Implementation and comparison of least-square method and thin-plate spline method for PWR online power-distribution monitoring based on NECP-ONION system

2020 ◽  
Vol 365 ◽  
pp. 110712
Author(s):  
Zhuo Li ◽  
Mingtao He ◽  
Liangzhi Cao ◽  
Hongchun Wu ◽  
Wei Shen ◽  
...  
Keyword(s):  
Thin Plate ◽  
Power Distribution ◽  
Least Square Method ◽  
Thin Plate Spline ◽  
Least Square ◽  
Spline Method

Thermal investigations of microelectronic chip with non‐uniform power distribution: temperature prediction and thermal placement design optimization

2004 ◽  
Vol 21 (3) ◽  
pp. 29-43 ◽  
Author(s):  
Teck Joo Goh ◽  
K.N. Seetharamu ◽  
G.A. Quadir ◽  
Z.A. Zainal ◽  
K. Jeevan Ganeshamoorthy
Keyword(s):  
Power Distribution ◽  
Heat Dissipation ◽  
Thermal Analyses ◽  
Convection Heat Transfer ◽  
Multiple Linear Regression Model ◽  
Least Square Method ◽  
Least Square ◽  
Junction Temperature ◽  
Thermal Investigations ◽  
Placement Distance

This paper presents the thermal analyses carried out to predict the temperature distribution of the silicon chip with non‐uniform power dissipation patterns and to determine the optimal locations of power generating sources in silicon chip design layout that leads to the desired junction temperature, Tj. Key thermal parameters investigated are the heat source placement distance, level of heat dissipation, and magnitude of convection heat transfer coefficient. Finite element method (FEM) is used to investigate the effect of the key parameters. From the FEM results, a multiple linear regression model employing the least‐square method is developed that relates all three parameters into a single correlation which would predict the maximum junction temperature, Tj,max.

Quantification of the spatial strain distribution of scoliosis using a thin-plate spline method

2014 ◽  
Vol 47 (1) ◽  
pp. 302-307 ◽  
Author(s):  
Yoshimori Kiriyama ◽  
Kota Watanabe ◽  
Morio Matsumoto ◽  
Yoshiaki Toyama ◽  
Takeo Nagura
Keyword(s):  
Thin Plate ◽  
Strain Distribution ◽  
Thin Plate Spline ◽  
Spline Method

scholarly journals Estimating parameters of linear regression with an exponential power distribution of errors by using a polynomial maximization method

2021 ◽  
Vol 1 (4 (109)) ◽  
pp. 64-73
Author(s):  
Serhii Zabolotnii ◽  
Vladyslav Khotunov ◽  
Anatolii Chepynoha ◽  
Olexandr Tkachenko
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Power Distribution ◽  
Maximum Likelihood Method ◽  
Least Square Method ◽  
Least Square ◽  
Likelihood Method ◽  
Random Errors ◽  
Exponential Power Distribution ◽  
Exponential Power

This paper considers the application of a method for maximizing polynomials in order to find estimates of the parameters of a multifactorial linear regression provided the random errors of the regression model follow an exponential power distribution. The method used is conceptually close to a maximum likelihood method because it is based on the maximization of selective statistics in the neighborhood of the true values of the evaluated parameters. However, in contrast to the classical parametric approach, it employs a partial probabilistic description in the form of a limited number of statistics of higher orders. The adaptive algorithm of statistical estimation has been synthesized, which takes into consideration the properties of regression residues and makes it possible to find refined values for the estimates of the parameters of a linear multifactorial regression using the numerical Newton-Rafson iterative procedure. Based on the apparatus of the quantity of extracted information, the analytical expressions have been derived that make it possible to analyze the theoretical accuracy (asymptotic variances) of estimates for the method of maximizing polynomials depending on the magnitude of the exponential power distribution parameters. Statistical modeling was employed to perform a comparative analysis of the variance of estimates obtained using the method of maximizing polynomials with the accuracy of classical methods: the least squares and maximum likelihood. Regions of the greatest efficiency for each studied method have been constructed, depending on the magnitude of the parameter of the form of exponential power distribution and sample size. It has been shown that estimates from the polynomial maximization method may demonstrate a much lower variance compared to the estimates from a least-square method. And, in some cases (for flat-topped distributions and in the absence of a priori information), may exceed the estimates from the maximum likelihood method in terms of accuracy

Direct Assessment of Effective Renal Plasma Flow from Renoscintigraphy with a Gamma Camera and an On-Line Computer

1981 ◽  
Vol 20 (06) ◽  
pp. 274-278
Author(s):  
J. Liniecki ◽  
J. Bialobrzeski ◽  
Ewa Mlodkowska ◽  
M. J. Surma
Keyword(s):  
Count Rate ◽  
Renal Plasma Flow ◽  
Early Period ◽  
Least Square Method ◽  
Least Square ◽  
Uptake Coefficient ◽  
Positive Linear Correlation ◽  
On Line ◽  
Best Fit ◽  
Plasma Activity

A concept of a kidney uptake coefficient (UC) of 131I-o-hippurate was developed by analogy from the corresponding kidney clearance of blood plasma in the early period after injection of the hippurate. The UC for each kidney was defined as the count-rate over its ROI at a time shorter than the peak in the renoscintigraphic curve divided by the integral of the count-rate curve over the "blood"-ROI. A procedure for normalization of both curves against each other was also developed. The total kidney clearance of the hippurate was determined from the function of plasma activity concentration vs. time after a single injection; the determinations were made at 5, 10, 15, 20, 30, 45, 60, 75 and 90 min after intravenous administration of 131I-o-hippurate and the best-fit curve was obtained by means of the least-square method. When the UC was related to the absolute value of the clearance a positive linear correlation was found (r = 0.922, ρ > 0.99). Using this regression equation the clearance could be estimated in reverse from the uptake coefficient calculated solely on the basis of the renoscintigraphic curves without blood sampling. The errors of the estimate are compatible with the requirement of a fast appraisal of renal function for purposes of clinical diagknosis.

PENGARUH KNOWLEDGE MANAGEMENT, KOMPETENSI, MOTIVASI TERHADAP KINERJA KARYAWAN

2015 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Miftahol Arifin
Keyword(s):  
Knowledge Management ◽  
Linear Regression ◽  
Significant Influence ◽  
Employee Performance ◽  
Least Square Method ◽  
Least Square ◽  
Analysis Tool ◽  
Ordinary Least Square ◽  
Competence Motivation ◽  
Performance Analyze

The purpose of this research is to analyze the influence of knowledge management on employee performance, analyze the effect of competence on employee performance, analyze the influence of motivation on employee performance). In this study, samples taken are structural employees PT.centris Kingdom Taxi Yogyakarta. The analysis tool in this study using multiple linear regression with Ordinary Least Square method (OLS). The conclusion of this study showed that the variables of knowledge management has a significant influence on employee performance, competence variables have an influence on employee performance, motivation variables have an influence on employee performance, The analysis showed that the variables of knowledge management, competence, motivation on employee performance.Keywords: knowledge management, competence, motivation, employee performance.

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