Analyzing Fuel Savings of an Aerodynamic Drag Reduction Device with the Aid of a Robust Linear Least Squares Method

2014 ◽  
Vol 7 (2) ◽  
pp. 675-684 ◽  
Author(s):  
Jouke Van der Krieke ◽  
Gandert Van Raemdonck
Keyword(s):  
Drag Reduction ◽  
Least Squares ◽  
Least Squares Method ◽  
Aerodynamic Drag ◽  
Linear Least Squares ◽  
Fuel Savings ◽  
Aerodynamic Drag Reduction

A Study of an Active Rear Diffuser Device for Aerodynamic Drag Reduction of Automobiles

2012 ◽  
Author(s):  
Seung-On Kang ◽  
Jun-Ho Cho ◽  
Sang-Ook Jun ◽  
Hoon-Il Park ◽  
Ki-Sun Song ◽  
...  
Keyword(s):  
Drag Reduction ◽  
Aerodynamic Drag ◽  
Aerodynamic Drag Reduction

scholarly journals GM(1,1;λ) with Constrained Linear Least Squares

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 278
Author(s):  
Ming-Feng Yeh ◽  
Ming-Hung Chang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Model Fitting ◽  
Ordinary Least Squares ◽  
Linear Least Squares ◽  
Unknown Parameters ◽  
Constrained Problems ◽  
Linear Least Squares Problems ◽  
Ordinary Least Squares Method ◽  
Better Than

The only parameters of the original GM(1,1) that are generally estimated by the ordinary least squares method are the development coefficient a and the grey input b. However, the weight of the background value, denoted as λ, cannot be obtained simultaneously by such a method. This study, therefore, proposes two simple transformation formulations such that the unknown parameters, and can be simultaneously estimated by the least squares method. Therefore, such a grey model is termed the GM(1,1;λ). On the other hand, because the permission zone of the development coefficient is bounded, the parameter estimation of the GM(1,1) could be regarded as a bound-constrained least squares problem. Since constrained linear least squares problems generally can be solved by an iterative approach, this study applies the Matlab function lsqlin to solve such constrained problems. Numerical results show that the proposed GM(1,1;λ) performs better than the GM(1,1) in terms of its model fitting accuracy and its forecasting precision.

Beam closed orbit correction and feedback by constrained linear least squares method in Hefei light source

2010 ◽  
Vol 22 (1) ◽  
pp. 155-158
Author(s):  
宣科 Xuan Ke ◽  
王琳 Wang Lin ◽  
李川 Li Chuan ◽  
李为民 Li Weimin ◽  
王季刚 Wang Jigang ◽  
...  
Keyword(s):  
Least Squares ◽  
Light Source ◽  
Least Squares Method ◽  
Closed Orbit ◽  
Linear Least Squares ◽  
Orbit Correction

scholarly journals Ellipse Coefficient Map-Based Geomagnetic Fingerprint Considering Azimuth Angles

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 708
Author(s):  
Ju-Hyeon Seong ◽  
Seung-Hyun Lee ◽  
Kyoung-Kuk Yoon ◽  
Dong-Hoan Seo
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Azimuth Angle ◽  
High Signal ◽  
Linear Least Squares ◽  
Positioning Accuracy ◽  
Signal Stability ◽  
Novel Approach ◽  
Magnetic Vectors ◽  
Equalization Method

Geomagnetic fingerprint has been actively studied because of the high signal stability and positioning resolution even when the time has elapsed. However, since the measured three-axis geomagnetism signals at one position are irregular according to the change of the azimuth angle, a large-sized database which is stored magnitudes per angles is required for robust and accurate positioning against the change of the azimuth angle. To solve this problem, this paper proposes a novel approach, an elliptic coefficient map based geomagnetic fingerprint. Unlike the general fingerprint, which stores strength or magnitude of the geomagnetism signals depending on the position, the proposed algorithm minimized the size of databased by storing the Ellipse coefficient map through the ellipse equation derived from the characteristics of 2-D magnetic vectors depending on the position. In addition, the curvature bias of ellipse was reduced by applying the normalized linear least-squares method to 2-D geomagnetic characteristics and the positioning accuracy was improved by applying the weighted geomagnetic signal equalization method.

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