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In this study, the three-dimensional Cartesian acoustic parabolic equation is fully solved in the stair-step representation of an ocean environment based on the split-step Padé algorithm. The sum representation of the Padé approximation of a full exponential operator in the split-step marching solution is used for parallel computing. A stabilized self-starter solution is derived in the same way. This solver is benchmarked against reference solutions for a rectangular duct propagation, an ideal wedge problem, and an axisymmetric ocean. Additionally, a solution for the Gaussian canyon problem is provided and compared with that obtained from the approximate parabolic equation solver, considering a leading-order cross-term. [ABSTRACT FROM AUTHOR]

An approximate form of three-dimensional Cartesian split-step marching solution for the acoustic parabolic equation is derived in order to obtain the efficient algorithm for sound propagation in the three-dimensional ocean. The operator splitting method is used to split the full exponential operator into three exponential operators for depth, cross-range, and the combination of the two. The first two terms are implemented with the split-step Padé algorithm and the final term is implemented with the Taylor series expansion in depth and cross-range operator. In order to resolve the divergence of Taylor approximation out of the interval of convergence, the rational filter of rectangular type is applied to the depth and cross-range operator. The use of the filter improves the stability of the solution but requires extra numerical burdens. Numerical issues involving the accuracy, efficiency, and stability of the proposed model are discussed and illustrated in an ocean wedge environment. [ABSTRACT FROM AUTHOR]

This paper presents a practical technique to evaluate the Percus–Yevick (PY) radial distribution function (RDF) for a water-saturated granular medium with a high volume fraction of grains. This is based on the weighted average of the PY RDF׳s recursive formula (Goodwin et al., 1992) and the PY integral equation׳s numerical solution using the filtered fast Fourier transform. In a highly dense medium, the combination of both models produces more accurate and stable results. The error analysis for the volume fraction of scatterers and the distance is given for validation. Finally, using the proposed PY RDF result, we solve an acoustic multiple-scattering equation with quasi-crystalline approximation (QCA) to obtain the effective bulk modulus and attenuation of the water-saturated granular medium, and confirm that our method provides a reasonable result. [ABSTRACT FROM AUTHOR]

Marine propeller is the dominant exciter of the hull surface above it causing high level of noise and vibration in the ship structure. Recent successful developments have led to non-cavitating propeller designs and thus present focus is the non-cavitating characteristics of propeller such as hydrodynamic noise and its induced hull excitation. In this paper, analytic source model of propeller non-cavitating noise, described by longitudinal quadrupoles and dipoles, is suggested based on the propeller hydrodynamics. To find the source unknown parameters, the multi-parameter inversion technique is adopted using the pressure data obtained from the model scale experiment and pressure field replicas calculated by boundary element method. The inversion results show that the proposed source model is appropriate in modeling non-cavitating propeller noise. The result of this study can be utilized in the prediction of propeller non-cavitating noise and hull excitation at various stages in design and analysis. [ABSTRACT FROM AUTHOR]

Abstract: Propeller sheet cavitation is the main contributor to high level of noise and vibration in the after body of a ship. Full measurement of the cavitation-induced hull pressure over the entire surface of the affected area is desired but not practical. Therefore, using a few measurements on the outer hull above the propeller in a cavitation tunnel, empirical or semi-empirical techniques based on physical model have been used to predict the hull-induced pressure (or hull-induced force). In this paper, with the analytic source model for sheet cavitation, a multi-parameter inversion scheme to find the positions of noise sources and their strengths is suggested. The inversion is posed as a nonlinear optimization problem, which is solved by the optimization algorithm based on the adaptive simplex simulated annealing algorithm. Then, the resulting hull pressure can be modeled with boundary element method from the inverted cavitation noise sources. The suggested approach is applied to the hull pressure data measured in a cavitation tunnel of the Samsung Heavy Industry. Two monopole sources are adequate to model the propeller sheet cavitation noise. The inverted source information is reasonable with the cavitation dynamics of the propeller and the modeled hull pressure shows good agreement with cavitation tunnel experimental data. [Copyright &y& Elsevier]

The classical ray approach for monostatic ocean boundary reverberation has been re-examined based on a geometrical ray-bundle concept. In this new formulation, the impulse response for the averaged scattering intensity is expressed by a simple function consisting of continuous ray-bundle quantities with respect to the time, and which can be regarded as a generalized function for ray-based reverberation in a boundary cell. To numerically evaluate this impulse response, a zeroth- and a first-order polynomial interpolation method are respectively applied to approximate the ray-bundle quantities. Then, the impulse function is reduced to the forms of the delta function and the rectangular function either with or without linear amplitude modulation linear in time. These basic functions are used to develop an explicit numerical scheme for the monostatic reverberation, which originates in the split-step marching algorithm for the range. This numerical scheme provides a considerable accuracy even with larger range steps and gives reasonable results for numerical examples of ocean waveguides with isovelocity, summer, and winter sound-speed profiles. [ABSTRACT FROM AUTHOR]

