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A methodology to learn acoustical responses based on limited experimental datasets is presented. From a methodological standpoint, the approach involves a multiscale-informed encoder used to cast the learning task in a finite-dimensional setting. A neural network model mapping parameters of interest to the latent variables is then constructed and calibrated using transfer learning and knowledge gained from the multiscale surrogate. The relevance of the approach is assessed by considering the prediction of the sound absorption coefficient for randomly-packed rigid spherical beads of equal diameter. A two-microphone method is used in this context to measure the absorption coefficient on a set of configurations with various monodisperse particle diameters and sample thicknesses, and a hybrid numerical approach relying on the Johnson-Champoux-Allard-Pride-Lafarge model is deployed as the multiscale-based predictor. It is shown that the strategy allows for the relationship between the micro-/structural parameters and the experimental acoustic response to be well approximated, even if a small physical dataset (comprised of ten samples) is used for training. The methodology, therefore, enables the identification and validation of acoustical models under constraints related to data limitation and parametric dependence. It also paves the way for an efficient exploration of the parameter space for acoustical materials design.
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Embedded Object Detection and Mapping in Soft Materials Using Optical Tactile Sensing
Abstract In this paper, we present a methodology that uses an optical tactile sensor for efficient tactile exploration of embedded objects within soft materials. The methodology consists of an exploration phase, where a probabilistic estimate of the location of the embedded objects is built using a Bayesian approach. The exploration phase is then followed by a mapping phase which exploits the probabilistic map to reconstruct the underlying topography of the workspace by sampling in more detail regions where there are expected to be embedded objects. To demonstrate the effectiveness of the method, we tested our approach on an experimental setup that consists of a series of quartz beads located underneath a polyethylene foam that prevents direct observation of the configuration and requires the use of tactile exploration to recover the location of the beads. We show the performance of our methodology using ten different configurations of the beads where the proposed approach is able to approximate the underlying configuration. We benchmark our results against a random sampling policy. Our empirical results show that our method outperforms the fully random policy in both the exploration and mapping phases. The exploration phase produces a better probabilistic map with fewer samples which enables an earlier transition to the mapping phase to reconstruct the underlying shape. On both the exploration and mapping phases, our proposed method presents a better consistency as compared to the random policy, with smaller standard deviation across the ten different bead configurations.
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- Award ID(s):
- 2142773
- PAR ID:
- 10497698
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- SN Computer Science
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2661-8907
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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