Accurate Measurement and Digital Modelling of Complex Surface

2014 ◽  
Vol 670-671 ◽  
pp. 1264-1268 ◽  
Author(s):  
Yan Qiang Liu ◽  
Hong Xun Cheng ◽  
Yan Zhong Wang ◽  
Jian Shen Wang ◽  
Ya Peng Sun
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Complex Surface ◽  
Surface Fitting ◽  
Nurbs Surface ◽  
Fitting Method ◽  
Digital Modelling

Accurate measurement and modeling of complex surface is the foundation to precisely evaluate the error of complex surface. According to the NURBS method for rebuilding complex surface, a sampling data automatic replenishment method based on neural network is proposed and the sampling data with high precision is acquired. By using bicubic NURBS surface fitting method, the complex surface is rebuilt precisely and efficiently.

scholarly journals APPLICATION OF MULTI-FACETED FUNCTION MODEL BASED ON VONDRAK FILTER OPTIMIZATION IN UAV AERIAL IMAGE HEIGHT CORRECTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1313-1317
Author(s):  
F. Zhou ◽  
L. Pu ◽  
S. H. Tang ◽  
Y. F. Yang
Keyword(s):  
High Precision ◽  
Surface Fitting ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Cubic Surface ◽  
Aerial Photogrammetry ◽  
Function Fitting ◽  
Fitting Method ◽  
Elevation Correction ◽  
Vondrak Filter

Abstract. With the rapid development of drone technology and digital camera technology, the method of obtaining high-precision coordinates based on UAV aerial photogrammetry technology is popular. The plane coordinate accuracy of the aerial image of the drone has been able to meet the needs of practical applications, but the elevation accuracy is generally low. Aiming at the low elevation accuracy of UAV aerial photogrammetry, a multi-face function fitting method based on Vondrak filter optimization was proposed. The improved fitting model was used to obtain the elevation correction value of the aerial image, thereby obtaining high-precision image elevation data. In this paper, based on the traditional multi-face function fitting method, some known points were used to model and find the difference between the measured elevation value and the measured elevation. The Vondrak filter was used to smooth the fitting result. Finally, a small number of known elevation points were used for checking, so that the obtained elevation was compared with the actual elevation. The experimental comparison showed that the improved multi-face function fitting method used Vondrak filter was improved by 34.76% compared with the quadric surface fitting, and improved by 14.48% compared with the optimized cubic surface fitting method. Research shows that the multi-faceted function method based on Vondrak filtering is superior to the traditional elevation correction method. The experiment verifies the effectiveness and feasibility of the improved method, and provides some reference value for the research of aerial image elevation correction model.

High-precision linearized interpretation for fully connected neural network

2021 ◽  
pp. 107572
Author(s):  
Xia Lei ◽  
Yongkai Fan ◽  
Kuan-Ching Li ◽  
Arcangelo Castiglione ◽  
Qian Hu
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Fully Connected

High precision detection method of safety helmet based on convolution neural network

2021 ◽  
Vol 36 (7) ◽  
pp. 1018-1026
Author(s):  
Tian-yu LI ◽  
◽  
Dong LI ◽  
Ming-ju CHEN ◽  
Hao WU ◽  
...  
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Detection Method ◽  
Convolution Neural Network

scholarly journals An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Nurbs Surface ◽  
Improved Method ◽  
Swarm Optimization

scholarly journals Surface Fitting for non-Grid Data from in-Situ Measuring Systems of Aero-engine Blade

2020 ◽  
Vol 206 ◽  
pp. 03023
Author(s):  
Qing Mao ◽  
Sen Wang ◽  
Shugui Liu
Keyword(s):  
Dynamic Performance ◽  
Surface Fitting ◽  
Data Conversion ◽  
Machining Accuracy ◽  
Nurbs Surface ◽  
Grid Data ◽  
Measuring Systems ◽  
Aero Engine ◽  
Engine Blade

High machining accuracy of aero-engine blade largely determines the carrying capacity, endurance, acceleration and the dynamic performance of the aero-engine, so a reliable machining error inspection and evaluation technique is imperative. In order to give a reliable error evaluation, the non- uniform rational B-spline (NURBS) technique is adopted to reconstruct the surface within a specified accuracy. Usually, data points measured from aero-engine blade are non-grid data in situ measuring systems. To overcome the difficulty of NURBS surface fitting from non-grid data, a new method based on data conversion is proposed, in which chord length parameterization and uniform parameter sampling are combined together to realize the data convertation, and subsequently hierarchical fitting strategy is applied to finish the NURBS surface reconstruction. The way proposed for data conversion is easy to realize, and by which gemetrical features of original measured data are also reserved well, which make the whole method outstanding in low time cost. Experimental results show that the method is fast, effective. The source code has been implemented in VC++, while the resulting pictures are constructed in Matlab with the obtained control points, knot vectors, and the orders.

scholarly journals Technical and Tactical Diagnosis Model of Table Tennis Matches Based on BP Neural Network

2021 ◽  
Author(s):  
Wenwen Huang ◽  
Miaomiao Lu ◽  
Yuxuan Zeng ◽  
Mengyue Hu ◽  
Yi Xiao
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Diagnostic Model ◽  
Table Tennis ◽  
Winning Probability ◽  
Highly Efficient ◽  
Diagnosis Model

Abstract Background: The technical and tactical diagnosis of table tennis is extremely important for the preparation of matches, and there is a nonlinear relationship between athletes’ performance and their sports quality. As the neural network model has high nonlinear dynamic processing ability and has high fitting accuracy, the main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to diagnose the influence of athletes’ techniques and tactics on the competition result. Methods: A three-layer back propagation neural network model for table tennis match diagnosis were established. The 30 technical and tactical analysis indexes that are closely related to winning a competition were selected based on the double three-phase evaluation method. And 100 table tennis matches were selected as data sample, of which 70 matches were taken as training sample to establish the diagnostic model, the other 30 matches were used to test the validity of the diagnostic model.Results: The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high precision up to 99.997% and highly efficient in fitting (R2 = 0.99). It had a good ability to diagnose the technical and tactical abilities of table tennis players. The technical and tactical diagnosis results showed that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability.Conclusion: The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high precision and highly efficient in fitting. By using this model, the weights of the influence of athletes’ technical and tactical indexes on the winning probability of the competition can be calculated, which provides a valuable reference for formulating targeted training plans for players.

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