Research on the influence of background light on the accuracy of a three-dimensional coordinate measurement system based on dual-PSD

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaohong Lu ◽  
Yu Zhou ◽  
Jinhui Qiao ◽  
Yihan Luan ◽  
Yongquan Wang
Keyword(s):  
Neural Network ◽  
Measurement System ◽  
Bp Neural Network ◽  
Measurement Accuracy ◽  
Three Dimensional ◽  
Electrical Method ◽  
Content Type ◽  
Coordinate Measurement ◽  
Background Light ◽  
Three Dimensional Coordinate

Purpose The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light. Design/methodology/approach The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained. Findings The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy. Originality/value BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

Three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA

2019 ◽  
Vol 36 (6) ◽  
pp. 2066-2083 ◽  
Author(s):  
Xiaohong Lu ◽  
Yongquan Wang ◽  
Jie Li ◽  
Yang Zhou ◽  
Zongjin Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Measuring System ◽  
Content Type ◽  
Coordinate Measurement ◽  
World Coordinate System ◽  
Measurement Algorithm ◽  
Three Dimensional Coordinate

Purpose The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high. Design/methodology/approach A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points. Findings The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points. Originality/value A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

Optimal Layout of Three-Dimensional Coordinate Measurement System Based on Laser Multi-Lateration

2014 ◽  
Vol 41 (7) ◽  
pp. 0708001
Author(s):  
胡进忠 Hu Jinzhong ◽  
余晓芬 Yu Xiaofen ◽  
任兴 Ren Xing ◽  
赵达 Zhao Da
Keyword(s):  
Measurement System ◽  
Three Dimensional ◽  
Optimal Layout ◽  
Coordinate Measurement ◽  
Three Dimensional Coordinate

Layout Optimization of Three-Dimensional Coordinate Measurement System Based on Laser Multi-Lateration

2014 ◽  
Vol 41 (1) ◽  
pp. 0108006
Author(s):  
胡进忠 Hu Jinzhong ◽  
余晓芬 Yu Xiaofen ◽  
彭鹏 Peng Peng ◽  
黄开辉 Huang Kaihui
Keyword(s):  
Measurement System ◽  
Three Dimensional ◽  
Layout Optimization ◽  
Coordinate Measurement ◽  
Three Dimensional Coordinate

Multidimensional research on agrometeorological disasters based on grey BP neural network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bingjun Li ◽  
Shuhua Zhang
Keyword(s):  
Neural Network ◽  
Natural Disaster ◽  
Bp Neural Network ◽  
Henan Province ◽  
Assessment System ◽  
Systematic Analysis ◽  
Content Type ◽  
Analysis And Evaluation ◽  
Grain Crops ◽  
Disaster Vulnerability

PurposeThe purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.Design/methodology/approachFirstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.FindingsThe results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.Practical implicationsThe systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.Originality/valueBy calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsong Tu ◽  
Yuanzhen Liu ◽  
Ming Zhou ◽  
Ruixia Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Compressive Strength ◽  
Thermal Insulation ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Training Sample ◽  
Recycled Aggregate ◽  
Generalization Performance ◽  
Content Type

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Applications of High-Precise Three-Dimensional Measurement System

2011 ◽  
Vol 222 ◽  
pp. 62-65 ◽  
Author(s):  
Takeshi Hashimoto ◽  
Mitsuo Kaneko ◽  
András Rövid ◽  
Hiroaki Ohta ◽  
Akira Fukuda ◽  
...  
Keyword(s):  
Global Warming ◽  
Measurement System ◽  
Measurement Accuracy ◽  
Three Dimensional ◽  
Long Distance ◽  
Accuracy Measurement ◽  
Close Relationship ◽  
Three Dimensional Measurement ◽  
High Accuracy Measurement ◽  
Moving Speed

To reveal the influence of global warming on glaciers, highly accurate observations of glacier movement must continue every year. It is thought that there is a close relationship between glacier moving speed and global warming. Thus, there have been precise, detailed observations of the movement of the Perito Moreno glacier in Patagonia of the Argentine Republic over the past five years. The measurement method of using GPS and an optical measuring instrument is generally used to monitor glacier movement, but the measurement accuracy attained is not optimal because of the huge size of the glacier. The measurement system used for the Perito Moreno observations, however, could realize high accuracy measurement over long distance. The measurement system is based on the principle of stereo measurement using cameras. This paper describes the results of the Perito Moreno glacier observations of this year and considers the effectiveness of glacier observation using cameras.

Paste-filling weighing control system optimization based on neural network

2017 ◽  
Vol 14 (2) ◽  
pp. 155-158 ◽  
Author(s):  
Guimei Wang ◽  
Yong Shuo Zhang ◽  
Lijie Yang ◽  
Shuai Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Back Propagation ◽  
System Optimization ◽  
Filling Material ◽  
Content Type ◽  
Control Precision ◽  
Filling System

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.

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