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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Cyber-Physical Data Fusion & Threat Detection with LSTM-Based Autoencoders in the Grid
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Supporting Documentation for "An Exploratory Data Mining Study of Re-entry Events: Foundations for Multi-Modality Sensing and Data Fusion"
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Vibro-acoustic modulation and data fusion for localizing alkali–silica reaction–induced damage in concrete
This article investigates the application of vibro-acoustic modulation testing for diagnosing damage in concrete structures. The vibro-acoustic modulation technique employs two excitation frequencies on a structure. The interaction of these excitations in the measured response indicates damage through the presence of sidebands in the frequency spectra. Past studies using this technique have mostly focused on metals and composites (thin plates or laminates). Our research focuses on concrete, which is a highly heterogeneous material susceptible to a variety of chemical, physical, and mechanical damage processes. In particular, this article investigates diagnosing cracking in concrete from an expansive gel produced by an alkali–silica reaction in the presence of moisture. Past studies have been limited to damage detection using vibro-acoustic modulation testing, whereas this article extends the technique to damage localization. A cement slab with pockets of reactive aggregate is used to investigate the diagnosis technique. The effects of different testing parameters, such as locations, magnitudes, and frequencies of the two excitations, are analyzed and incorporated in the damage localization methodology. A Bayesian probabilistic methodology is developed to fuse the information from multiple test configurations in order to construct damage probability maps for the test specimen. The results of vibro-acoustic modulation–based damage localization are validated by petrographic study of cores taken from the slab.
Introduction to the Special Section on Seismoacoustics and Seismoacoustic Data Fusion
This special section of the Bulletin of the Seismological Society of America provides a broad overview on recent advances to the understanding of the seismoacoustic wavefield through 19 articles. Leveraging multiphenomenology datasets is instrumental for the continued success of future planetary missions, nuclear test ban treaty verification, and natural hazard monitoring. Progress in our theoretical understanding of mechanical coupling, advancements in coupled-media wave modeling, and developments of efficient multitechnology inversion procedures are key to fully exploiting geophysical datasets on Earth and beyond. We begin by highlighting papers describing experimental setups and instrumentation, followed by characterization of natural and anthropogenic sources of interest, and ending in new open-access datasets. Finally, we conclude with an overview of challenges that remain as well as some potential directions for future investigation within the growing multidisciplinary field of seismoacoustics.
Data Fusion and Disease Forecasting for Dengue in Brazil [Slides]
Abstract not provided.
Seismo-acoustic Data Fusion: Determining the Best Acquisition Designs for Multi-Phenomenological Monitoring Campaigns.
Abstract not provided.
Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics.
Abstract not provided.
Multifidelity data fusion in convolutional encoder/decoder assembly networks for computational fluid dynamics.
Abstract not provided.
Multifidelity Data Fusion in Convolutional Encoder/Decoder Networks.
Abstract not provided.
Uncertainty-Based Information Theory for Heterogenous Data Fusion [Slides]
Abstract not provided.
Transferring Properties of Analogous Datasets Through Data-Informed Latent Spaces Using Heterogeneous Metaphoric Data Fusion
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Atmospheric Disturbance Classification and Estimation Using Novel Sensors and Sensor Data Fusion
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Autoencoders for Multimodal Data Fusion
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