Variational Gaussian Process for optimal sensor placement
Applications of Mathematics
,
(in press.)
Abstract:
Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensors location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a Variational Gaussian Process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn non-linear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed-up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.
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Bibtex:
@article{Tajnafoi2021, author = {Tajnafoi, Gabor and Arcucci, Rossella and Mottet, Laetitia and Vouriot, Carolanne and Molina-Solana, Miguel and Pain, Christopher and Guo, Yike}, title = {Variational Gaussian Process for optimal sensor placement}, journal = {Applications of Mathematics}, year = {in press.}, volume = {}, doi = {}, comment = {}, timestamp = {29} }