scholarly journals Inverse Cascade Evidenced by Information Entropy of Passive Scalars in Submerged Canopy Flows

2020 ◽  
Vol 47 (9) ◽  
Author(s):  
Khaled Ghannam ◽  
Davide Poggi ◽  
Elie Bou‐Zeid ◽  
Gabriel G. Katul
Keyword(s):  
Information Entropy ◽  
Passive Scalars ◽  
Inverse Cascade ◽  
Canopy Flows

Approach for cleaning uncertain data based on information entropy theory

2013 ◽  
Vol 33 (9) ◽  
pp. 2490-2492
Author(s):  
Yuanxiang QIN ◽  
Liang DUAN ◽  
Kun YUE
Keyword(s):  
Information Entropy ◽  
Uncertain Data ◽  
Entropy Theory

Model of Trust Monitoring and Updating Based on Dynamic Weighted Allocation

2019 ◽  
Vol 12 (1) ◽  
pp. 79-85
Author(s):  
Bin Hu ◽  
Yuemin Wu ◽  
Min Sun ◽  
Zheng Bang Liu ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Information Entropy ◽  
Network Environment ◽  
Mathematical Methods ◽  
Efficient Operation ◽  
Open Network ◽  
Dynamic Trust ◽  
Effective Synthesis ◽  
Effectiveness And Efficiency ◽  
And Performance

Backgrounds: In order to guarantee safe and efficient operation interaction in open network environment, a new dynamic trust monitoring and updating model based on behavior context is proposed in this paper. Methods: Setting four behavior attributes such as security, availability, reliability and performance. Then utilizing the fuzzy clustering and information entropy mathematical methods to carry out the effective synthesis on such attributes. Conclusion: The effectiveness and efficiency of the schema are verified by simulation.

Inverse cascade in simulated MHD turbulence driven by small scale source of magnetic helicity

2016 ◽  
Vol 52 (1) ◽  
pp. 261-268
Author(s):  
R. Stepanov ◽  
◽  
V. Titov ◽  
◽  
Keyword(s):  
Magnetic Helicity ◽  
Small Scale ◽  
Mhd Turbulence ◽  
Inverse Cascade

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

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