The Experts below are selected from a list of 5307 Experts worldwide ranked by ideXlab platform
Hong Yang - One of the best experts on this subject based on the ideXlab platform.
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A New Hybrid Model for Underwater Acoustic Signal Prediction
Complexity, 2020Co-Authors: Wanni Chang, Hong YangAbstract:The prediction of Underwater Acoustic Signal is the basis of Underwater Acoustic Signal processing, which can be applied to Underwater target Signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of Underwater Acoustic Signal. Aiming at the difficulty in Underwater Acoustic Signal sequence prediction, a new hybrid prediction model for Underwater Acoustic Signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of Underwater Acoustic Signal sequence and improve operation efficiency, the original Signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the Signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.
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Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine
Complexity, 2020Co-Authors: Hong Yang, Gao LipengAbstract:Aiming at the chaotic characteristics of Underwater Acoustic Signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of Underwater Acoustic Signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an Underwater Acoustic Signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and Underwater Acoustic Signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of Underwater Acoustic Signal series.
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Underwater Acoustic Signal Prediction Based on Correlation Variational Mode Decomposition and Error Compensation
IEEE Access, 2020Co-Authors: Hong Yang, Gao LipengAbstract:Underwater Acoustic Signal is highly complex and difficult to predict. To improve the prediction accuracy of Underwater Acoustic Signal, a complex Underwater Acoustic Signal prediction method combining correlation variational mode decomposition (CVMD), least squares support vector machine (LSSVM) and Gaussian process regression (GPR) is proposed. Aiming at the problem of sample partitioning, this paper proposes a method of obtaining the embedding dimension and time delay based on the extreme learning machine prediction model. By selecting the appropriate time delay and embedding dimension, the prediction accuracy has improved. Aiming at the K-value selection of variational mode decomposition (VMD), this paper proposes a CVMD decomposition method, which improves the adaptability of VMD algorithm by selecting K-value through the correlation coefficient. Firstly, CVMD is used to decompose the Underwater Acoustic time series into several different components. Then, LSSVM prediction models are established for each component. Finally, to further improve the prediction accuracy of the model, Gaussian process regression (GPR) is used to correct the prediction result. One-step and multi-step prediction of Underwater Acoustic time series is carried out in this paper. Simulation results show that the model proposed in this paper has high prediction accuracy and can be effectively used in Underwater Acoustic Signal prediction.
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A New Denoising Method for Underwater Acoustic Signal
IEEE Access, 2020Co-Authors: Hong YangAbstract:In recent years, the rapid development of marine science has put forward higher and higher requirements for the processing of ship-radiated noise Signal. Ship-radiated noise is the noise Signal generated by the vibration of various mechanical equipment or the movement of the hull and radiated into the sea when the ship is traveling. Ship-radiated noise Signal contains a large number of time-varying, nonlinear and non-stationary components. The denoising processing of ship-radiated noise is the most critical part of Underwater Acoustic Signal processing. In order to more effective reduce the noise of the ship-radiated noise Signal, a new denoising method for Underwater Acoustic Signal based on mutual information variational mode decomposition (MIVMD), multivariate multiscale dispersion entropy (mvMDE), and lift wavelet threshold (LWTD) and Savitzky Golay filter (S-G filter), named MIVMD-mvMDE-LWTD-SG, is proposed. Firstly, MIVMD is used to decompose the original Signal into $n$ sub-Signals. Secondly, the mvMDE value of each sub-Signal is calculated, and the $n$ sub-Signals are divided into high-frequency components and low-frequency components according to the threshold. Then, S-G filter and LWTD method are used to reduce the noise of low-frequency components and high-frequency components respectively. Finally, the low-frequency components and high-frequency components after the denoising processing are reconstructed to obtain the denoising Signal. In order to verify the effectiveness of the proposed method, the proposed method is used to reduce the noise of chaotic Signal under different Signal-to-noise ratios (SNR), and compared with the EMD-mvMDE-LWTD and MIVMD-mvMDE-LWTD method. The results show that the proposed method can effective remove the noise in the chaotic Signal, better distinguish the adjacent trajectories in the phase space, approximate the real chaotic attractor trajectory, and better retain the useful information in the chaotic Signal. The proposed method is further applied to the actual ship-radiated noise Signal, and the experimental analysis shows its effectiveness, which lays a solid foundation for further prediction and detection.
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A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising.
