scholarly journals Research on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jin-Yu Zhang ◽  
Xiang-Bing Meng ◽  
Wei Xu ◽  
Wei Zhang ◽  
Yong Zhang
Keyword(s):  
Differential Evolution ◽  
Exponential Decay ◽  
Differential Evolution Algorithm ◽  
Infrared Image ◽  
Image Data ◽  
Image Sequence ◽  
Thermal Image ◽  
Compression Algorithm ◽  
Double Exponential ◽  
Sequence Compression

This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method.

Pose Estimation for 3D Work Piece Using Differential Evolution Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1708-1712
Author(s):  
Wei Liu ◽  
Jian Hua Su ◽  
Sui Wu Zheng ◽  
Peng Wang
Keyword(s):  
Differential Evolution ◽  
Pose Estimation ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Image Data ◽  
Work Piece ◽  
Model Parameters ◽  
Optimization Strategy ◽  
3D Object ◽  
Accurate Model

In industrial fields, precise pose of a 3D object is the prerequisite of the subsequent tasks like grasping and assembly, thus many researches on accurate pose estimation of a 3D object are explored over the last decades. To get the pose of a 3D work piece from the 2D image data is a challenging task in industrial applications. This paper proposes a fully automated pose estimation system which is capable to estimate the accurate model and pose of a 3D work piece that can well match the 2D image data. This is achieved by representing the above problem as an optimization problem aiming at finding the accurate model parameters and pose parameters of work piece by minimize the difference between the real 2D image and the hypothetical 2D image that produced through the given parameters from 3D image. Due to the coupling of the unknown model and pose parameters and the discontinuity of the objective function, the above optimization problem cannot be solved through traditional optimization approaches. Hence, we utilize a heuristic optimization strategy - Differential Evolution to cope with the problem. The experimental results demonstrate the effectiveness of the proposed method.

A novel compression algorithm for infrared thermal image sequence based on K-means method

2014 ◽  
Vol 64 ◽  
pp. 18-25 ◽  
Author(s):  
Jin-Yu Zhang ◽  
Wei Xu ◽  
Wei Zhang ◽  
Xiangbin Meng ◽  
Yong Zhang
Keyword(s):  
Image Sequence ◽  
Thermal Image ◽  
Compression Algorithm

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm

Parameter Estimation By Using An Improved Bee Memory Differential Evolution Algorithm (Ibmde) To Simulate Biochemical Pathways

2013 ◽  
Vol 8 (999) ◽  
pp. 1-6
Author(s):  
Chuii Khim Chong ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Mohd Shahir Shamsir ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Biochemical Pathways ◽  
Evolution Algorithm

The Economic Dispatch of Wind Integrated Power System based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 13 (3) ◽  
pp. 384-395
Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Bee Colony ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch ◽  
Integrated Power System

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

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