RCMFRDE: Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy for Feature Extraction of Ship-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.

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Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

Entropy ◽

10.3390/e24010022 ◽

2021 ◽

Vol 24 (1) ◽

pp. 22

Author(s):

Yuxing Li ◽

Peiyuan Gao ◽

Bingzhao Tang ◽

Yingmin Yi ◽

Jianjun Zhang

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Nearest Neighbor ◽

Recognition Rate ◽

Noise Signal ◽

Permutation Entropy ◽

K Nearest Neighbor ◽

Feature Extraction Method ◽

Radiated Noise ◽

Single Feature

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.

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Feature Extraction of Ship Radiation Signals Based on Wavelet Packet Decomposition and Energy Entropy

Mathematical Problems in Engineering ◽

10.1155/2022/8092706 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Yuxing Li ◽

Feiyue Ning ◽

Xinru Jiang ◽

Yingmin Yi

Keyword(s):

Signal Processing ◽

Feature Extraction ◽

Nearest Neighbor ◽

Wavelet Packet ◽

Recognition Rate ◽

Center Frequency ◽

Permutation Entropy ◽

Wavelet Packet Decomposition ◽

K Nearest Neighbor ◽

Energy Entropy

The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.

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Recognition of 3D Objects from 2D Views Features

Journal of Electronic Commerce in Organizations ◽

10.4018/jeco.2015040105 ◽

2015 ◽

Vol 13 (2) ◽

pp. 50-58

Author(s):

R. Khadim ◽

R. El Ayachi ◽

Mohamed Fakir

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Color Image ◽

Recognition Rate ◽

Experimental Results ◽

Support Vector ◽

K Nearest Neighbor ◽

3D Objects ◽

Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

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Writer Identification Based on Arabic Handwriting Recognition by using Speed Up Robust Feature and K- Nearest Neighbor Classification

Journal of University of Babylon for Pure and Applied Sciences ◽

10.29196/jubpas.v27i1.2060 ◽

2019 ◽

Vol 27 (1) ◽

pp. 1-10

Author(s):

Alia Karim Abdul Hassan ◽

Bashar Saadoon Mahdi ◽

Asmaa Abdullah Mohammed

Keyword(s):

Feature Extraction ◽

Nearest Neighbor ◽

Recognition Rate ◽

Image Feature ◽

Writer Identification ◽

K Nearest Neighbor ◽

Nearest Neighbor Classification ◽

Speed Up ◽

Speed Up Robust Feature ◽

Neighbor Classification

In a writer recognition system, the system performs a “one-to-many” search in a large database with handwriting samples of known authors and returns a possible candidate list. This paper proposes method for writer identification handwritten Arabic word without segmentation to sub letters based on feature extraction speed up robust feature transform (SURF) and K nearest neighbor classification (KNN) to enhance the writer's identification accuracy. After feature extraction, it can be cluster by K-means algorithm to standardize the number of features. The feature extraction and feature clustering called to gather Bag of Word (BOW); it converts arbitrary number of image feature to uniform length feature vector. The proposed method experimented using (IFN/ENIT) database. The recognition rate of experiment result is (96.666).

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Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Sensors ◽

10.3390/s22010112 ◽

2021 ◽

Vol 22 (1) ◽

pp. 112

Author(s):

Hamada Esmaiel ◽

Dongri Xie ◽

Zeyad A. H. Qasem ◽

Haixin Sun ◽

Jie Qi ◽

...

Keyword(s):

Feature Extraction ◽

Recognition Rate ◽

Extraction Methods ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Radiated Noise ◽

Passive Sonar ◽

Multi Stage ◽

Mode Decomposition

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

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A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy

Entropy ◽

10.3390/e21060624 ◽

2019 ◽

Vol 21 (6) ◽

pp. 624 ◽

Cited By ~ 3

Author(s):

Zhe Chen ◽

Yaan Li ◽

Renjie Cao ◽

Wasiq Ali ◽

Jing Yu ◽

...

Keyword(s):

Feature Extraction ◽

Mutual Information ◽

Noise Reduction ◽

Extraction Method ◽

Recognition Rate ◽

Permutation Entropy ◽

Feature Extraction Method ◽

Normalized Mutual Information ◽

Radiated Noise ◽

New Feature

Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction methods for ship-radiated noise are less reliable under noisy conditions because they lack noise reduction procedures or are single-scale based. In order to simultaneously solve these problems, a new feature extraction method is proposed based on improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), normalized mutual information (norMI), and multiscale improved permutation entropy (MIPE). Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from ship-radiated noise. The noise reduction process is then conducted by identifying and eliminating the noise IMFs. Next, the norMI and MIPE of the signal-dominant IMFs are calculated, respectively; and the norMI is used to weigh the corresponding MIPE result. The multi-scale entropy feature is finally defined as the sum of the weighted MIPE results. Experimental results show that the recognition rate of the proposed method achieves 90.67% and 83%, respectively, under noise free and 5 dB conditions, which is much higher than existing entropy feature extraction algorithms. Hence, the proposed method is more reliable and suitable for feature extraction of ship-radiated noise in practice.

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Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽

10.3390/e22040468 ◽

2020 ◽

Vol 22 (4) ◽

pp. 468 ◽

Cited By ~ 1

Author(s):

Dongri Xie ◽

Hamada Esmaiel ◽

Haixin Sun ◽

Jie Qi ◽

Zeyad A. H. Qasem

Keyword(s):

Feature Extraction ◽

Correlation Coefficient ◽

Extraction Method ◽

Recognition Rate ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Feature Extraction Method ◽

Radiated Noise ◽

Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

Entropy ◽

10.3390/e23050503 ◽

2021 ◽

Vol 23 (5) ◽

pp. 503

Author(s):

Dongri Xie ◽

Shaohua Hong ◽

Chaojun Yao

Keyword(s):

Feature Extraction ◽

Recognition Rate ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Marine Animals ◽

Practical Applications ◽

Radiated Noise ◽

Mode Decomposition

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

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Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

Computational Intelligence and Neuroscience ◽

10.1155/2019/5627156 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Shan Guan ◽

Kai Zhao ◽

Shuning Yang

Keyword(s):

Feature Extraction ◽

Decision Tree ◽

Motor Imagery ◽

Nearest Neighbor ◽

Recognition Rate ◽

Classification Algorithm ◽

Classification Error ◽

K Nearest Neighbor ◽

Feature Extraction Algorithm ◽

Extraction Algorithm

This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.

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

Entropy ◽

10.3390/e23040476 ◽

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.

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