This study was undertaken to investigate the types and concentrations of microbial agents in various medical wastes as well as to characterize their survivals in these wastes at different temperatures for microbial risk assessment. Medical wastes collected from 5 major hospitals in South Korea were classified and stored at three different temperatures (-20, 6, and 30°C). Presence of various microorganisms such as pathogenic viruses and bacteria were investigated by both cultivation and by (RT)-PCR assays. A number of (opportunistic) pathogenic bacteria, including Pseudomonas spp., Lactobacillus spp., Staphylococcus spp., Micrococcus spp., Kocuria spp., Brevibacillus spp., Microbacterium oxydans, and Propionibacterium acnes, were identified from the various medical wastes. In addition, pathogenic viruses such as noroviruses and hepatitis B virus were also detected in one of the human tissue wastes. Commonly identified bacterial and viral pathogens such as Pseudomonas spp., Corynebacterium diphtheriae, Escherichia coli, Staphylococcus spp., and respiratory synctial virus (RSV) were inoculated into either gauzes or diapers, and their survivals were characterized. Viral agents such as RSV showed poor survival in most environmental conditions, and demonstrated that various pathogens could be present in medical wastes but that the associated health risk appeared to be low. However, medical waste should be carefully controlled and monitored to prevent nosocomial infection associated with the exposure to these wastes. [ABSTRACT FROM AUTHOR]

In this study, near-field acoustic scattering from a finite elastic cylindrical shell is investigated based on a previous cylinder method. An analytic expression for scattered pressure is derived in the form of a Kirchhoff–Helmholtz integral with the assumption that the field induced on the cylinder surface is determined by the canonical solution of an infinite elastic cylindrical shell. A useful approximate solution of the integral is obtained in the near-field zone of the receiver and then applied to formulate the near-field target strength. The results of the approximate solution are compared with the full numerical solution and experimental data measured using a linear frequency-modulated continuous-wave pulse at a frequency range of 5 to 25 kHz in a water tank in a laboratory. The results indicate consistency, except for some discrepancies due to experimental restrictions. The approximate solution is validated via finite element modeling using COMSOL Multiphysics. Moreover, the range dependency of the near-field formula and the effect of the end-caps in the finite cylindrical shell are investigated through simulations. [ABSTRACT FROM AUTHOR]

Accurate estimation of the frequency component is an important issue to identify and track marine objects (e.g., surface ship, submarine, etc.). In general, a passive sonar system consists of a sensor array, and each sensor receives data that have common information of the target signal. In this paper, we consider multiple-measurement sparse Bayesian learning (MM-SBL), which reconstructs sparse solutions in a linear system using Bayesian frameworks, to detect the common frequency components received by each sensor. In addition, the direction of arrival estimation was performed on each detected common frequency component using the MM-SBL based on beamforming. The azimuth for each common frequency component was confirmed in the frequency-azimuth plot, through which we identified the target. In addition, we perform target tracking using the target detection results along time, which are derived from the sum of the signal spectrum at the azimuth angle. The performance of the MM-SBL and the conventional target detection method based on energy detection were compared using in-situ data measured near the Korean peninsula, where MM-SBL displays superior detection performance and high-resolution results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l 1 -norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity. [ABSTRACT FROM AUTHOR]

Noise induced by incipient-propeller tip vortex cavitation (TVC) has a few sources near the propeller tips, which radiate a broadband signal. This article describes a compressive sensing (CS)-based TVC localization technique for coherent multiple-frequency processing, which jointly processes the measured data at multiple frequencies. Block-sparse CS, which groups several single-frequency measurements into blocks, is adopted for coherent multiple-frequency processing. The coherent multiple-frequency processing improves localization performance over that of single-frequency processing. Unlike single-frequency processing using conventional CS, which combines independent single-frequency measurement treatments by averaging, coherent multiple-frequency processing produces accurate localization without requiring a sufficient number of treated frequencies, long-time-sampled data with a time-invariant signal assumption, or even a single cavitation event. The approach is demonstrated on experimental data from a transducer source experiment and a cavitation source experiment. [ABSTRACT FROM AUTHOR]

