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Multimodal and Tensor Data Analytics for Industrial Systems Improvement

This volume covers the latest methodologies for using multimodal data fusion and analytics across several applications. The curated content presents recent developments and challenges in multimodal data analytics and shines a light on a pathway toward new research developments. Chapters are composed...

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Bibliographic Details
Other Authors: Gaw, Nathan (Editor), Pardalos, Panos M. (Editor), Gahrooei, Mostafa Reisi (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2024, 2024
Edition:1st ed. 2024
Series:Springer Optimization and Its Applications
Subjects:
Data Analysis And Big Data
Optimization
Statistics 
Quantitative Research
Statistics
Mathematical Optimization
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • 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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