A new image thresholding and gradient optimization algorithm using object class uncertainty theory

2009 ◽  
Author(s):  
Yinxiao Liu ◽  
Punam K. Saha
Keyword(s):  
Optimization Algorithm ◽  
Uncertainty Theory ◽  
Object Class ◽  
Image Thresholding ◽  
Gradient Optimization ◽  
Class Uncertainty

scholarly journals Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Linguo Li ◽  
Lijuan Sun ◽  
Yu Xue ◽  
Shujing Li ◽  
Xuwen Huang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Image Thresholding

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

scholarly journals Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1134 ◽  
Author(s):  
Heming Jia ◽  
Chunbo Lang ◽  
Diego Oliva ◽  
Wenlong Song ◽  
Xiaoxu Peng
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Satellite Image ◽  
Similarity Index ◽  
Image Thresholding ◽  
Grasshopper Optimization Algorithm ◽  
Significant Difference ◽  
Grasshopper Optimization

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

An object class-uncertainty induced adaptive force and its application to a new hybrid snake

2007 ◽  
Vol 40 (10) ◽  
pp. 2656-2671 ◽  
Author(s):  
Punam Kumar Saha ◽  
Bipul Das ◽  
Felix W. Wehrli
Keyword(s):  
Object Class ◽  
Class Uncertainty
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