Search results for "Bayesian optimisation"

showing 2 items of 2 documents

Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis

2020

The use of robotics is beginning to play a key role in automating the data collection process in Non Destructive Testing (NDT). Increasing the use of automation quickly leads to the gathering of large quantities of data, which makes it inefficient, perhaps even infeasible, for a human to parse the information contained in them. This paper presents a solution to this problem by making the process of NDT data acquisition an autonomous one as opposed to an automatic one. In order to achieve this, the robotic data acquisition task is treated as an optimisation problem, where one seeks to find locations with the highest indication of damage. The resulting algorithm combines damage detection tech…

0209 industrial biotechnologyComputer scienceTKAerospace Engineering02 engineering and technologycomputer.software_genre01 natural sciencesField (computer science)Settore ING-IND/14 - Progettazione Meccanica E Costruzione Di Macchine020901 industrial engineering & automationData acquisitionNon-destructive testing (NDT)0103 physical sciencesUltrasoundUncertainty quantificationOutlier analysis010301 acousticsCivil and Structural EngineeringData collectionbusiness.industryMechanical EngineeringProbabilistic logicBayesian optimisationAutomationComputer Science ApplicationsControl and Systems EngineeringSignal ProcessingOutlierStructural health monitoringData miningbusinesscomputerGaussian process (GP) regression
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Efficient spatial designs using Hausdorff distances and Bayesian optimization

2021

An iterative Bayesian optimisation technique is presented to find spatial designs of data that carry much information. We use the decision theoretic notion of value of information as the design criterion. Gaussian process surrogate models enable fast calculations of expected improvement for a large number of designs, while the full-scale value of information evaluations are only done for the most promising designs. The Hausdorff distance is used to model the similarity between designs in the surrogate Gaussian process covariance representation, and this allows the suggested algorithm to learn across different designs. We study properties of the Bayesian optimisation design algorithm in a sy…

Statistics and ProbabilityHausdorff distancebayesilainen menetelmäBayesian optimizationHausdorff spacepäätöksentukijärjestelmätBayesian optimisationpaikkatietoanalyysivalue of informationValue of informationHausdorff distanceoptimointiStatistics Probability and UncertaintyAlgorithmMathematics
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