Multi-scale permutation entropy as a tool for complexity analysis of ship-radiated noise

2016 ◽  
Author(s):  
Yang Wang ◽  
Qing-yu Liu
Keyword(s):  
Complexity Analysis ◽  
Permutation Entropy ◽  
Radiated Noise ◽  
Multi Scale

scholarly journals Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 476
Author(s):  
Yuxing Li ◽  
Bo Geng ◽  
Shangbin Jiao
Keyword(s):  
Feature Extraction ◽  
High Efficiency ◽  
Recognition Rate ◽  
Coarse Graining ◽  
Classification Method ◽  
Permutation Entropy ◽  
Radiated Noise ◽  
Multi Scale ◽  
Single Scale ◽  
Extraction Scheme

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.

scholarly journals RCMFRDE: Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy for Feature Extraction of Ship-Radiated Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yuxing Li ◽  
Shangbin Jiao ◽  
Bo Geng ◽  
Xinru Jiang
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Complexity Analysis ◽  
Recognition Rate ◽  
Experimental Results ◽  
Coarse Grained ◽  
Permutation Entropy ◽  
K Nearest Neighbor ◽  
Distance Information ◽  
Radiated Noise

Dispersion entropy (DE), as a newly proposed entropy, has achieved remarkable results in its application. In this paper, on the basis of DE, combined with coarse-grained processing, we introduce the fluctuation and distance information of signal and propose the refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE). As an emerging complexity analysis mode, RCMFRDE has been used for the first time for the feature extraction of ship-radiated noise signals to mitigate the loss caused by the misclassification of ships on the ocean. Meanwhile, a classification and recognition method combined with K-nearest neighbor (KNN) came into being, namely, RCMFRDE-KNN. The experimental results indicated that RCMFRDE has the highest recognition rate in the single feature case and up to 100% in the double feature case, far better than multiscale DE (MDE), multiscale fluctuation-based DE (MFDE), multiscale permutation entropy (MPE), and multiscale reverse dispersion entropy (MRDE), and all the experimental results show that the RCMFRDE proposed in this paper improves the separability of the commonly used entropy in the hydroacoustic domain.

scholarly journals Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

Entropy ◽  
2012 ◽  
Vol 14 (7) ◽  
pp. 1186-1202 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Domenico Labate ◽  
Fabio La Foresta ◽  
Alessia Bramanti ◽  
Giuseppe Morabito ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Permutation Entropy ◽  
Multi Scale

Facial expression awareness based on multi-scale permutation entropy of EEG

2018 ◽  
Vol 21 (4) ◽  
pp. 287 ◽  
Author(s):  
Xiaofeng Liu ◽  
Bin Hu ◽  
Xiangwei Zheng ◽  
Xiaowei Li
Keyword(s):  
Facial Expression ◽  
Permutation Entropy ◽  
Multi Scale

Detecting the Flow Pattern Transition in the Gas-Liquid Two-Phase Flow Using Multivariate Multi-Scale Entropy Analysis

2019 ◽  
Vol 74 (10) ◽  
pp. 837-848 ◽  
Author(s):  
Yudong Liu ◽  
Dayang Wang ◽  
Yingyu Ren ◽  
Ningde Jin
Keyword(s):  
Flow Pattern ◽  
Two Phase Flow ◽  
Flow Patterns ◽  
Mean Value ◽  
Entropy Rate ◽  
Permutation Entropy ◽  
Phase Flow ◽  
Two Phase ◽  
Multi Scale ◽  
Flow Pattern Transition

AbstractDue to the complex flow structure and non-uniform phase distribution in the vertical upward gas-liquid two-phase flow, an eight-electrode rotating electric field conductance sensor is used to obtain multi-channel conductance signals. The flow patterns of the vertical upward gas-liquid two-phase flow are classified according to the images obtained from a high-speed camera. Then, we employ the multivariate weighted multi-scale permutation entropy (MWMPE) to detect the instability of flow pattern transition in the gas-liquid two-phase flow. Afterwards, we compare the results of the MWMPE with those of the single-channel weighted multi-scale permutation entropy (SCWMPE) and multivariate multi-scale sample entropy (MMSE). The comparison results indicate that, compared with the SCWMPE and MMSE, the MWMPE has superior performance in terms of the high-resolution presentation of flow instability in the gas-liquid two-phase flow. Finally, we extract the mean value of the MWMPE in whole scales and the entropy rate of the MWMPE in the small scales. The results indicate that the normalized mean value and normalized entropy rate of MWMPE are very sensitive to the transitions of flow patterns, thus allowing the detection of the instability of flow pattern transition.

scholarly journals The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 170 ◽  
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yu Wei ◽  
Yongbo Li
Keyword(s):  
Time Delay ◽  
Parameter Selection ◽  
Permutation Entropy ◽  
Embedding Dimension ◽  
Optimization Strategy ◽  
Extraction Ability ◽  
Dynamic Changes ◽  
Multi Scale ◽  
Simulated Signal ◽  
Selection Of

Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods.

scholarly journals Compacting the description of a time-dependent multivariable system and its multivariable driver by reducing the state vectors to aggregate scalars: the Earth's solar-wind-driven magnetosphere

2019 ◽  
Vol 26 (4) ◽  
pp. 429-443 ◽  
Author(s):  
Joseph E. Borovsky ◽  
Adnane Osmane
Keyword(s):  
Solar Wind ◽  
State Vector ◽  
Complexity Analysis ◽  
Low Noise ◽  
Time Dependent ◽  
Permutation Entropy ◽  
System Response ◽  
System A ◽  
Driven System ◽  
Time Dependent System

Abstract. Using the solar-wind-driven magnetosphere–ionosphere–thermosphere system, a methodology is developed to reduce a state-vector description of a time-dependent driven system to a composite scalar picture of the activity in the system. The technique uses canonical correlation analysis to reduce the time-dependent system and driver state vectors to time-dependent system and driver scalars, with the scalars describing the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The methodology identifies independent modes of reaction of a system to its driver. The analysis of the magnetospheric system is demonstrated. Using autocorrelation analysis, Jensen–Shannon complexity analysis, and permutation-entropy analysis the properties of the derived aggregate scalars are assessed and a new mode of reaction of the magnetosphere to the solar wind is found. This state-vector-reduction technique may be useful for other multivariable systems driven by multiple inputs.

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