Real-time state estimation using recurrent neural network and topological data analysis
dc.contributor.author | Razmarashooli, Arman | |
dc.contributor.author | Martinez, Daniel A. Salazar | |
dc.contributor.author | Chua, Yang Kang | |
dc.contributor.author | Laflamme, Simon | |
dc.contributor.author | Hu, Chao | |
dc.contributor.department | Civil, Construction and Environmental Engineering | |
dc.contributor.department | Electrical and Computer Engineering | |
dc.date.accessioned | 2024-05-31T13:55:51Z | |
dc.date.available | 2024-05-31T13:55:51Z | |
dc.date.issued | 2024-05-09 | |
dc.description.abstract | High-rate systems are defined as physical systems that undergo large perturbations, often exceeding 100 g’s, over very short durations, often less than 100 milliseconds. Examples include blast mitigation mechanisms and advanced weaponry. The use of control feedback to empower high-rate systems requires the capability to estimate system states of interest in the realm of microseconds. However, due to the dynamics of these high-rate systems being highly nonlinear and nonstationary, it is challenging to predict their behavior using conventional state estimation methods. To address this issue, we conduct a study that explores the integration of topological data analysis (TDA) and recurrent neural network (RNN) to improve predictive capabilities for high-rate systems. Here, TDA features are used as the input to a machine learning algorithm to determine the state of a high-rate system. We conduct practical evaluations using laboratory datasets from experiments in the dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR), focusing on localizing fast-changing boundary conditions on a cantilever beam. The study demonstrates the ability of the method to classify and predict a system’s fundamental frequencies. This approach helps understand the structure of the underlying high-rate dynamics, leading to improved accuracy and precision in state estimation and prediction. | |
dc.description.comments | This proceeding is published as Razmarashooli, Arman, Daniel A. Salazar Martinez, Yang Kang Chua, Simon Laflamme, and Chao Hu. "Real-time state estimation using recurrent neural network and topological data analysis." In Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII, vol. 12950, pp. 106-114. SPIE, 2024. doi: https://doi.org/10.1117/12.3010900. © 2024 SPIE. Posted with Permission. | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/VrO5menw | |
dc.language.iso | en | |
dc.publisher | Society of Photographic Instrumentation Engineers (SPIE) | |
dc.source.uri | https://doi.org/10.1117/12.3010900 | * |
dc.subject.disciplines | DegreeDisciplines::Engineering::Electrical and Computer Engineering::Systems and Communications | |
dc.subject.disciplines | DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences | |
dc.subject.disciplines | DegreeDisciplines::Physical Sciences and Mathematics::Computer Sciences::Theory and Algorithms | |
dc.subject.keywords | Structural health monitoring | |
dc.subject.keywords | High-rate systems | |
dc.subject.keywords | Nonlinear time series | |
dc.subject.keywords | Topological data analysis | |
dc.subject.keywords | Recurrent neural network | |
dc.subject.keywords | Ensemble learning | |
dc.title | Real-time state estimation using recurrent neural network and topological data analysis | |
dc.type | Presentation | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 84547f08-8710-4934-b91e-ba5f46ab9abe | |
relation.isOrgUnitOfPublication | 933e9c94-323c-4da9-9e8e-861692825f91 | |
relation.isOrgUnitOfPublication | a75a044c-d11e-44cd-af4f-dab1d83339ff |
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