Saved in:
| Main Authors: | Zhang, Qi, Xie, Lei, Xu, Weihua, Su, Hongye |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.09524 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations
by: Risca, Diogo, et al.
Published: (2025)
by: Risca, Diogo, et al.
Published: (2025)
Three-layer deep learning network random trees for fault detection in chemical production process
by: Lu, Ming, et al.
Published: (2024)
by: Lu, Ming, et al.
Published: (2024)
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
by: Lin, Runze, et al.
Published: (2024)
by: Lin, Runze, et al.
Published: (2024)
Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
by: Pasparakis, George D., et al.
Published: (2025)
by: Pasparakis, George D., et al.
Published: (2025)
Using machine learning for fault detection in lighthouse light sensors
by: Kampouridis, Michael, et al.
Published: (2024)
by: Kampouridis, Michael, et al.
Published: (2024)
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
by: Jalayer, Reza, et al.
Published: (2024)
by: Jalayer, Reza, et al.
Published: (2024)
Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
by: Vieira, João Paulo, et al.
Published: (2025)
by: Vieira, João Paulo, et al.
Published: (2025)
Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learning
by: Lin, Runze, et al.
Published: (2025)
by: Lin, Runze, et al.
Published: (2025)
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge
by: Li, Zhongzhi, et al.
Published: (2024)
by: Li, Zhongzhi, et al.
Published: (2024)
Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
by: Calandriello, Daniele, et al.
Published: (2026)
by: Calandriello, Daniele, et al.
Published: (2026)
Online multidimensional dictionary learning
by: Addi, Ferdaous Ait, et al.
Published: (2025)
by: Addi, Ferdaous Ait, et al.
Published: (2025)
Advanced application of alkaline/basic electrolyzed water in the food and agriculture industry as cleaning, processing, preserving, and functional agents
by: Yanlin Du, et al.
Published: (2025)
by: Yanlin Du, et al.
Published: (2025)
Off-the-grid learning of mixtures from a continuous dictionary
by: Butucea, Cristina, et al.
Published: (2022)
by: Butucea, Cristina, et al.
Published: (2022)
Accurate and fast anomaly detection in industrial processes and IoT environments
by: Tonini, Simone, et al.
Published: (2024)
by: Tonini, Simone, et al.
Published: (2024)
Dual adversarial and contrastive network for single-source domain generalization in fault diagnosis
by: Li, Guangqiang, et al.
Published: (2024)
by: Li, Guangqiang, et al.
Published: (2024)
Stabilization of industrial processes with time series machine learning
by: Anoshin, Matvei, et al.
Published: (2025)
by: Anoshin, Matvei, et al.
Published: (2025)
Hard-constraint physics-residual networks enable robust extrapolation for hydrogen crossover prediction in PEM water electrolyzers
by: Kim, Yong-Woon, et al.
Published: (2025)
by: Kim, Yong-Woon, et al.
Published: (2025)
Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis
by: Wu, Wenyi, et al.
Published: (2025)
by: Wu, Wenyi, et al.
Published: (2025)
Canonical variate residual analysis for industrial processes fault detection
by: Yuting Li, et al.
Published: (2024)
by: Yuting Li, et al.
Published: (2024)
Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
by: Butucea, Cristina, et al.
Published: (2022)
by: Butucea, Cristina, et al.
Published: (2022)
Atom dimension adaptation for infinite set dictionary learning
by: Băltoiu, Andra, et al.
Published: (2024)
by: Băltoiu, Andra, et al.
Published: (2024)
Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning
by: Xu, Hongye, et al.
Published: (2024)
by: Xu, Hongye, et al.
Published: (2024)
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
by: Vashishtha, Govind, et al.
Published: (2025)
by: Vashishtha, Govind, et al.
Published: (2025)
Online LLM watermark detection via e-processes
by: Su, Weijie, et al.
Published: (2026)
by: Su, Weijie, et al.
Published: (2026)
Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimization
by: Xiang, Haotian, et al.
Published: (2025)
by: Xiang, Haotian, et al.
Published: (2025)
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
by: Vitale, Francesco, et al.
Published: (2025)
by: Vitale, Francesco, et al.
Published: (2025)
Buffer replay enhances the robustness of multimodal learning under missing-modality
by: Zhu, Hongye, et al.
Published: (2025)
by: Zhu, Hongye, et al.
Published: (2025)
Deep learning-based fault identification in condition monitoring
by: Dhungana, Hariom, et al.
Published: (2024)
by: Dhungana, Hariom, et al.
Published: (2024)
Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network
by: Kozlenko, Mykola, et al.
Published: (2025)
by: Kozlenko, Mykola, et al.
Published: (2025)
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise
by: Liao, Jing-Xiao, et al.
Published: (2024)
by: Liao, Jing-Xiao, et al.
Published: (2024)
A domain adaptation neural network for digital twin-supported fault diagnosis
by: Chen, Zhenling, et al.
Published: (2025)
by: Chen, Zhenling, et al.
Published: (2025)
A multi‐sensors nonlinear industrial process fault detection method based on Procrustes analysis transfer learning
by: Jing Wang, et al.
Published: (2026)
by: Jing Wang, et al.
Published: (2026)
TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring
by: Leszek, Simon
Published: (2025)
by: Leszek, Simon
Published: (2025)
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
by: Langdon, Becky, et al.
Published: (2026)
by: Langdon, Becky, et al.
Published: (2026)
Bayesian learning of the optimal action-value function in a Markov decision process
by: Guo, Jiaqi, et al.
Published: (2025)
by: Guo, Jiaqi, et al.
Published: (2025)
The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature
by: Li, Yuyan
Published: (2023)
by: Li, Yuyan
Published: (2023)
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
by: Donhauser, Konstantin, et al.
Published: (2024)
by: Donhauser, Konstantin, et al.
Published: (2024)
Tighter sparse variational Gaussian processes
by: Bui, Thang D., et al.
Published: (2025)
by: Bui, Thang D., et al.
Published: (2025)
Similar Items
-
Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis
by: Zhang, Qi, et al.
Published: (2024) -
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
by: Zhang, Qi, et al.
Published: (2024) -
Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations
by: Risca, Diogo, et al.
Published: (2025) -
Three-layer deep learning network random trees for fault detection in chemical production process
by: Lu, Ming, et al.
Published: (2024) -
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
by: Lin, Runze, et al.
Published: (2024)