Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Gahrooei, Mostafa Reisi; Pardalos, Panos M.; Gaw, Nathan

Springer International Publishing AG

05/2024

394

Dura

9783031530913

15 a 20 dias

Descrição não disponível.
Chapter 1: Introduction to multimodal and tensor data analytics.- Chapter 2: Functional Methods for Multimodal Data Analysis.- Chapter 3: Advanced Data Analytical Techniques for Profile Monitoring.- Chapter 4: Statistical process monitoring methods based on functional data analysis.- Chapter 5: Tensor and multimodal data analysis.- Chapter 6: Tensor Data Analytics in Advanced Manufacturing Processes.- Chapter 7: Spatiotemporal Data Analysis - A Review of Techniques, Applications, and Emerging Challenges.- Chapter 8: Offshore Wind Energy Prediction Using Machine Learning with Multi-Resolution Inputs.- Chapter 9: Sparse Decomposition Methods for Spatio-temporal Anomaly Detection.- Chapter 10: Multimodal Deep Learning.- Chapter 11: Multimodal Deep Learning for Manufacturing Systems: Recent Progress and Future Trends.- Chapter 12: Synergy of Engineering and Statistics: Multimodal data Fusion for Quality Improvement.- Chapter 13: Manufacturing data fusion: a case study with steel rollingprocesses.- Chapter 14: AI-enhanced Fault Detection using Multi-structured Data in Semiconductor Manufacturing.- Chapter 15: A Survey of Advances in Multimodal Federated Learning with Applications.- Chapter 16: Bayesian Multimodal Data Analytics: An introduction.- Chapter 17: Bayesian approach to multimodal data in human factors engineering.- Chapter 18: Bayesian Multimodal Models for Risk Analyses of Low-Probability High-Consequence Events.
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multimodal data;multivariate statistics;tensor data analytics;Bayesian multimodal;spatio-temporal data;multimodel deep learning;federated learning;distributed learning