A NEW METHOD TO CALCULATE THE MECHANICS PARAMETERS OF SLIP SURFACES

2008 ◽  
Vol 22 (09n11) ◽  
pp. 1558-1563
Author(s):  
Q. SHEN ◽  
J. XU ◽  
C. X. CHEN ◽  
Z. H. YE ◽  
R. WANG
Keyword(s):  
Robust Regression ◽  
Slip Surface ◽  
Back Analysis ◽  
Least Square Method ◽  
Optimization Method ◽  
Least Square ◽  
New Method ◽  
Landslide Stability ◽  
Slip Surfaces ◽  
Site Test

Obtaining the more exact parameters of slip surfaces is proved to be crucial in the analysis of landslide stability, and there are four study methods available i.e. in-site test, laboratory test, back analysis and theoretical analysis. In general, the former two are the basic one, whereas the latter two are secondary. A set of test values about shearing stress and direct stress can be obtained through in-situ and lab tests, then the parameters of slip surface should be calculated to fitting for the test value by the least square method usually, but the effect of anomalous values should increase markedly because the quadratic sum of residual errors is adopted in calculations. In order to reduce the effect of anomalous value, a new method based on the robust regression analysis and the particle swarm optimization method are adopted to calculate the mechanics parameter of landslides in this paper. In the new method, the sum of residual absolute values is used to replace the quadratic sum of residual error, and then the quadratic sum of anomalous value can be avoided. Comparing to the least square method, the new method can reduce availably the effect of abnormal values, and the results are more credible. Furthermore, an engineering example is cited to show the validity.

scholarly journals Linear time-delay circuit and stabilizing controller

2021 ◽  
Vol 2091 (1) ◽  
pp. 012005
Author(s):  
Yuhao Cong ◽  
Yong Zhang ◽  
Guang-Da Hu
Keyword(s):  
Time Delay ◽  
Large Time ◽  
Linear Time ◽  
Least Square Method ◽  
Optimization Method ◽  
Delay System ◽  
Least Square ◽  
Single Chip ◽  
Stabilizing Controller ◽  
Delay Circuit

Abstract This paper is concerned with a linear time-delay circuit and its feedback control. We use electronic components such as resistors and capacitors to realize a linear time-delay system. The time-delays are generated by operational amplifiers and single-chip microcomputers. Based on the actual data measured by the oscilloscope, the parameters of the system are estimated using the least square method. Then a comparison study between the waveform image measured by the oscilloscope and the numerical simulation obtained by MATLAB verifies the effectiveness of the parameters estimations of the circuit system. Furthermore, the circuit system is unstable with a large time-delay, a feedback controller is designed to stabilize the circuit system using the optimization method in the literature. Finally, the experimental results in the linear time-delay circuit show the effectiveness of the optimization method.

Study on Ship Form Technical and Economic Evaluation DS Based on Interactive MCDM

2006 ◽  
Author(s):  
Sang Song ◽  
Li-Hua Jun
Keyword(s):  
Decision Making ◽  
Analytical Hierarchy Process ◽  
Accurate Result ◽  
Multiple Criteria Decision Making ◽  
Least Square Method ◽  
Least Square ◽  
New Method ◽  
Decision Systems ◽  
Complex Decision ◽  
Hierarchy Process

A new method of interactive Multiple Criteria Decision Making (MCDM) is presented. In order to settle the problem that in the cases for the ship designers sometimes it is difficult to make a decision when facing so complex ship form schemes. The conventional AHP (Analytical Hierarchy Process) is adopted, but it mostly depends on the designer’s subjective and leads to the systematic error. The new method can obtain the accurate result with the rigid least square method as a tool, making full use of the AHP and the objective information entropy, which reflects the inherent attribute. When applied in the practice, it is proved to be effective, practical and dependable for future ships’ complex Decision Systems (DS).

