Improved Sine-Fitting Algorithms for Measurements of Complex Ratio of AC Voltages by Asynchronous Sequential Sampling

2019 ◽  
Vol 68 (6) ◽  
pp. 1659-1665 ◽  
Author(s):  
Jerzy Augustyn ◽  
Marian Kampik
Keyword(s):  
Sequential Sampling ◽  
Complex Ratio

Evaluation of bag sequential sampling technique for ambient air analysis. Comments

1979 ◽  
Vol 13 (5) ◽  
pp. 609-610
Author(s):  
Allen Hoffman ◽  
Roger Sperling ◽  
John Polasek ◽  
Jerry Bullin
Keyword(s):  
Sequential Sampling ◽  
Sampling Technique ◽  
Ambient Air ◽  
Air Analysis

A Robust Error-Pursuing Sequential Sampling Approach for Global Metamodeling Based on Voronoi Diagram and Cross Validation

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Shengli Xu ◽  
Haitao Liu ◽  
Xiaofang Wang ◽  
Xiaomo Jiang
Keyword(s):  
Engineering Design ◽  
Voronoi Diagram ◽  
Cross Validation ◽  
Sequential Sampling ◽  
Voronoi Cell ◽  
Sensitive Region ◽  
Engineering Design Problems ◽  
Simulation Based ◽  
Global Metamodeling ◽  
Sampling Approach

Surrogate models are widely used in simulation-based engineering design and optimization to save the computing cost. The choice of sampling approach has a great impact on the metamodel accuracy. This article presents a robust error-pursuing sequential sampling approach called cross-validation (CV)-Voronoi for global metamodeling. During the sampling process, CV-Voronoi uses Voronoi diagram to partition the design space into a set of Voronoi cells according to existing points. The error behavior of each cell is estimated by leave-one-out (LOO) cross-validation approach. Large prediction error indicates that the constructed metamodel in this Voronoi cell has not been fitted well and, thus, new points should be sampled in this cell. In order to rapidly improve the metamodel accuracy, the proposed approach samples a Voronoi cell with the largest error value, which is marked as a sensitive region. The sampling approach exploits locally by the identification of sensitive region and explores globally with the shift of sensitive region. Comparative results with several sequential sampling approaches have demonstrated that the proposed approach is simple, robust, and achieves the desired metamodel accuracy with fewer samples, that is needed in simulation-based engineering design problems.

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