scholarly journals Reducing noise for PIC simulations using kernel density estimation algorithm

2018 ◽  
Vol 25 (10) ◽  
pp. 102107 ◽  
Author(s):  
Wentao Wu ◽  
Hong Qin
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Algorithm

The Feature Description Method of Radar Signal Based on Improved Kernel Density Estimation

2013 ◽  
Vol 380-384 ◽  
pp. 3501-3504
Author(s):  
Fei Ye ◽  
Jie Zhou ◽  
Jun Luo ◽  
Xing Rong Gao
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Algorithm ◽  
Radar Signal ◽  
Sample Distribution ◽  
Feature Description ◽  
Feature Based ◽  
Selection Of ◽  
Probability Density Curve

According to the problem that the existing radar signal feature cannot effectively express and analysis its characteristic, a description method of radar emitter signal feature based on improved kernel density estimation is proposed. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. Then the probability density curve using kernel density estimation algorithm as radar emitter signal parameters characteristics stored into database.

Statistical Gas Distribution Modeling Using Kernel Methods

2011 ◽  
pp. 153-179 ◽  
Author(s):  
Sahar Asadi ◽  
Matteo Reggente ◽  
Cyrill Stachniss ◽  
Christian Plagemann ◽  
Achim J. Lilienthal
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Methods ◽  
Kernel Density ◽  
Estimation Algorithm ◽  
Three Dimensions ◽  
Sensor Data ◽  
Gas Distribution ◽  
Distribution Models ◽  
Gas Measurements

Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methods that statistically model gas distribution. Gas measurements are treated as random variables, and the gas distribution is predicted at unseen locations either using a kernel density estimation or a kernel regression approach. The resulting statistical models do not make strong assumptions about the functional form of the gas distribution, such as the number or locations of gas sources, for example. The major focus of this chapter is on two-dimensional models that provide estimates for the means and predictive variances of the distribution. Furthermore, three extensions to the presented kernel density estimation algorithm are described, which allow to include wind information, to extend the model to three dimensions, and to reflect time-dependent changes of the random process that generates the gas distribution measurements. All methods are discussed based on experimental validation using real sensor data.

The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree

2014 ◽  
Vol 989-994 ◽  
pp. 3689-3692
Author(s):  
Fei Ye ◽  
Xin Wang ◽  
Dong Hui Peng ◽  
Chuan Hai Jiao
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Algorithm ◽  
Parameter Analysis ◽  
Sample Distribution ◽  
Group Number ◽  
Selection Of

The optimal group is an important problem of histogram algorithm, and how to confirm group number has not a quantitative rule. So the concept of the close degree is imported to make the close degree between the upper contour line of histogram and the PDF(probability density function) of parameter as the judging criteria of optimal group. With the unknown of the PDF of parameter, the improved kernel density estimation algorithm can pre-select and estimate the PDF. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. In the parameter analysis of radar emitter signal, the algorithm based on improved kernel density estimation and close degree is used to determine optimal group, and the result indicate that this method is effective and can search the optimal group automatically.

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