ArticleEngineering structures2023
Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model.
Article in Engineering structures, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization.Sensors (Basel, Switzerland) · 2024Article
- Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.IEEE journal of translational engineering in health and medicine · 2024Article
- Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model.Engineering structures · 2023Article
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4 authors.
Funding
Abstract
Methods for identifying human activity have a wide range of potential applications, including security, event time detection, intelligent building environments, and human health. Current methodologies typically rely on either wave propagation or structural dynamics principles. However, force-based methods, such as the probabilistic force estimation and event localization algorithm (PFEEL), offer advantages over wave propagation methods by avoiding challenges such as multi-path fading. PFEEL utilizes a probabilistic framework to estimate the force of impacts and the event locations in the calibration space, providing a measure of uncertainty in the estimations. This paper presents a new implementation of PFEEL using a data-driven model based on Gaussian process regression (GPR). The new approach was evaluated using experimental data collected on an aluminum plate impacted at eighty-one points, with a separation of five centimeters. The results are presented as an area of localization relative to the actual impact location at different probability levels. These results can aid analysts in determining the required precision for various implementations of PFEEL.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.