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Spatio-Temporal Learning and Monitoring for Complex Dynamic Processes with Irregular Data

  • 1st Edition - July 25, 2025
  • Authors: Chunhui Zhao, Wanke Yu
  • Language: English
  • Paperback ISBN:
    9 7 8 - 0 - 4 4 3 - 3 3 6 7 5 - 1
  • eBook ISBN:
    9 7 8 - 0 - 4 4 3 - 3 3 6 7 6 - 8

Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregu… Read more

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Elsevier academics book covers
Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.

The book not only discusses the complex models but also their real-world applications in industry.

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