Entropy (Basel Switzerland), 2018Co-Authors: Xiao Chen, Hong Yang, Long WangAbstract:Owing to the complexity of the ocean background noise, Underwater Acoustic Signal denoising is one of the hotspot problems in the field of Underwater Acoustic Signal processing. In this paper, we propose a new technique for Underwater Acoustic Signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information (MI), permutation entropy (PE), and wavelet threshold denoising. CEEMDAN is an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD). First, CEEMDAN is employed to decompose noisy Signals into many intrinsic mode functions (IMFs). IMFs can be divided into three parts: noise IMFs, noise-dominant IMFs, and real IMFs. Then, the noise IMFs can be identified on the basis of MIs of adjacent IMFs; the other two parts of IMFs can be distinguished based on the values of PE. Finally, noise IMFs were removed, and wavelet threshold denoising is applied to noise-dominant IMFs; we can obtain the final denoised Signal by combining real IMFs and denoised noise-dominant IMFs. Simulation experiments were conducted by using simulated data, chaotic Signals, and real Underwater Acoustic Signals; the proposed denoising technique performs better than other existing denoising techniques, which is beneficial to the feature extraction of Underwater Acoustic Signal.
Gao Lipeng - One of the best experts on this subject based on the ideXlab platform.
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Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine
Complexity, 2020Co-Authors: Hong Yang, Gao LipengAbstract:Aiming at the chaotic characteristics of Underwater Acoustic Signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of Underwater Acoustic Signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an Underwater Acoustic Signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and Underwater Acoustic Signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of Underwater Acoustic Signal series.
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Underwater Acoustic Signal Prediction Based on Correlation Variational Mode Decomposition and Error Compensation
IEEE Access, 2020Co-Authors: Hong Yang, Gao LipengAbstract:Underwater Acoustic Signal is highly complex and difficult to predict. To improve the prediction accuracy of Underwater Acoustic Signal, a complex Underwater Acoustic Signal prediction method combining correlation variational mode decomposition (CVMD), least squares support vector machine (LSSVM) and Gaussian process regression (GPR) is proposed. Aiming at the problem of sample partitioning, this paper proposes a method of obtaining the embedding dimension and time delay based on the extreme learning machine prediction model. By selecting the appropriate time delay and embedding dimension, the prediction accuracy has improved. Aiming at the K-value selection of variational mode decomposition (VMD), this paper proposes a CVMD decomposition method, which improves the adaptability of VMD algorithm by selecting K-value through the correlation coefficient. Firstly, CVMD is used to decompose the Underwater Acoustic time series into several different components. Then, LSSVM prediction models are established for each component. Finally, to further improve the prediction accuracy of the model, Gaussian process regression (GPR) is used to correct the prediction result. One-step and multi-step prediction of Underwater Acoustic time series is carried out in this paper. Simulation results show that the model proposed in this paper has high prediction accuracy and can be effectively used in Underwater Acoustic Signal prediction.
Richard J. Vaccaro - One of the best experts on this subject based on the ideXlab platform.
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1999 Underwater Acoustic Signal Processing Workshop
1999Co-Authors: Richard J. VaccaroAbstract:Abstract : Contains abstracts for 22 talks given at the 1999. Underwater Acoustic Signal Processing Workshop. The workshop included a special session on the detection; classification, and localization of marine mammals.
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the past present and the future of Underwater Acoustic Signal processing
IEEE Signal Processing Magazine, 1998Co-Authors: Richard J. VaccaroAbstract:This is a collection of articles written by members of the Underwater Acoustic Signal Processing (UASP) Technical Committee. The first article, by D. W. Tufts, deals with the history of UASP prior to 1980. In this period, initial mathematical models were developed and the first experimental investigations of Underwater Acoustic propagation were performed. It was also recognized during this time that there are many similarities between radar and sonar Signal processing. The article by J.P. Ianniello deals with research in passive and active sonar from 1980 to the present. Work in this period included experimental verification of algorithms that had been developed in the 1960s and 1970s (e.g. for adaptive beamforming), as well as the development of new approaches, which include Acoustic propagation modeling in the design of Signal processing algorithms. Such processing is referred to as matched field processing. A common task in passive sonar systems is to estimate the difference in times at which different sensors receive the same Signal. Time-delay estimation is a first stage that feeds into subsequent processing blocks. I. Lourtie provides a concise review of work in this field. The article by J.C. Preisig deals with Underwater Acoustic communications. The Underwater channel has several features that make reliable communication a challenging problem. Nevertheless, progress is being made by combining results from ocean Acoustic modeling, communication theory, and Signal processing. The final article, by J.M.F. Moura, deals with the future of Signal processing in the ocean. In addition to considering advances in detection and localization, he deals with new applications such as Acoustic tomography, physical oceanography, and synthetic aperture sonar.
