Self-Adaptive Methods to Characterize Bio-Acoustic Scattering and Propagation

2012 ◽  
Author(s):  
W. A. Kuperman
Keyword(s):  
Acoustic Scattering ◽  
Adaptive Methods ◽  
Self Adaptive

scholarly journals Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 238
Author(s):  
Zhuofei Xu ◽  
Yuxia Shi ◽  
Qinghai Zhao ◽  
Wei Li ◽  
Kai Liu
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Extreme Values ◽  
Rolling Bearing ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Printing Press ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Self Adaptive

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

scholarly journals Research on Self-adaptive Methods of Sensor Data Interface

2017 ◽  
Vol 10 (1) ◽  
pp. 99-112
Author(s):  
Chunshan Shen ◽  
Jun Jiao ◽  
Huimin Ma ◽  
Shuqing Wang
Keyword(s):  
Adaptive Methods ◽  
Sensor Data ◽  
Self Adaptive

scholarly journals Differential Evolution for Neural Networks Optimization

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 69 ◽  
Author(s):  
Marco Baioletti ◽  
Gabriele Di Bari ◽  
Alfredo Milani ◽  
Valentina Poggioni
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Differential Evolution ◽  
Adaptive Methods ◽  
Experimental Results ◽  
Classification Problems ◽  
Adaptive Differential Evolution ◽  
Neural Networks Optimization ◽  
Better Than ◽  
Self Adaptive

In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.

Self-Adaptive Illumination Method for Optical Multi-Touch System

2011 ◽  
Vol 480-481 ◽  
pp. 1463-1468
Author(s):  
Tao Yang ◽  
Xiang Li ◽  
Yong Tian Wang ◽  
Yue Liu
Keyword(s):  
Adaptive Methods ◽  
Experimental Results ◽  
Ambient Lighting ◽  
Illumination Method ◽  
Background Image ◽  
Accuracy And Stability ◽  
Self Adaptive

The uneven illumination distribution would affect the accuracy and stability of an optical multi-touch system. In this paper, different self-adaptive methods based on the type of interference source are integrated. When the ambient lighting changes, the luminance of the captured image will changes accordingly. This method tests the type of interference source by the speed and the region of ambient lighting change, and uses different function to update the background image rapidly. Experimental results show that with the help of the presented solutions, the ghost points are efficiently reduced and the system can adapt to the changes of ambient lighting in only 1 second.

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