Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Edited by Nathan Gaw (), Panos M. Pardalos () and Mostafa Reisi Gahrooei ()

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2024
ISBN: 978-3-031-53092-0
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Chapters in this book:

Introduction to Multimodal and Tensor Data Analytics

Nathan Gaw, Mostafa Reisi Gahrooei and Panos M. Pardalos

Functional Methods for Multimodal Data Analysis

Minhee Kim

Advanced Data Analytical Techniques for Profile Monitoring

Peiyao Liu and Chen Zhang

Statistical Process Monitoring Methods Based on Functional Data Analysis

Christian Capezza, Fabio Centofanti, Antonio Lepore, Alessandra Menafoglio, Biagio Palumbo and Simone Vantini

Tensor and Multimodal Data Analysis

Jing Zeng and Xin Zhang

Tensor Data Analytics in Advanced Manufacturing Processes

Bo Shen

Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging Challenges

Imtiaz Ahmed and Ahmed Shoyeb Raihan

Offshore Wind Energy Prediction Using Machine Learning with Multi-Resolution Inputs

Feng Ye, Travis Miles and Ahmed Aziz Ezzat

Sparse Decomposition Methods for Spatio-Temporal Anomaly Detection

Hao Yan, Ziyue Li, Xinyu Zhao and Jiuyun Hu

Multimodal Deep Learning

Amirreza Shaban and Safoora Yousefi

Multimodal Deep Learning for Manufacturing Systems: Recent Progress and Future Trends

Yinan Wang and Xiaowei Yue

Synergy of Engineering and Statistics: Multimodal Data Fusion for Quality Improvement

Jianjun Shi, Michael Biehler and Shancong Mou

Manufacturing Data Fusion: A Case Study with Steel Rolling Processes

Andi Wang

AI-Enhanced Fault Detection Using Multi-Structured Data in Semiconductor Manufacturing

Linus Kohl, Theresa Madreiter and Fazel Ansari

A Survey of Advances in Multimodal Federated Learning with Applications

Gregory Barry, Elif Konyar, Brandon Harvill and Chancellor Johnstone

Bayesian Multimodal Data Analytics: AnIntroduction

Marco Luigi Giuseppe Grasso and Panagiotis Tsiamyrtzis

Bayesian Approach to Multimodal Data in Human Factors Engineering

Katherina A. Jurewicz and David M. Neyens

Bayesian Multimodal Models for Risk Analyses of Low-Probability High-Consequence Events

Arda Vanli

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DOI: 10.1007/978-3-031-53092-0

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