Conference paper
A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators
Drug Delivery and Sensing, Department of Health Technology, Technical University of Denmark1
Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Department of Health Technology, Technical University of Denmark2
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
University of Copenhagen5
Department of Health Technology, Technical University of Denmark6
Thermal analysis using resonating micro-electromechanical systems shows great promise in characterizing materials in the early stages of research. Through thermal cycles and actuation using a piezoelectric speaker, the resonant behaviour of a model drug, theophylline monohydrate, is measured across the surface whilst using a laser-Doppler vibrometer for readout.
Acquired is a sequence of spectra that are strongly correlated in time, temperature and spatial location of the readout. Traditionally, each spectrum is analyzed individually to locate the resonance peak. We propose a Bayesian model using a warped Gaussian process prior taking the correlations into account and demonstrate on both synthetic and experimental data, that it yields better estimates of both location and amplitude of the resonance peak.
Thus, the proposed model can give a more precise characterization of drugs, which is important in drug discovery and development
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 1-6 |
Proceedings: | 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing |
Series: | Ieee International Workshop on Machine Learning for Signal Processing, Mlsp |
ISBN: | 1728108241 , 172810825X , 172810825x , 9781728108247 , 9781728108254 , 1728108233 and 9781728108230 |
ISSN: | 21610363 and 15512541 |
Types: | Conference paper |
DOI: | 10.1109/MLSP.2019.8918876 |
ORCIDs: | Vording, Maximillian Fornitz , Schmidt, Mikkel Nørgaard and Alstrøm, Tommy Sonne |