An experimental and theoretical comparison of C3–C5 linear ketones

2017 ◽  
Vol 36 (1) ◽  
pp. 561-568 ◽  
Author(s):  
Heiko Minwegen ◽  
Ultan Burke ◽  
Karl Alexander Heufer
Keyword(s):  
Theoretical Comparison

THEORETICAL COMPARISON OF THE AERODYNAMIC PROPERTIES OF BRANCH SHREDDER HAMMERS WITH HORIZONTAL ROTATION AXIS

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
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Keyword(s):  
Rotation Axis ◽  
Theoretical Comparison ◽  
Aerodynamic Properties

Experimental and Theoretical Comparison of Actinide and Lanthanide Bonding in M[N(EPR2)2]3Complexes (M = U, Pu, La, Ce; E = S, Se, Te; R = Ph,iPr, H)

2008 ◽  
Vol 47 (1) ◽  
pp. 29-41 ◽  
Author(s):  
Andrew J. Gaunt ◽  
Sean D. Reilly ◽  
Alejandro E. Enriquez ◽  
Brian L. Scott ◽  
James A. Ibers ◽  
...  
Keyword(s):  
Theoretical Comparison

Improved square-root cubature information filter

2016 ◽  
Vol 39 (4) ◽  
pp. 579-588 ◽  
Author(s):  
Yulong Huang ◽  
Yonggang Zhang ◽  
Ning Li ◽  
Lin Zhao
Keyword(s):  
Large Scale ◽  
Estimation Error ◽  
Sampling Interval ◽  
Prediction Errors ◽  
Square Root ◽  
Sigma Point ◽  
Theoretical Comparison ◽  
Error Covariance ◽  
Information Filter ◽  
Scale Matrix

In this paper, a theoretical comparison between existing the sigma-point information filter (SPIF) framework and the unscented information filter (UIF) framework is presented. It is shown that the SPIF framework is identical to the sigma-point Kalman filter (SPKF). However, the UIF framework is not identical to the classical SPKF due to the neglect of one-step prediction errors of measurements in the calculation of state estimation error covariance matrix. Thus SPIF framework is more reasonable as compared with UIF framework. According to the theoretical comparison, an improved cubature information filter (CIF) is derived based on the superior SPIF framework. Square-root CIF (SRCIF) is also developed to improve the numerical accuracy and stability of the proposed CIF. The proposed SRCIF is applied to a target tracking problem with large sampling interval and high turn rate, and its performance is compared with the existing SRCIF. The results show that the proposed SRCIF is more reliable and stable as compared with the existing SRCIF. Note that it is impractical for information filters in large-scale applications due to the enormous computational complexity of large-scale matrix inversion, and advanced techniques need to be further considered.

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