Xinyuan Dong - One of the best experts on this subject based on the ideXlab platform.
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self contained high snr Underwater Acoustic Signal acquisition node and synchronization sampling method for multiple distributed nodes
Sensors, 2019Co-Authors: Jiajia Jiang, Han Liu, Fajie Duan, Xianquan Wang, Zhongbo Sun, Xinyuan DongAbstract:Aiming at the application demand in Underwater noise monitoring, observation of marine animal, antisubmarine and Underwater target localization, a high-SNR Underwater Acoustic Signal acquisition (UASA) node that combines a self-contained acquisition system and floating platform is designed to improve the acquisition performance of a single UASA node, and a high-accuracy synchronization sampling method among multiple distributed UASA nodes based on master-slave dual phase-locked loops (MSDPLL) is proposed to improve the synchronization sampling accuracy. According to the equivalent model of hydrophone and application requirements, low noise Signal conditioning circuit and large-capacity data storage modules are designed. Based on the long-term monitoring requirements for Underwater Acoustic Signal and distributed positioning requirements for Underwater targets, the structure of a single UASA node is designed and MSDPLL is developed for high-accuracy synchronization sampling among multiple UASA nodes. Related experimental results verified the performance of the UASA node and the synchronization sampling method.
Aaron T Gulliver - One of the best experts on this subject based on the ideXlab platform.
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a novel Underwater Acoustic Signal denoising algorithm for gaussian non gaussian impulsive noise
IEEE Transactions on Vehicular Technology, 2021Co-Authors: Jingjing Wang, Shefeng Yan, Wei Shi, Xinghai Yang, Ying Guo, Aaron T GulliverAbstract:Gaussian/non-Gaussian impulsive noises in Underwater Acoustic (UWA) channel seriously impact the quality of Underwater Acoustic communication. The common denoising algorithms are based on Gaussian noise model and are difficult to apply to the coexistence of Gaussian/non-Gaussian impulsive noises. Therefore, a new UWA noise model is described in this paper by combining the symmetric $\alpha$ -stable (S $\alpha$ S) distribution and normal distribution. Furthermore, a novel Underwater Acoustic Signal denoising algorithm called AWMF+GDES is proposed. First, the non-Gaussian impulsive noise is adaptively suppressed by the adaptive window median filter (AWMF). Second, an enhanced wavelet threshold optimization algorithm with a new threshold function is proposed to suppress the Gaussian noise. The optimal threshold parameters are obtained based on good point set and dynamic elite group guidance combined simulated annealing selection artificial bee colony (GDES-ABC) algorithm. The numerical simulations demonstrate that the convergence speed and the convergence precision of the proposed GDES-ABC algorithm can be increased by 25% $\sim$ 66% and 21% $\sim$ 73%, respectively, compared with the existing algorithms. Finally, the experimental results verify the effectiveness of the proposed Underwater Acoustic Signal denoising algorithm and demonstrate that both the proposed wavelet threshold optimization method based on GDES-ABC and the AWMF+GDES algorithm can obtain higher output Signal-to-noise ratio (SNR), noise suppression ratio (NSR), and smaller root mean square error (RMSE) compared with the other algorithms.
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adoption of hybrid time series neural network in the Underwater Acoustic Signal modulation identification
Journal of The Franklin Institute-engineering and Applied Mathematics, 2020Co-Authors: Yan Wang, Hao Zhang, Conghui Cao, Aaron T GulliverAbstract:Abstract The deep learning methods powerfully enhance the identification performance by retrieving the deep data features in many fields, which can be used in automatic modulation classification (AMC) work for the better results in the Acoustic Underwater communication. A novel hybrid time series network structure is scheduled for AMC in this paper. It can accommodate the variable-length Signal datas to match the fixed-length input request in the common neural network, and there is the ability to suitably deal with the zero data in the Signal sequence to alleviate the effect losses. The proposed network has the mix of two time series network styles to enrich the extracted Signal modulation classification features, and dramatically improves the recognition capability and owns the low computation complexity. In the meanwhile, the internal network structure is optimized by the well-designed cascading order, which acquires more hidden Signal data representations to increase the accuracy. The simulation experiments shows that the proposed network is more effective and robust than the conventional deep learning methods to identify ten modulation modes in the serious interference communication environment.
