scholarly journals Least Squares Consensus for Matching Local Features

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 275
Author(s):  
Qingming Zhang ◽  
Buhai Shi ◽  
Haibo Xu
Keyword(s):  
Least Squares ◽  
Order Statistics ◽  
Random Sampling ◽  
Sampling Methods ◽  
Least Square Method ◽  
Least Square ◽  
Nearest Neighbour ◽  
Data Set ◽  
Alternative Scheme ◽  
New Approach

This paper presents a new approach to estimate the consensus in a data set. Under the framework of RANSAC, the perturbation on data has not been considered sufficiently. We analysis the computation of homography in RANSAC and find that the variance of its estimation monotonically decreases when the size of sample increases. From this result, we carry out an approach which can suppress the perturbation and estimate the consensus set simultaneously. Different from other consensus estimators based on random sampling methods, our approach builds on the least square method and the order statistics and therefore is an alternative scheme for consensus estimation. Combined with the nearest neighbour-based method, our approach reaches higher matching precision than the plain RANSAC and MSAC, which is shown in our simulations.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

scholarly journals The Effect Of Milenial Generation Characteristics Of Job Satisfaction Relations And Employee Engagement

2020 ◽  
Vol 1 (4) ◽  
pp. 239-248
Author(s):  
Alfatih Sikki Manggabarani ◽  
Faisal Marzuki ◽  
Mahendro
Keyword(s):  
Employee Engagement ◽  
Random Sampling ◽  
Employee Satisfaction ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Partial Least Square ◽  
Least Square ◽  
Analysis Method ◽  
Partial Least Square Analysis ◽  
Analysis Technique

This research is a quantitative study that aims to determine the Millennial Generation Characteristics of Employee Engagement. The population in this study The study was conducted by taking samples of Millennials who are actively working at Micro Finance companies with a total of 150 respondents. The sample size was taken as many as 150 respondents, with probability sampling methods especially simple random sampling. Data collection was carried out through questionnaires. The analysis technique used is the PLS (Partial Least Square) analysis method. The results of this study indicate that the value of R- Square (R2) Employee Engagement is 0.786 and Employee Satisfaction is 0.647 thus indicating that the contribution of Grit, Worklife Balance, and Jon Resources variables to Employee Engagement and Employee Satisfaction are 0.786 or 78.6% and 0.647 or 64.7%. And the rest is influenced by other factors not examined.

Localization of Mobile Robot Based on Least Square Method

2013 ◽  
Vol 347-350 ◽  
pp. 808-811
Author(s):  
Jia Lu Li ◽  
Lin Bing Long ◽  
Bao Feng Zhang
Keyword(s):  
Motion Estimation ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Least Squares ◽  
Least Square Method ◽  
Least Square ◽  
Experimental Results ◽  
Sift Algorithm ◽  
Key Techniques ◽  
Point Detection

Localization is the basis for navigation of mobile robots. This paper focuses on key techniques of localization for mobile robots based on vision. Firstly, the specific measures and steps of the algorithm are analyzed and researched in depth. In the study, SIFT algorithm combined with epipolar geometry constraint is used on the environment feature point detection, matching and tracking. And the method of RANSAC combined with the least squares is used to obtain accurate results of the motion estimation. Then the necessary experiments are carried out to verify the correctness and effectiveness of algorithms. The experimental results verified the accuracy of the improved algorithm.

Comparing of Coordinate Transformation Based on Total Least Squares and Least Squares

2014 ◽  
Vol 522-524 ◽  
pp. 1211-1214
Author(s):  
Qing Wu Meng ◽  
Lu Meng
Keyword(s):  
Least Squares ◽  
Coordinate Transformation ◽  
Least Square Method ◽  
Coefficient Matrix ◽  
Total Least Squares ◽  
Least Square ◽  
Experimental Results ◽  
Transformation Models ◽  
Total Least Square

The coordinate transformation models based on least square method and total least square are built and discussed. The least square model only includes the errors of observation vectors, the total least square model simultaneously takes into consideration to the errors of observation vectors and the errors of coefficient matrix. The both models are verified and compared in experiment. The experimental results showed that the model of total least square is more in line with actual, and more reasonable than by least square theoretically, and the coordinate transformation solution result of total least square with least square is more near.

scholarly journals Data approximation using Lotka-Volterra models and a software minimization function

2019 ◽  
Vol 15 (2) ◽  
pp. 5-14
Author(s):  
M. Fečkan ◽  
J. Pačuta
Keyword(s):  
Mathematical Models ◽  
Historical Data ◽  
Least Square Method ◽  
Least Square ◽  
Data Set ◽  
Data Approximation ◽  
Standard Methods ◽  
Volterra Models ◽  
Coefficient Estimation

Abstract In recent years, a lot of effort has been put into finding suitable mathematical models that fit historical data set. Such models often include coefficients and the accuracy of data approximation depends on them. So the goal is to choose the unknown coefficients to achieve the best possible approximation of data by the corresponding solution of the model. One of the standard methods for coefficient estimation is the least square method. This can provide us data approximation but it can also serve as a starting method for further minimizations such as Matlab function fminsearch.

