Using PT-Kriging Method for Stress Wave Three Dimensional Imaging of Wood Internal Defects

In order to improve the three-dimensional imaging accuracy of stress wave in wood internal defects detection, a three-dimensional stress wave imaging method of wood internal defects based on PT-Kriging (Particle Swarm Optimization Top-k Kriging) is proposed. Based on the ordinary Kriging interpolation, the algorithm is used to fit the mutation function by Particle Swarm Optimization (PSO) optimization algorithm. At the same time, the Top-k query is introduced to find the k known points in the neighborhood so that the high precision of the fitting and the global optimization of the parameters can be realized. Compared with the Kriging algorithm, the algorithm has higher imaging accuracy and can reflect the characteristics of wood internal defects more accurately.

[1]  Xinyu Shao,et al.  Optimization of welding process parameters by combining Kriging surrogate with particle swarm optimization algorithm , 2016 .

[2]  Huihui Yu,et al.  Three-Dimensional Short-Term Prediction Model of Dissolved Oxygen Content Based on PSO-BPANN Algorithm Coupled with Kriging Interpolation , 2016 .

[3]  Amel Grissa Touzi,et al.  Intelligent top k query answering using meta-data base , 2016, Intell. Decis. Technol..

[4]  Robert J. Ross,et al.  Analysis of wave velocity patterns in black cherry trees and its effect on internal decay detection , 2014 .

[5]  Stephen P. Hubbell,et al.  Use of sonic tomography to detect and quantify wood decay in living trees , 2016, Applications in Plant Sciences.

[6]  Kleijnen,et al.  Tilburg University Regression and Kriging Metamodels with Their Experimental Designs in Simulation , 2015 .

[7]  Xiping Wang,et al.  Stress wave velocity patterns in the longitudinal-radial plane of trees for defect diagnosis , 2016, Comput. Electron. Agric..

[8]  Hiroyuki Kitagawa,et al.  Top-k Outlier Detection from Uncertain Data , 2014, Int. J. Autom. Comput..

[10]  J. Song,et al.  Incorporation of parameter uncertainty into spatial interpolation using Bayesian trans-Gaussian kriging , 2015, Advances in Atmospheric Sciences.

[11]  K. Thammi Reddy,et al.  Top-k Closed Sequential Graph Pattern Mining , 2016 .

[12]  Xiping Wang,et al.  Tomographic Image Reconstruction Using an Interpolation Method for Tree Decay Detection , 2014 .

[13]  Jia Tang,et al.  Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques , 2017 .

[14]  Robert J. Ross,et al.  Identifying bacterially infected oak by stress wave nondestructive evaluation , 1992 .