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A robust data reconciliation method is proposed.
The semantic reconciliation method uses three approaches (Adjustment Context, Ontology Intersection, and Semantic Alignment) to provide support for the semantic information relationships across the product design and manufacturing.
A novel robust data reconciliation method is proposed based on GT distribution and historical data, at the same time, its robustness characteristics are investigated.
The reconciliation method outlined in this paper is based on mass and energy conservation constraints for units that operate in stationary conditions.
The convective heat transfer coefficients were obtained using the data reconciliation method for two energy balance equations defined at different locations and for the power loss equation for the AC current flow of the considered switchgear.
We develop two data reconciliation (DR) methods for the validation of measurements, detection and isolation of sensors faults and reconstruction of missing data: first a static data reconciliation is developed for a hydraulic cross-structure of an irrigation canal, then a dynamic data reconciliation method is applied to take account of measurements at the upstream and downstream end of each pool.
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These observations lead to the conclusion that BPA and reconciliation methods are designed to implement different research programs based on different epistemologies.
Brooks parsimony analysis (BPA) and reconciliation methods in studies of host parasite associations differ fundamentally, despite using the same null hypothesis.
BPA is an a posteriori method that is designed to assess the host context of parasite speciation events, whereas reconciliation methods are a priori methods that are designed to fit parasite phylogenies to a host phylogeny.
Reconciliation methods may eliminate or modify input data to maximize fit of single parasite clades to a null hypothesis of cospeciation, by invoking different a priori assumptions, including a known host phylogeny.
BPA assumes coevolutionary complexity (historical contingency), relying on parsimony as an a posteriori explanatory tool to summarize complex results, whereas reconciliation methods, which embody formalized assumptions of maximum cospeciation, are based on a priori conceptual parsimony.
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