Hierarchical Cosine Similarity Entropy for Feature Extraction of Ship-Radiated Noise

Chen, Zhe; Li, Yaan; Liang, Hongtao; Yu, Jing

doi:10.3390/e20060425

Cited by 23 publications

(20 citation statements)

References 31 publications

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“…There are some parameters that need to be predetermined for computing the IPE and MIPE algorithm, such as embedding dimension , time delay , discretization level , scale factor , and data-length . The time lag is analogous to downsampling to some extent, and = 1 is usually taken for structural preservation [30,31]. Without specification, = 1 is chosen for subsequent study.…”

Section: Selection Of Parametersmentioning

confidence: 99%

Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Chen

Liang

et al. 2019

Complexity

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Measuring complexity of observed time series plays an important role for understanding the characteristics of the system under study. Permutation entropy (PE) is a powerful tool for complexity analysis, but it has some limitations. For example, the amplitude information is discarded; the equalities (i.e., equal values in the analysed signal) are not properly dealt with; and the performance under noisy condition remains to be improved. In this paper, the improved permutation entropy (IPE) is proposed. The presented method combines some advantages of previous modifications of PE. Its effectiveness is validated through both synthetic and experimental analyses. Compared with PE, IPE is capable of detecting spiky features and correctly differentiating heart rate variability (HRV) signals. Moreover, it performs better under noisy condition. Ship classification experiment results demonstrate that IPE achieves 28.66% higher recognition rate than PE at 0dB. Hence, IPE could be used as an alternative of PE for analysing time series under noisy condition.

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Section: Selection Of Parametersmentioning

confidence: 99%

Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Chen

Liang

et al. 2019

Complexity

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“…The proposed method 98.3333% The DSE of original ship-radiated noise 48.3333% The EMD-PIMF-PE method [6] 70% The IMF-norMI-DSE method 66.6667%…”

Section: Accuracy Ratementioning

confidence: 99%

“…Consequently, the identification of ship-radiated noise plays a significant role in the military and economic fields [1][2][3]. Due to the influence of marine environment noise, the actual measured ship-radiated noise is non-Gaussian, non-stationary, and non-linear [4][5][6]. Therefore, it is an arduous task to identify ship-radiated noise effectively.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [21] used EMD to decompose the ship-radiated noise, taking the PE of the IMF with the highest energy as the feature vector. Chen et al [6] proposed a feature extraction method of ship-radiated noise based on hierarchical cosine similar entropy. Compared with multi-scale sample entropy, the classification accuracy was significantly improved from 75%-95.63%.…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy

Yang

2019

Entropy

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To improve the recognition accuracy of ship-radiated noise, a feature extraction method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD), mutual information (MI), and differential symbolic entropy (DSE) is proposed in this paper. RPSEMD is an improved empirical mode decomposition (EMD) that alleviates the mode mixing problem of EMD. DSE is a new tool to quantify the complexity of nonlinear time series. It not only has high computational efficiency, but also can measure the nonlinear complexity of short time series. Firstly, the ship-radiated noise is decomposed into a series of intrinsic mode functions (IMFs) by RPSEMD, and the DSE of each IMF is calculated. Then, the MI between each IMF and the original signal is calculated; the sum of MIs is taken as the denominator; and each normalized MI (norMI) is obtained. Finally, each norMI is used as the weight coefficient to weight the corresponding DSE, and the weighted DSE (WDSE) is obtained. The WDSEs are sent into the support vector machine (SVM) classifier to classify and recognize three types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 98.3333%. Consequently, the proposed WDSE method can effectively achieve the classification of ships.

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“…The similarity of this type evaluates the degree to which coverage is consistent with the service demands (∼w (e) j ). The use of other ways of thinking on research components, such as hierarchical entropy and cosine correlation, is also very useful [38].…”

Section: Simulations and Performance Analysismentioning

confidence: 99%

Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

Horváth

Gazda

Šlapak

et al. 2019

Entropy

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Attempts to develop flexible on-demand drone-assisted mobile network deployment are increasingly driven by cost-effective and energy-efficient innovations. The current stage opens up a wide range of theoretical discussions on the management of swarm processes, networks and other integrated projects. However, dealing with these complex issues remains a challenging task, although heuristic approaches are usually utilized. This article introduces a model of autonomous and adaptive drones that provide the function of aerial mobile base stations. Its particular goal is to analyze post-disaster recovery if the network failure takes place. We assume that a well-structured swarm of drones can re-establish the connection by spanning the residual functional, fixed infrastructure, and providing coverage of the target area. Our technique uses stochastic Langevin dynamics with virtual and adaptive forces that bind drones during deployment. The system characteristics of the swarms are a priority of our focus. The assessment of parametric sensitivity with the insistence on the manifestation of adaptability points to the possibility of improving the characteristics of the swarms in different dynamic situations.

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Hierarchical Cosine Similarity Entropy for Feature Extraction of Ship-Radiated Noise

Cited by 23 publications

References 31 publications

Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy

Modeling and Analysis of Self-Organizing UAV-Assisted Mobile Networks with Dynamic On-Demand Deployment

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