scholarly journals Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO

2021 ◽  
Vol 15 (4) ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Tie Cai ◽  
Wei Cao
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Kriging Model ◽  
Recognition Algorithm ◽  
Image Features ◽  
Detection Accuracy ◽  
Recognition Method ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Citrus Huanglongbing ◽  
Recognition Efficiency

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local based search Multi-objective particle swarm optimization algorithm(CKMOPSO) selects feature vectors with strong classification capabilities from the original disease image features, experimental results show that this is an effective recognition method.

scholarly journals License plate identification and recognition in a non-standard environment using neural pattern matching

2021 ◽  
Author(s):  
Imran Shafi ◽  
Imtiaz Hussain ◽  
Jamil Ahmad ◽  
Pyoung Won Kim ◽  
Gyu Sang Choi ◽  
...  
Keyword(s):  
Character Recognition ◽  
Detection Algorithm ◽  
Input Image ◽  
Recognition Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
Recognition Mechanism ◽  
Robust Detection ◽  
Feature Representations ◽  
Recognition Efficiency

AbstractNon-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

Optimization of structure parameters for angular contact ball bearings based on Kriging model and particle swarm optimization algorithm

2016 ◽  
Vol 231 (23) ◽  
pp. 4298-4308 ◽  
Author(s):  
Feng Jilu ◽  
Sun Zhili ◽  
Sun Hongzhe
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Generation ◽  
Ball Bearing ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Kriging Model ◽  
Combination Method ◽  
Structure Parameters ◽  
Angular Contact Ball Bearing ◽  
Swarm Optimization

To achieve the heat generation of an angular contact ball bearing, especially when confronted with a difficult challenge, is a complexity of numerical and analytical models of bearings. A combination method of the Kriging model and particle swarm optimization algorithm is proposed for optimizing structure parameters of the bearing to obtain the minimum heat generation of the bearing. Therefore, the heat generation and stiffness of the angular contact ball bearing, which are acquired based on pseudo statics analysis and raceway control theory of the bearing, are the optimization goal and constraint condition, respectively, that are used in particle swarm optimization. Taking the angular contact ball bearing NSK-7016A5 as an example, the results show that the total heat generation of the bearing is decreased and that the axial stiffness of the bearing is increased by optimizing the structure parameters of the bearing. In the end, the combination method that uses both Kriging and particle swarm optimization to optimize the structure parameters of the bearing could obtain satisfactory design results and increased bearing design efficiency; it also bears the potential for the design parameter optimization of other mechanical structures, which may lead to better design results.

scholarly journals Structure optimization design of a thin-film diffraction imaging system based on the Kriging model and the improved particle swarm optimization algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 875-889
Author(s):  
Yitian Wang ◽  
Liu Zhang ◽  
Huanyu Zhao ◽  
Fan Zhang
Keyword(s):  
Thin Film ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Imaging System ◽  
Kriging Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Attitude Stability ◽  
Diffraction Imaging

Abstract. A thin-film diffraction imaging system is a type of space telescope imaging system with high resolution and loose surface tolerance often used in various fields, such as ground observation and military reconnaissance. However, because this system is a large and flexible multi-body structure, it can produce flexural vibration easily during the orbit operation, which has a serious effect on the attitude stability of the system and results in low pointing accuracy. Therefore, this study proposes an optimization method based on the Kriging model and the improved particle swarm optimization algorithm to improve the stability and optimize the structure of the entire system. Results showed the area–mass ratio of the thin-film diffraction imaging system decreased by 9.874 %, the first-order natural frequency increased by 23.789 %, and the attitude stability of the thin-film diffraction imaging system improved.

RFID Anti-Collision Method Based on Particle Swarm Optimization and Support Vector Machine

2012 ◽  
Vol 170-173 ◽  
pp. 3398-3401
Author(s):  
Wen Ge Zhao
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Collision Detection ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Swarm Optimization ◽  
Detection Technology

RFID anti-collision method based on particle swarm optimization and support vector machine is presented in the paper. Support vector machine is a new detection technology,which is applied to RFID anti-collision detection. Particle swarm optimization algorithm is applied to choose the appropriate parameters of support vector machine. Particle swarm optimization algorithm can make the particle move toward the optimal resolution based on the history best experiences of each particle and global best position in swarm.The proposed RFID anti-collision structure is mainly composed of protocol processing module, interface module, RFID anti-collision method and serial-parallel conversion.The testing results show that RFID collision detection accuracy of particle swarm optimization and support vector machine than that of traditional support vector machine and BP neural network.

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