such as the change in manufacturing strategy and
production technology lead to the creation of multimode industrial processes and the …
production technology lead to the creation of multimode industrial processes and the …
Few-shot cotton leaf spots disease classification based on metric learning
X Liang - Plant Methods, 2021 - Springer
Background Cotton diceases seriously affect the yield and quality of cotton. The type of pest
or disease suffered by cotton can be determined by the disease spots on the cotton leaves …
or disease suffered by cotton can be determined by the disease spots on the cotton leaves …
Instance weighted SMOTE by indirectly exploring the data distribution
A Zhang, H Yu, S Zhou, Z Huan, X Yang - Knowledge-Based Systems, 2022 - Elsevier
The synthetic minority oversampling technique (SMOTE) algorithm is considered a
benchmark algorithm for addressing the class imbalance learning (CIL) problem. However …
benchmark algorithm for addressing the class imbalance learning (CIL) problem. However …
An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling
X Gao, B Ren, H Zhang, B Sun, J Li, J Xu, Y He… - Expert Systems with …, 2020 - Elsevier
In many real-world applications classification problems suffer from class-imbalance. The
classification methods for imbalanced data with only data processing or algorithm …
classification methods for imbalanced data with only data processing or algorithm …