A5003088128- Machine Learning and ELM
- Non-Destructive Testing Techniques
- Advanced Algorithms and Applications
- Advanced Sensor and Control Systems
- Fault Detection and Control Systems
- Industrial Technology and Control Systems
- Extracellular vesicles in disease
- Smart Materials for Construction
- Mineral Processing and Grinding
Tongji University
2024
Yunnan University
2021-2022
Abstract As a classical machine learning technique, the kernel autoencoder (KAE) algorithm exhibits outstanding capabilities of nonlinear data reconstruction, making it highly suitable for industrial process monitoring and early fault detection. However, its detection rate (FDR) will decrease significantly when KAE is applied to complex with dynamic characteristics. In response this challenge, novel adaptive graph embedded (ADG-KAE) proposed in paper, which representation based approach....
The conventional semi-supervised extreme learning machine (SS-ELM) algorithm can provide a solution to the lack of labeled samples in wind turbine blade icing fault detection, but its performance is limited by irrationality spherical nearest neighbor graph (SNNG) calculation strategy. To solve this problem, novel ellipsoidal (ESS-ELM) proposed paper and applied detection. In study, we creatively propose (ENNG) strategy that considers distribution information construct ESS-ELM algorithm....
The extreme learning machine-autoencoder (ELM-AE) algorithm has attracted significant attention with regards to the online monitoring and fault detection of industrial process in recent years. However, ELM-AE generally observes increased false alarm rate (FAR) when it is applied complex time-varying characteristics. To solve this problem, a novel adaptive (AELM-AE) for proposed paper. AELM-AE implemented by embedding approximate linear dependence (ALD) method into conventional algorithm. ALD...