Subdivided Error Correction Method for Photoelectric Axis Angular Displacement Encoder Based on Particle Swarm Optimization

2020 ◽  
pp. 1-1
Author(s):  
Xu Gao ◽  
Shuhang Li ◽  
Qinglin Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Error Correction ◽  
Angular Displacement ◽  
Particle Swarm ◽  
Correction Method ◽  
Swarm Optimization ◽  
Error Correction Method

Optimized V-Shaped Beam Micro-Electrothermal Actuator Using Particle Swarm Optimization (PSO) Technique

2019 ◽  
Vol 11 (1) ◽  
pp. 62-67 ◽  
Author(s):  
Salah Eldeen Osman ◽  
Musaab Zarog
Keyword(s):  
Particle Swarm Optimization ◽  
Microelectromechanical Systems ◽  
Low Voltage ◽  
Angular Displacement ◽  
Particle Swarm ◽  
Entropy Generation Rate ◽  
Swarm Optimization ◽  
Variable Optical Attenuator ◽  
Output Force ◽  
Wide Range

Background: Electrothermal microactuators are very promising for wide range of Microelectromechanical Systems (MEMS) applications due to the low voltage requirement and large force produced. Method: A new optimized V-beam electrothermal micro actuator was implemented in variable optical attenuator. In this work, Particle Swarm Optimization (PSO) technique is proposed to design the Vshaped beam. Result: The approach has successfully improved both angular displacement & output force of the microactuator. Entropy generation rate was used as optimization criteria.

An Optical Coherence Tomographic Aberration Correction Method Based on the Particle Swarm Optimization Algorithm

2020 ◽  
Vol 40 (10) ◽  
pp. 1011002
Author(s):  
毕津慈 Bi Jinci ◽  
高志山 Gao Zhishan ◽  
朱丹 Zhu Dan ◽  
马剑秋 Ma Jianqiu ◽  
袁群 Yuan Qun ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Correction Method ◽  
Aberration Correction ◽  
Optical Coherence ◽  
Swarm Optimization

scholarly journals Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

2017 ◽  
Author(s):  
Minlan Jiang ◽  
Lan Jiang ◽  
Dingde Jiang ◽  
Fei Li ◽  
Houbing Song
Keyword(s):  
Particle Swarm Optimization ◽  
Measurement Error ◽  
Error Correction ◽  
Optimization Algorithm ◽  
Measurement Errors ◽  
Particle Swarm ◽  
Dynamic Measurement ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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