scholarly journals Local magnitude scale in Slovenia

2013 ◽  
Vol 34 ◽  
pp. 23-28 ◽  
Author(s):  
J. Bajc ◽  
Ž. Zaplotnik ◽  
M. Živčić ◽  
M. Čarman
Keyword(s):  
Vertical Component ◽  
Large Data ◽  
Least Square Method ◽  
Least Square ◽  
Magnitude Scale ◽  
Local Magnitude ◽  
Data Set ◽  
Seismic Stations ◽  
Short Period ◽  
Environment Agency

Abstract. In the paper a calibration study of the local magnitude scale in Slovenia is presented. The Seismology and Geology Office of the Slovenian Environment Agency routinely reports the magnitudes MLV of the earthquakes recorded by the Slovenian seismic stations. The magnitudes are computed from the maximum vertical component of the ground velocity with the magnitude equation that was derived some thirty years ago by regression analysis of the magnitudes recorded by a Wood-Anderson seismograph in Trieste and a short period seismograph in Ljubljana. In the study the present single magnitude MLV equation is replaced by a general form of the Richter local magnitude MWA equation. The attenuation function and station-component corrections that compensate the local effects near seismic stations are determined from the synthetic Wood-Anderson seismograms of a large data set by iterative least-square method. The data set used consists of approximately 18 000 earthquakes during a period of 14 yr, each digitally recorded on up to 29 stations. The derived magnitude equation is used to make the final comparison between the new MWA magnitudes and the routinely calculated MLV magnitudes. The results show good overall accordance between both magnitude equations. The main advantage of the introduction of station-component corrections is the reduced uncertainty of the local magnitude that is assigned to a certain earthquake.

Solitary waves of the RLW equation via least squares method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ozlem Ersoy Hepson ◽  
Idris Dag ◽  
Bülent Saka ◽  
Buket Ay
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Spline Functions ◽  
Algebraic Equations ◽  
B Spline ◽  
B Splines ◽  
Rlw Equation ◽  
Set Up

Abstract Integration using least squares method in space and Crank–Nicolson approach in time is managed to set up an algorithm to solve the RLW equation numerically. Trial functions in the least square method consist of a combination of the quartic B-spline functions. Integration of the RLW equation gives a system of algebraic equations. The solutions consisting of a combination of the quartic B-splines are given for some initial and boundary value problems of RLW equation.

A Method of Data Processing for Determining Shear Strength Parameters of Rock

2011 ◽  
Vol 97-98 ◽  
pp. 397-401 ◽  
Author(s):  
Zhi Rong Jia ◽  
Jun Shi
Keyword(s):  
Shear Strength ◽  
Slope Stability ◽  
Least Square Method ◽  
Least Square ◽  
Mechanical Parameters ◽  
Shear Strength Parameters ◽  
Strength Parameters ◽  
New Approach ◽  
Calculating Method ◽  
Main Factors

One of the main factors that influence the analytic results of slope stability is the determining of mechanical parameters. When the least square method is adopted as the method for determining the rock shear strength parameters, its precondition that the observation errors exist only in one observation variable is always being neglected. By analysis, the calculating of shear strength parameters is divided into three kind of situations, and a new approach to calculate the shear strength parameters is obtained according to the fact that the quadratic sum of distance between line and points in minimum, and the calculating formulae and applying scopes of three different calculating methods are also put forward. A calculating case shows that there are obvious differences among different calculating methods for determining the strength parameters, and it is necessary to select the calculating method based on the observation errors.

scholarly journals Reliability Modeling and Estimation of the Gear System

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Enjun Bai ◽  
Liyang Xie ◽  
Hongyi Ma ◽  
Jungang Ren ◽  
Shijian Zhang
Keyword(s):  
Order Statistics ◽  
Least Square Method ◽  
Small Sample ◽  
Gear System ◽  
Least Square ◽  
Linear Regression Method ◽  
Reliability Model ◽  
Statistical Parameters ◽  
Reliability Modeling ◽  
Reliability Models

Traditional reliability models of the gear system either simplify the gear system unreasonably or are too complicated to be applicable for some gear systems. Therefore, a reliability model of the gear system considering statistically dependent failure based on the theory of the order statistics is developed in this paper. Firstly, the gear bending fatigue test of small sample is implemented to obtain the P-S-N curves that are fitted by using the least square method and the linear regression method of the statistical parameters under the test stress levels. Then, according to stress-strength interference theory and the theory of the order statistics, the reliability models considering the number of load cycles from a gear to gear system are built, which indicates clear modeling process of the gear system. Finally, the proposed reliability model of gear system is validated by using the Monte Carlo Simulation (MCS) and the system reliability under special load history is analyzed qualitatively.

A New Method to Identify the Preisach Distribution Function of Hysteresis

2005 ◽  
Vol 475-479 ◽  
pp. 2107-2110 ◽  
Author(s):  
Fan Li ◽  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Wen Bo Zhang ◽  
Hui Bin Xu ◽  
...  
Keyword(s):  
Distribution Function ◽  
Least Squares ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Squares Method ◽  
Input Sequence ◽  
Least Square Method ◽  
Least Square ◽  
New Method ◽  
Inference System

In this paper, a new method which combines the least square method with Tree-Structured fuzzy inference system is presented to approximate the Preisach distribution function. Firstly, by devising the input sequence and measure the output, discrete Preisach measure can be identified by the use of the least squares method. Then, the Preisach function can be obtained with Tree-Structured fuzzy inference system without any special smoothing means. So, this new method is not sensitive to noise, and is a universal approximator of the Preisach function. It collect the merit and overcome the deficiency of the existing methods.

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