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Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures

Posted By: AvaxGenius
Date: 28 Mar 2021 14:44:10
Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures

Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures by Alireza Entezami
English | EPUB | 2021 | 145 Pages | ISBN : 3030662586 | 12.3 MB

This book conducts effective research on data-driven Structural Health Monitoring (SHM), and accordingly presents many novel feature extraction methods by time series analysis and signal processing, to extract reliable damage sensitive features from vibration responses. In this regard, some limitations of time series modeling are dealt with.
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Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures

Posted By: AvaxGenius
Date: 2 Feb 2021 04:45:59
Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures

Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures by Alireza Entezami
English | PDF | 2021 | 145 Pages | ISBN : 3030662586 | 5.3 MB

This book conducts effective research on data-driven Structural Health Monitoring (SHM), and accordingly presents many novel feature extraction methods by time series analysis and signal processing, to extract reliable damage sensitive features from vibration responses. In this regard, some limitations of time series modeling are dealt with.
Details

Random Graphs for Statistical Pattern Recognition

Posted By: AvaxGenius
Date: 12 Apr 2020 12:52:16
Random Graphs for Statistical Pattern Recognition

Random Graphs for Statistical Pattern Recognition by David J. Marchette
English | PDF | 2004 | 253 Pages | ISBN : 0471221767 | 9.56 MB

A timely convergence of two widely used disciplines
Random Graphs for Statistical Pattern Recognition is the first book to address the topic of random graphs as it applies to statistical pattern recognition. Both topics are of vital interest to researchers in various mathematical and statistical fields and have never before been treated together in one book. The use of data random graphs in pattern recognition in clustering and classification is discussed, and the applications for both disciplines are enhanced with new tools for the statistical pattern recognition community. New and interesting applications for random graph users are also introduced.
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