Frequency-difference matched-field processing is a high-frequency source localization technique formulated by matching the frequency-difference autoproduct of the measured field and replicas at the difference-frequency. Although it successfully localizes sound sources by sparse vertical array in shallow or deep ocean with an environmental mismatch, there is still some ambiguity in replica modeling and signal processing. Here, the existing conventional processor is modified to match the bandwidth-averaged autoproduct of the measured field with replicas of the bandwidth-averaged autoproduct, or approximately its self-term for the expected source locations. The proposed processor is consistent with the perspective of matched-field processing and can naturally relieve some drawbacks of the existing one, such as low peak or low dynamic range on the ambiguity surface. Numerical tests are carried out in several shallow ocean environments and the source localization using experimental data are performed to confirm the properties of the proposed processor. It is found that the high-frequency diffracted field always leaves traces on its bandwidth-averaged autoproduct field. These high-frequency marks cause a bias in source localization in the presence of a sound speed mismatch even in low difference-frequencies. [ABSTRACT FROM AUTHOR]

In order to assess the damage detectability of the structural health monitoring method using diffuse fields, a laboratory experiment is conducted on an aluminum plate where two accelerometers are mounted as receivers. Two types of damages, a nonlinear material attachment and a punctuated hole, are considered. A hand-held impact hammer is used for the excitation, whereby the hammer is moved over grid points drawn on the aluminum plate, and thus the diffuse fields are generated by superposing the wave fields by many excitations randomly sampled. From the cross-correlation of diffuse fields between two receivers, we extract the coherent wave field in cases with and without damages. To detect the damage, a novel damage detection algorithm using a support vector machine is suggested based on the reduced features, transformed from several statistical parameters of damaged and undamaged noise cross-correlation functions, aided by the principal component analysis. The performance of the proposed algorithm is analyzed for the number of sources and damage types. [ABSTRACT FROM AUTHOR]

Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation. [ABSTRACT FROM AUTHOR]

Several acoustic models, such as the poro-elastic model, visco-elastic model, and multiple scattering model, have been used for describing the dispersion relation in a porous granular medium. However, these models are based on continuum or scattering theory, and therefore cannot explain the broadband measurements in cases where scattering and non-scattering losses co-exist. Additionally, since the models assume that the porous granular medium consists of grains of identical size (unimodal size distribution), the models does not account for the behavior of wave dispersion in a medium that has a distribution of differing grain sizes. As an alternative approach, this study proposes a new broadband attenuation model that describes the high frequency dispersion relation for the p-wave in the case of elastic grain scatterers existing in the background fluid medium. The broadband model combines the Biot-Stoll plus grain contact squirt and shear flow (BICSQS) model and the quasicrystalline approximation (QCA) multiple scattering model. Additionally, distribution of grain size effect is examined rudimentarily through consideration of bimodal grain size distribution. Through the quantitative analysis of the broadband model and measured data, it is shown that the model can explain the attenuation dependencies of frequency and grain size distribution for a water-saturated granular medium in the frequency range from 350 kHz to 1.1 MHz. This study can be applied to the high frequency acoustic SONAR modeling and design in the water-saturated environment. [ABSTRACT FROM AUTHOR]

An efficient algorithm for long-range monostatic reverberation is developed using geometrical ray-bundle as its propagation model. In the standard approach for reverberation modeling, scattered signals are numerically integrated along the rough surface. In this work, the integration along the rough surface is converted to an integration along the delay time of source signal. It can be expressed in an analytic form under the assumption of ignoring time-dispersion effect. Repetitive eigen-ray searches for calculating scattered signals are avoided by using a split-step marching algorithm for the range based on the geometrical ray-bundle. When the bottom is range-dependent, arrival times of scattered signals from the rough bottom at same range is different according to azimuthal angles and they cannot be directly summed in time domain. The reverberation signals along the azimuthal angles are interpolated to obtain new reverberation signals at certain times. They are summed to compute a time series reverberation signal from the rough sloping bottom. The algorithm is applied to problems of reverberation modeling workshop, and its results are compared with those from the standard reverberation algorithm. [ABSTRACT FROM AUTHOR]

This paper describes the results of range-dependent geoacoustic inversion using vertical line array data obtained from the 4th Matched Acoustic Properties and Localization Experiment conducted in the East Sea of Korea. The narrowband multitone continuous-wave signal from the towed source was analyzed to estimate the range-dependent geoacoustic properties along the radial track. The primary approach is based on the sectorwise inversion scheme. The inversion region up to 7.5 km from the vertical line array was divided into several segments, and the subinversions for each segment were performed sequentially. To reduce the dominance of low-angle arrivals, which bears little information for the bottom segment in question, matched beam processing with beam filtering was used for the cost function. The performance of proposed algorithm was tested using simulated data for an environment representative of the experimental site. The inversion results for the experimental data were consistent with the geophysical database and were validated from matched-field source localization using frequencies different from those used in the inversion. [ABSTRACT FROM AUTHOR]

Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components. [ABSTRACT FROM AUTHOR]

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean. [ABSTRACT FROM AUTHOR]