A Novel Method of Wavelet Threshold Shrinkage Based on Genetic Algorithm and Sample Entropy

2013 ◽  
Author(s):  
Yan Xingwei ◽  
Lu Dawei ◽  
Yang Afeng ◽  
Zhang Jun ◽  
Du Chun
Keyword(s):  
Genetic Algorithm ◽  
Sample Entropy ◽  
Novel Method

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

A novel method to design S-box based on chaotic map and genetic algorithm

2012 ◽  
Vol 376 (6-7) ◽  
pp. 827-833 ◽  
Author(s):  
Yong Wang ◽  
Kwok-Wo Wong ◽  
Changbing Li ◽  
Yang Li
Keyword(s):  
Genetic Algorithm ◽  
Chaotic Map ◽  
Novel Method

A New Intelligent Fault Recognition Method for Rotating Machinery

2013 ◽  
Vol 706-708 ◽  
pp. 1705-1708
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Jie Min ◽  
Yan Jie Zhou
Keyword(s):  
Rank Order ◽  
Sample Entropy ◽  
Rotating Machinery ◽  
Morphological Filter ◽  
Recognition Method ◽  
Fault Pattern ◽  
Fault Recognition ◽  
Novel Method ◽  
Fault Information ◽  
Feature Frequency

In this paper, a novel method to recognize rotor fault pattern was proposed based on rank-order morphological filter, harmonic window decomposition, sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Then, the six feature frequency bands which contain the typical fault information were extracted by harmonic window decomposition that needs not decomposition; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different rotor vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in fault diagnosis of rotating machinery effectively.

A Novel Method Based on Adjusted Sample Entropy for Process Capability Analysis in Complex Manufacturing Processes

2015 ◽  
Author(s):  
Xianguang Kong ◽  
Shing I. Chang ◽  
Zheng Zhang
Keyword(s):  
Manufacturing Systems ◽  
Process Capability ◽  
Process Variations ◽  
Sample Entropy ◽  
Process Data ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
Novel Method ◽  
Fixed Distribution ◽  
Over Time

As manufacturing systems become increasingly complex, manufacturing enterprises face the challenging need for precise, effective process capability analyses. However, acquisition of process data, characterized by larger volume and complexity, is much easier, consequently making process capability analysis more difficult. Traditional process capability analysis methods such as Cp or Cpk are not adequate for large volume process data collected over time because these methods assume that process distribution remains unchanged. Therefore, the goal of this paper is to explore the use of sample entropy (SampEn) as it relates to univariate process capability analysis. The proposed method, which alleviates the fixed distribution assumption, can identify changing process variations over time. We proposed a novel method based on Adjusted Sample Entropy (AdSEn) to quantify process variation changes. A study based on simulation data sets showed that the proposed method provides adequate process capability information.

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