scholarly journals COMPARISON OF THE LINEAR AND THE NON-LINEAR BLOCK ADJUSTMENT ON SATELLITE IMAGES RESEARCH

2018 ◽  
Vol XLII-3 ◽  
pp. 775-780
Author(s):  
C. Li ◽  
C. Chen ◽  
Z. Guo ◽  
Q. Liu
Keyword(s):  
Linear Model ◽  
Satellite Images ◽  
Satellite Image ◽  
Least Square Method ◽  
Least Square ◽  
Image Block ◽  
Function Model ◽  
Check Points ◽  
Non Linear ◽  
Linear Least Square

The Rational Function Model （RFM） is a non-linear model. Usually, the RFM-based satellite image block adjustment uses the Taylor series to expand error equations, and then solves the linear model. Theoretically, linearization of a non-linear model affects the accuracy and reliability of the adjustment result. This paper presents linear and non-linear methods for solving the RFM-based block adjustment,and takes ZiYuan3 (ZY-3) satellite imagery block adjustment as an example, using same check points to assess the accuracy of the two methods. In this paper, a non-linear least square method is used for solving the RFM-based block adjustment, which expands a solution to the block adjustment.

scholarly journals Calculation of ICG kinetics by two simultaneous differential equations and non-linear least square method.

Kanzo ◽  
1988 ◽  
Vol 29 (10) ◽  
pp. 1368-1373
Author(s):  
Yutaka SAGAWA ◽  
Toshiko YOSHIKATA ◽  
Nagaki SHIMADA ◽  
Motonobu SUGIMOTO
Keyword(s):  
Differential Equations ◽  
Least Square Method ◽  
Least Square ◽  
Non Linear ◽  
Linear Least Square

A Study on the New Static Photoelastic Experimental Hybrid Method

2005 ◽  
Vol 297-300 ◽  
pp. 2187-2194
Author(s):  
Jai Sug Hawong ◽  
Konstantin Teche
Keyword(s):  
Experimental Data ◽  
Numerical Method ◽  
Experimental Method ◽  
Hybrid Method ◽  
Least Square Method ◽  
Least Square ◽  
Photoelastic Experimental Hybrid Method ◽  
Non Linear ◽  
Newton Raphson ◽  
Linear Least Square

In photoelastic experimental method, until now, we have used the Newton-Raphson numerical method in analysis of photoelastic experimental data such as the non-linear least square method for the photoelastic expreriment. We used the Hook-Jeeves’ numerical method in stead of Newton-Raphson numerical method for the non-linear least square method for photoelastic experimental method. The new photoelastic experimental hybrid method, that is, the photoelastic experimental hybrid method with Hook-Jeeves’ numerical method has been developed in this research. Applying the new photoelastic experimental hybrid method to stress concentration problems and plane fracture problems, it’s validity was assured. The new photoelastic experimental hybrid method is more precise and stabler than the photoelastic experimental hybrid method with Newton- Raphson numerical method (the old photoelastic experimental hybrid method)

Research on the Parameter Estimating Algorithms of Image Edge Detection

2012 ◽  
Vol 562-564 ◽  
pp. 1279-1285
Author(s):  
Ya Ceng Shang ◽  
Jing Chen ◽  
Jun Wei Tian
Keyword(s):  
Linear Model ◽  
Likelihood Estimation ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
Least Square ◽  
Great Likelihood ◽  
Index Model ◽  
Non Linear ◽  
Image Edge ◽  
Future Choice

During detecting the edge of the images, the text partly use great likelihood estimation and least square method estimation algorithm to estimate, we found the result of two estimate algorithms used in the same model are different through experimental analysis. Aiming at above mentioned problems, this text divides the commonly used model in pattern process into the linear model and non-linear model, among the non-linear model, it divides into multinomial model, gauss model, shouldered index model and power counting model, and this text use great likelihood estimate algorithm and least square method estimation algorithm to estimate these models separately, and draw their scope of the application through the experiment, also provide the convenience for the future choice.

Optimisation of Autonomous Threaded Fastenings Based on Non-Linear Least Square Method With GUIs

2004 ◽  
Author(s):  
Mongkorn Klingajay ◽  
Wuttipong Wanathap
Keyword(s):  
User Interfaces ◽  
Least Square Method ◽  
Estimation Procedure ◽  
Least Square ◽  
Unknown Parameters ◽  
Screw Insertion ◽  
Assembly Method ◽  
Non Linear ◽  
On Line ◽  
Linear Least Square

Threaded fastenings are a common assembly method, accounting for over a quarter of all assembly operations. They are especially popular because they permit easy disassembly for maintenance, repair, relocation and recycling. Screw insertions are typically carried out manually as it is a difficult operation to automate. There is very little published research on automating threaded fastenings, and most research on automated assembly focuses on the peg-in-hole assembly problem. Non-linear least square method was designed and employed to identify torque signature signals during online threaded fastening. Creating interactive simulations and graphical user interfaces became necessary as a visualization aid. This provides help and support for the user, allowing them to concentrate on the concept they are illustrating and to put emphasis on the monitoring process rather than the mechanics of running the program. This paper presents a Graphical User Interface (GUI) tool to accommodate and support threaded fastening operations used in assembly line industries. This tool was produced as interactive software with a convenient GUI in combination with the computing and graphics capability of MATLAB. It has applied to automated monitoring of threaded fastenings based-on an analytical model and on-line parameter estimation. The monitoring problem deals with predicting the integrity of the screw insertion process based on the torque vs. insertion angle curves generated during the insertions. A Non-linear Least Square Method (NLSM) is applied for estimation of four unknown parameters during a self-tapping screw insertion to be presented. It is shown that these parameters, required by the model, can be reliably estimated on-line. Experimental results are presented to validate the estimation procedure.

scholarly journals Uncertainty analysis of linear least square fitting apply to non-linear model

2018 ◽  
Vol 1144 ◽  
pp. 012153 ◽  
Author(s):  
T Arunthong ◽  
S Thuenhan ◽  
P Wongsripan ◽  
S Chomkokard ◽  
W Wongkokua ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Linear Model ◽  
Least Square ◽  
Least Square Fitting ◽  
Non Linear ◽  
Linear Least Square
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