Optimal measurement methods for distributed parameter system identification

By Dariusz Ucinski. 2004

561 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/0bfd174e2823c396cc627faee2d286ee2e5870ff

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In Civil and Structural Engineering

Citations in the corpus

Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain (2010, background): “[54] The Bayesian approach to structural uncertainty honors the need for model identification, leading to a diversification of sampled lag distances [e.g., Uciński, 2005; Diggle and Lophaven, 2006; Müller, 2007].”
Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design (2012, background): “The impact or utility of a design is defined as its individual capability to reduce uncertainty associated with a prediction goal, or to maximize some related measure of data utility [e.g., Federov and Hackl, 1997; Uciński, 2005; Müller, 2007].”
On Bayesian A- and D-optimal experimental designs in infinite dimensions (2014, background): “, that of optimal experimental design (OED) (Atkinson and Donev, 1992; Uciński, 2005; Pukelsheim, 2006), is an integral part of modeling and decision making under uncertainty.”
Optimal sensor placement and motion coordination for target tracking (2006, background): “, see [10, 13].”
Optimality analysis of sensor-target localization geometries (2010, background): “maximum) design for the system parameters will minimize the volume of the uncertainty ellipsoid generatedbyC(w), is invariant under scale changes in the parameters and is invariant to linear transformations of the output (Ucinski, 2004).”
On Optimal Sensor Placement and Motion Coordination for Target Tracking (2005, background): “For example, the detFIM is the cost function in “D-optimum design” as discussed by [ 13 ].”