A Novel Defogging Algorithm Based on Genetic Algorithm with Analysis of Scientific Data Materials

2012 ◽  
Vol 461 ◽  
pp. 806-809
Author(s):  
Xiao Guang Li ◽  
Li Kang
Keyword(s):  
Genetic Algorithm ◽  
Scientific Data ◽  
Object Image ◽  
Optimal Parameters ◽  
Single Object ◽  
Global Contrast ◽  
Image Defogging ◽  
Global Optimal ◽  
Improved Model ◽  
Degraded Images

This paper proposes a novel defogging algorithm based on the improved model with analysis of scientific data materials. By integrating the merit of genetic algorithm for searching global optimal parameters, the problem of fog-degraded images defogging restoration is transformed into the problem of optimization estimation for original undegraded image by maximizing the global contrast object function, the proposed algorithm can restore the object image as complete as possible in probability sense. Experimental results for single object image defogging gain satisfy visual effect.

A New Defogging Algorithm Based on Atmospheric Degradation Physical Model

2014 ◽  
Vol 989-994 ◽  
pp. 2484-2487
Author(s):  
Xiao Guang Li
Keyword(s):  
Physical Model ◽  
Object Image ◽  
Object Function ◽  
Atmospheric Scattering ◽  
Global Contrast ◽  
Atmospheric Degradation ◽  
Quality Image ◽  
Degraded Image ◽  
Degraded Images ◽  
Atmospheric Scattering Model

Aiming at the degeneration phenomenon of images taken in mist, according to features of the drop quality image, this paper proposed a novel defogging algorithm based on improved atmospheric scattering model. The problem of fog-degraded images defogging restoration is transformed into the problem of optimization estimation for original un-degraded image by maximizing the global contrast object function, in this way, the proposed algorithm can restore the object image as completely as possible in probability sense. The experimental results show that the method can effectively improve the fog degeneration phenomenon and improve image clarity, significantly improving drop quality image visual effect.

Research on an Improved Algorithm of Fog-Degrade Image Defogging Based on Analysis of Scientific Data Materials

2011 ◽  
Vol 282-283 ◽  
pp. 425-428 ◽  
Author(s):  
Xiao Guang Li ◽  
Li Kang
Keyword(s):  
Scientific Data ◽  
Experimental Results ◽  
Visual Effects ◽  
Attenuation Law ◽  
Image Defogging ◽  
Degraded Image ◽  
Degraded Images ◽  
The Impact ◽  
Fuzzy Region ◽  
Improved Algorithm

Based on the analysis for the fuzzy nature of fog-degraded images, an improved algorithm of fog-degrade image defogging was presented. By normalizing the degraded image, the algorithm allayed the impact of different fog conditions on gray levels distribution range. According to the attenuation law of atmospheric contrast in fog weather, the algorithm enhanced the contrast of normalized image in fuzzy region. Experimental results show that the proposed algorithm is effective, demonstrate the satisfying visual effects of this improved algorithm.

Optimal Design of T-S Fuzzy Controller Based on Improved Genetic Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 924-929
Author(s):  
Ling Jiao Dong ◽  
Shao Xing Su
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Fuzzy Controller ◽  
Fuzzy Reasoning ◽  
Control Performance ◽  
Optimal Parameters ◽  
Improved Genetic Algorithm ◽  
Control Rules ◽  
Excellent Control ◽  
Global Optimal

To solving the problem that there had been too many undetermined parameters in the fuzzy control rules, it presented a simplified Takagi-Sugno, namely T-S, fuzzy reasoning method. It reduced the parameters of the IF-THEN rules greatly. In addition this paper also improved the genetic algorithm on the analysis of the prior genetic algorithm, by which the global optimal parameters of the controller can be found easily and quickly thus the control rules can be amended and perfected. The simulation results show that the improved genetic algorithm can find the optimal parameters at a high speed and the optimized T-S fuzzy controller can obtain an excellent control performance.

Research of Accurate NURBS Curve Fitting Based on Genetic Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1927-1930
Author(s):  
Yin Juan Zhang ◽  
Yong Ke Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Data Processing ◽  
Curve Fitting ◽  
Data Fitting ◽  
Weight Coefficient ◽  
Scientific Data ◽  
Nurbs Curve ◽  
B Spline ◽  
Global Optimal

In order to control the curve modality of non-uniform rational B-spline accurately, the genetic algorithm is presented to the manipulative precision of NURBS curve fitting. The manipulative precision of curve fitting and the overall side-by-side search ability of genetic algorithm were researched; the excellent unit is founded in the field of weight coefficient. The precision result and the curve figure of curve fitting using the excellent weight coefficient are better. The examples of data fitting are given to show that the curves fitting used genetic algorithms are better in approximation. The precision result of curve fitting is improved. The global optimal search of genetic algorithm provides a reliable tool for scientific data processing.

Holes Machining Process Optimization with Genetic Algorithm

2011 ◽  
Vol 460-461 ◽  
pp. 117-122 ◽  
Author(s):  
Guang Yu Zhu ◽  
Lian Fang Chen
Keyword(s):  
Genetic Algorithm ◽  
Cutting Tool ◽  
Machining Process ◽  
Processing Route ◽  
Optimal Route ◽  
Optimal Processing ◽  
Global Optimal ◽  
Level Method ◽  
Multi Level ◽  
Changing Times

In this paper, a multi-level method has been adopted to optimize the holes machining process with genetic algorithm (GA). Based on the analyzing of the features of the part with multi-holes, the local optimal processing route for the holes with the same processing feature is obtained with GA, then try to obtain the global optimal route with GA by considering the obtained local optimal route and the holes with different features. That is what the multi-level method means. The optimal route means the minimum moving length of the cutting tool and the minimum changing times of the cutting tool. The experiment is carried out to verify the algorithm and the proposed method, and result indicates that with GA and using the multi-level method the optimal holes machining route can be achieved efficiently.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

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