M
Michael A. Schuh
Researcher at Montana State University
Publications - 36
Citations - 423
Michael A. Schuh is an academic researcher from Montana State University. The author has contributed to research in topics: Search engine indexing & iDistance. The author has an hindex of 12, co-authored 36 publications receiving 359 citations. Previous affiliations of Michael A. Schuh include Georgia State University.
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Journal ArticleDOI
Multivariate time series dataset for space weather data analytics
Rafal A. Angryk,Petrus C. Martens,Berkay Aydin,Dustin J. Kempton,Sushant S. Mahajan,Sunitha Basodi,Azim Ahmadzadeh,Xumin Cai,Soukaina Filali Boubrahimi,Shah Muhammad Hamdi,Michael A. Schuh,Manolis K. Georgoulis,Manolis K. Georgoulis +12 more
TL;DR: A comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series is introduced and made openly accessible.
Proceedings ArticleDOI
A large-scale solar image dataset with labeled event regions
TL;DR: This paper introduces a new public benchmark dataset of solar image data from the Solar Dynamics Observatory (SDO) mission, which contains over 15,000 images and nearly 24,000 solar events spanning the first six months of 2012.
Proceedings ArticleDOI
Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions
TL;DR: This work proposes a set of measures to identify spatio-temporal co-occurring patterns and proposes an Apriori-based spatIO-tem temporal co- Occurrence mining algorithm to find prevalent spatio’s temporal representations for extended spatial representations that evolve over time.
Journal ArticleDOI
A Comparative Evaluation of Automated Solar Filament Detection
Michael A. Schuh,Juan M. Banda,Pietro N. Bernasconi,Rafal A. Angryk,Petrus C. Martens,Petrus C. Martens +5 more
TL;DR: The results confirm the reliable event reporting of the AAFDCC module and establishes the TFR module’s ability to effectively detect solar filaments in Hα solar images.
Proceedings ArticleDOI
Graph-based ontology-guided data mining for D-matrix model maturation
TL;DR: A maturation approach is proposed which uses the graph-theoretic representations of Timed Failure Propagation Graph models and diagnostic sessions based on recently standardized diagnostic ontologies to determine statistical discrepancies between that which is expected by the models and that which has been encountered in practice.