J
Joel B. Harley
Researcher at University of Florida
Publications - 112
Citations - 1426
Joel B. Harley is an academic researcher from University of Florida. The author has contributed to research in topics: Structural health monitoring & Guided wave testing. The author has an hindex of 15, co-authored 111 publications receiving 1047 citations. Previous affiliations of Joel B. Harley include Carnegie Mellon University & University of Illinois at Urbana–Champaign.
Papers
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Journal ArticleDOI
Sparse recovery of the multimodal and dispersive characteristics of Lamb waves.
Joel B. Harley,Jose M. F. Moura +1 more
TL;DR: This paper presents a methodology referred to as sparse wavenumber analysis based on sparse recovery methods, which accurately recovers the Lamb wave's frequency-wavenumber representation with a limited number of surface mounted transducers.
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Scale transform signal processing for optimal ultrasonic temperature compensation
Joel B. Harley,Jose M. F. Moura +1 more
TL;DR: This paper presents a new methodology for optimal, stretch-based temperature compensation that operates on signals in the stretch factor and scale-transform domains that shows improved computational speed relative to other optimal methods.
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Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection
Yujie Ying,James H. Garrett,Irving J. Oppenheim,Lucio Soibelman,Joel B. Harley,Jun Shi,Yuanwei Jin +6 more
TL;DR: In this article, a multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced for damage detection in a steel pipe under environmental and operational variations.
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Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition
TL;DR: This paper develops a robust damage detection method based on singular value decomposition (SVD), and shows that the orthogonality of singular vectors ensures that the effect of damage and that of environmental and operational variations are separated into different singular vectors.
Journal ArticleDOI
Data-driven matched field processing for Lamb wave structural health monitoring
Joel B. Harley,Jose M. F. Moura +1 more
TL;DR: Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions.