Pose Estimation of Round-Shape Workpieces Based on Genetic Algorithm

2013 ◽  
Vol 579-580 ◽  
pp. 665-669
Author(s):  
Gui Chao Lin ◽  
Xiang Jun Zou ◽  
Meng Si Zhu ◽  
Ke Yin Chen ◽  
Zhuang Xu Ke
Keyword(s):  
Genetic Algorithm ◽  
Pose Estimation ◽  
Error Function ◽  
Estimation Method ◽  
Nonlinear Function ◽  
Transformation Model ◽  
Estimation Methods ◽  
Improved Genetic Algorithm ◽  
Analytic Equation ◽  
Round Shape

Since traditional pose estimation methods with the features of points, lines and so on might not be applied directly to round-shape workpieces, a new pose estimation method for round-shape workpieces genetic algorithm based was proposed. Compared with previous studies, this method needs no auxiliary information, such as points, lines, concentric circles and so on. Firstly, transformation model of perspective projection of round-shape workpiece was created, and the round-shape workpiece was characterized by analytic equation. Secondly, via detecting the contour and extracting its feature of workpiece, a feature error function was established with respect to the pose angles, which was a multi-objective nonlinear function. Finally, the error function was solved by an improved genetic algorithm and the pose estimation of round-shape workpiece was achieved. The result of related experiments showed that the method had high accuracy, and to some extent inhibited the effects of noise.

State-of-Health Online Estimation for Li-Ion Battery

2020 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Liu Fang ◽  
◽  
Liu Xinyi ◽  
Su Weixing ◽  
Chen Hanning ◽  
...  
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Battery Management ◽  
State Of Health ◽  
Li Ion Battery ◽  
Improved Genetic Algorithm ◽  
Battery Management Systems ◽  
Li Ion

To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method, the algorithm proposed in this article has higher accuracy, fewer identification parameters, and lower computational complexity.

The Application of Improved Genetic Algorithm in Distribution Network Reconfiguration

2012 ◽  
Vol 490-495 ◽  
pp. 1689-1693 ◽  
Author(s):  
Wen Hua Zhou ◽  
Xiao Long Chen
Keyword(s):  
Genetic Algorithm ◽  
Distribution Network ◽  
Estimation Method ◽  
Computation Time ◽  
Original Data ◽  
Engineering Practice ◽  
Network Reconfiguration ◽  
Improved Genetic Algorithm ◽  
Load Curve ◽  
Distribution Network Reconfiguration

This paper presents an improved algorithm for distribution network reconfiguration. The objectives is to minimized the power loss and the percentage of over-voltage. Based on the traditional genetic algorithm, the adaptable function selection and the disposal of terminating evolution criteria has been improved, to improve the convergence of the system and the calculation accuracy. At the same time, using a new estimation method to correct the load curve. This approach takes full advantage of existing distribution network's original data, it can significantly reduce the computation time, its accuracy to meet the requirements of engineering practice. Test results have been presented along with the discussion of the algorithm.

Maximum Likelihood Parameter Estimation of CNC System Reliability

2014 ◽  
Vol 487 ◽  
pp. 282-285
Author(s):  
Yan Gu ◽  
Yi Qiang Wang ◽  
Xiao Qin Zhou ◽  
Xiu Hua Yuan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
System Reliability ◽  
Simulated Annealing Algorithm ◽  
Likelihood Function ◽  
Estimation Method ◽  
Distribution Model ◽  
Improved Genetic Algorithm ◽  
Cnc System

In order to increase calculation accuracy of CNC system reliability, this paper proposed a maximum likelihood parameter estimation method based on improved genetic algorithm. In the parameter estimation process for CNC system reliability distribution model, the maximum likelihood function value was gained by improving genetic algorithm through simulated annealing algorithm. Parameter estimation was carried out by setting Weibull distribution as an example. The result shows that the improved genetic algorithm can increase solution efficiency and convergence rate. Besides, it can effectively estimate parameters of reliability distribution model.

scholarly journals 3D Single Person Pose Estimation Method Based on Deep Learning

2020 ◽  
Author(s):  
Xinrui Yuan ◽  
Hairong Wang ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Estimation Method ◽  
Hybrid Network ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Single Person ◽  
Depth Study ◽  
Human Pose ◽  
Set Up

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.

scholarly journals Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Fang Liu ◽  
Jie Ma ◽  
Weixing Su
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Particle Filter ◽  
Electric Vehicles ◽  
Estimation Method ◽  
Operating Conditions ◽  
Model Parameters ◽  
Improved Genetic Algorithm ◽  
Soc Estimation ◽  
Unscented Particle Filter

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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