Image Segmentation to HSI Model Based on Improved Particle Swarm Optimization

2009 ◽  
pp. 757-765 ◽  
Author(s):  
Bo Zhao ◽  
Yajun Chen ◽  
Wenhua Mao ◽  
Xiaochao Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Model Based

scholarly journals Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China

2019 ◽  
Vol 11 (11) ◽  
pp. 3096 ◽  
Author(s):  
Xinghan Xu ◽  
Weijie Ren
Keyword(s):  
Air Pollution ◽  
Particle Swarm Optimization ◽  
Air Quality ◽  
Hybrid Model ◽  
Particle Swarm ◽  
Echo State Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Model Based ◽  
Pm2.5 Concentration

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.

Fast Multilevel Thresholding Method for Image Segmentation Based on Improved Particle Swarm Optimization and Maximal Variance

2012 ◽  
Vol 532-533 ◽  
pp. 1741-1746 ◽  
Author(s):  
Zheng Tao Peng ◽  
Kang Ling Fang ◽  
Zhi Qi Su ◽  
Shi Hong Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Multilevel Thresholding ◽  
Object Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Thresholding Method ◽  
Optimal Thresholds

To determine the optimal thresholds in image segmentation, a new multilevel thresholding method based on improved particle swarm optimization (IPSO) is proposed in this paper. Firstly, use the conception of independent peaks to divide the histogram to several regions, secondly, the optimization object function using maximum between-class variance (MV) method can be gotten in each area, by the non-uniform mutation and Geese-LDW PSO optimization of the object function, the optimal thresholds can be gotten, and the image can be segmented with the thresholds. Compared with the basic MV algorithm and genetic algorithm (GA) modified MV, the experimental results show that the new method not only realizes the image segmentation well, but also improves the speed.

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