Differential Entropy

2005 ◽  
pp. 243-259
Keyword(s):  
Differential Entropy

scholarly journals Investigation of Deformation Inhomogeneity and Low-Cycle Fatigue of a Polycrystalline Material

2021 ◽  
Vol 11 (6) ◽  
pp. 2673
Author(s):  
Mu-Hang Zhang ◽  
Xiao-Hong Shen ◽  
Lei He ◽  
Ke-Shi Zhang
Keyword(s):  
Cyclic Loading ◽  
Low Cycle Fatigue ◽  
Strain Tensor ◽  
Equivalent Strain ◽  
Differential Entropy ◽  
Strain Components ◽  
Deformation Inhomogeneity ◽  
Standard Deviations ◽  
Number Of Cycles ◽  
The Relationship

Considering the relationship between inhomogeneous plastic deformation and fatigue damage, deformation inhomogeneity evolution and fatigue failure of superalloy GH4169 under temperature 500 °C and macro tension compression cyclic loading are studied, by using crystal plasticity calculation associated with polycrystalline representative Voronoi volume element (RVE). Different statistical standard deviation and differential entropy of meso strain are used to measure the inhomogeneity of deformation, and the relationship between the inhomogeneity and strain cycle is explored by cyclic numerical simulation. It is found from the research that the standard deviations of each component of the strain tensor at the cyclic peak increase monotonically with the cyclic loading, and they are similar to each other. The differential entropy of each component of the strain tensor also increases with the number of cycles, and the law is similar. On this basis, the critical values determined by statistical standard deviations of the strain components and the equivalent strain, and that by differential entropy of strain components, are, respectively, used as fatigue criteria, then predict the fatigue–life curves of the material. The predictions are verified with reference to the measured results, and their deviations are proved to be in a reasonable range.

Spectral blind deconvolution with differential entropy regularization for infrared spectrum

2015 ◽  
Vol 71 ◽  
pp. 481-491 ◽  
Author(s):  
Hai Liu ◽  
Zhaoli Zhang ◽  
Sanya Liu ◽  
Jiangbo Shu ◽  
Tingting Liu ◽  
...  
Keyword(s):  
Infrared Spectrum ◽  
Blind Deconvolution ◽  
Differential Entropy

Differential-entropy identification of the resolution of the measurement transducers of navigation instruments

1998 ◽  
Vol 41 (10) ◽  
pp. 902-905
Author(s):  
V. V. Baranov ◽  
Yu. V. Bazanov ◽  
R. A. Doroshchuk
Keyword(s):  
Differential Entropy

scholarly journals Differential Entropy Feature Signal Extraction Based on Activation Mode and Its Recognition in Convolutional Gated Recurrent Unit Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Yongsheng Zhu ◽  
Qinghua Zhong
Keyword(s):  
Feature Extraction ◽  
Time Scales ◽  
Signal Acquisition ◽  
Differential Entropy ◽  
Signal Sequences ◽  
Power Spectral ◽  
Similar Accuracy ◽  
Gated Recurrent Unit ◽  
Unit Network ◽  
Different Time Scales

In brain-computer-interface (BCI) devices, signal acquisition via reducing the electrode channels can reduce the computational complexity of models and filter out the irrelevant noise. Differential entropy (DE) plays an important role in emotional components of signals, which can reflect the area activity differences. Therefore, to extract distinctive feature signals and improve the recognition accuracy based on feature signals, a method of DE feature signal recognition based on a Convolutional Gated Recurrent Unit network was proposed in this paper. Firstly, the DE and power spectral density (PSD) of each original signal were mapped to two topographic maps, and the activated channels could be selected in activation modes. Secondly, according to the position of original electrodes, 1D feature signal sequences with four bands were reconstructed into a 3D feature signal matrix, and a radial basis function interpolation was used to fill in zero values. Then, the 3D feature signal matrices were fed into a 2D Convolutional Neural Network (2DCNN) for spatial feature extraction, and the 1D feature signal sequences were fed into a bidirectional Gated Recurrent Unit (BiGRU) network for temporal feature extraction. Finally, the spatial-temporal features were fused by a fully connected layer, and recognition experiments based on DE feature signals at the different time scales were carried out on a DEAP dataset. The experimental results showed that there were different activation modes at different time scales, and the reduction of the electrode channel could achieve a similar accuracy with all channels. The proposed method achieved 87.89% on arousal and 88.69% on valence.

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