scholarly journals Determination of the Sediment Carrying Capacity Based on Perturbed Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi-hui Ni ◽  
Qiang Zeng ◽  
Wu Li-chun
Keyword(s):  
Carrying Capacity ◽  
Influencing Factor ◽  
Least Square Method ◽  
Least Square ◽  
New Method ◽  
Linear Regression Method ◽  
Average Flow Velocity ◽  
New Formula ◽  
Sediment Carrying Capacity

According to the previous studies of sediment carrying capacity, a new method of sediment carrying capacity on perturbed theory was proposed. By taking into account the average water depth, average flow velocity, settling velocity, and other influencing factors and introducing the median grain size as one main influencing factor in deriving the new formula, we established a new sediment carrying capacity formula. The coefficients were determined by the principle of dimensional analysis, multiple linear regression method, and the least square method. After that, the new formula was verified through measuring data of natural rivers and flume tests and comparing the verified results calculated by Cao Formula, Zhang Formula, Li Formula, Engelung-Hansen Formula, Ackers-White Formula, and Yang Formula. According to the compared results, it can be seen that the new method is of high accuracy. It could be a useful reference for the determination of sediment carrying capacity.

Novel weights optimization method based on least square method for FPN AND-Rule

2010 ◽  
Vol 24 (7) ◽  
pp. 667-672 ◽  
Author(s):  
Jianxiao Zou ◽  
Houjun Wang ◽  
Jun Shu ◽  
Shicai Fan
Keyword(s):  
Least Square Method ◽  
Optimization Method ◽  
Least Square

Reliability Evaluation on Machining Center Based on Three-Parameter Weibull Distribution

2012 ◽  
Vol 430-432 ◽  
pp. 1645-1649 ◽  
Author(s):  
Gong Chang Ren ◽  
Zhi Wei Yang ◽  
Bo Min Meng
Keyword(s):  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Location Parameter ◽  
Least Square Method ◽  
Optimization Method ◽  
Reliability Evaluation ◽  
Least Square ◽  
Distribution Model ◽  
Adaptive Genetic Algorithm ◽  
Machining Center

In order to improve the model accuracy of reliability evaluation, the Three-Parameter Weibull Distribution model of time between fault was established by introducing location parameters. The correlation coefficient optimization method based on the adaptive genetic algorithm was firstly applied to estimate the location parameter of the Three-Parameter Weibull Distribution. Shape parameter and scale parameter were obtained by the least square method. The time between failures of these series machining center submitting to three-parameter weibull Distribution was checked by the test hypothesis of goodness-of-fit distribution. Finally, the machining center was carried out reliability evaluation based on the Three-Parameter Weibull Distribution model.

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.

A New Method for Evaluating the Profile of Large Paraboloidal Antennas

2014 ◽  
Vol 541-542 ◽  
pp. 1408-1413 ◽  
Author(s):  
Yi Ran Jiang
Keyword(s):  
Genetic Algorithm ◽  
Least Square Method ◽  
Position Error ◽  
Least Square ◽  
New Method ◽  
Adaptive Adjustment ◽  
Theoretical Contour

In order to solve the problem of installation and measuring errors in the profile evaluation of large parabolic antennas, this paper proposes a new method for evaluating the profile of large paraboloidal antennas based on genetic algorithm and least square method. This method can effectively realize the adaptive adjustment of the measured points and the theoretical contour in the process of evaluating profile, which can eliminate the position error between the measured points and the theoretical contour that influences on the results of the profile evaluation. The availability of this method has been proved by simulation.

scholarly journals Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia

2022 ◽  
Vol 18 (2) ◽  
pp. 251-260
Author(s):  
Malecita Nur Atala Singgih ◽  
Achmad Fauzan
Keyword(s):  
Regression Analysis ◽  
Robust Regression ◽  
Estimation Method ◽  
Least Square Method ◽  
Influential Factors ◽  
Least Square ◽  
Method Of Moment ◽  
Best Estimate ◽  
M Estimation ◽  
Mm Estimation

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98  

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