• Journal of Ocean Engineering and Technology
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• v.34 no.2
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• pp.147-154
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• 2020

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.371-376
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• 2020

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.

• Journal of Ocean Engineering and Technology
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• v.34 no.4
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• ETRI Journal
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• v.45 no.3
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• pp.379-393
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• 2023

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized leastmean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l₁-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3E
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• pp.104-111
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• 2008

Recently, a method for robust time reversal focusing has been introduced to extend the period of stable focusing in time-dependent ocean environments [S. Kim et al., J. Acoust. Soc. Am. 114, 145-157, (2003)]. In this study, concept of focal-size broadening based on waveguide invariant theory in an ocean time reversal acoustics is described. It is achieved by imposing the multiple location constraints. The signal vector used in multiple location constraints are found from the theory on waveguide invariant for frequency band corresponding the extended focal range. The broadening of foci in an ocean waveguide can play an important role in the application of time reversal processing, particularly to the underwater acoustic communication with moving vehicles. The proposed method is demonstrated in the context of the underwater acoustic communication from the transmit/receive array (TRA) to a slowly moving vehicle.

• The Journal of the Acoustical Society of Korea
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• v.25 no.5
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• pp.239-245
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• 2006

Time-reversal processing (TRP) has been shown as an effective way to focus in both time and space. The temporal focusing properties have been used extensively in underwater acoustics communications. Recently. adaptive time-reversal processing (ATRP) was applied to the simultaneous multiple focusing in an ocean waveguide. In this study. multiple focusing with ATRP is extended to the underwater acoustic communication algorithm for multiple receiving locations. The developed algorithm is applied to the underwater acoustic communication to show, via simulation and real data, that the simultaneous self-equalization at multiple receiving locations is achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.546-555
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• 2013

In this paper, we propose a novel method to enhance the harmonic components from the frequency lines of the passive sonar signals. For this, we first separate the stable frequency lines from unstable ones using mean and difference of spectral bins in the vernier analysis. Then we emphasize the harmonic components using autocorrelation-based postprocessing, and enhance them by reducing the background noise with the split-window two pass mean algorithm. Experimental results for real underwater acoustic data are presented with our discussions.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.1-8
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• 2011

In this paper, a water tank experiment using Binary Frequency Shift Keying (BFSK) method for text transmission performance by water surface fluctuation is conducted. Water surface fluctuation and delay spread which affect the channel coherence bandwidth is a limiting factor in underwater acoustic communication. The amplitude fluctuation and delay spread the smooth surface and fluctuation surface, were identified. The effective delay spread of both cases are 5ms, 4ms corresponding to the coherence bandwidth of 200Hz, 250Hz, respectively. The bit error rate of BFSK modulated text transmission is about $10^{-4}$ in less than 200bps in smooth surface but less than 250bps in fluctuation surface. Therefore, this experiment shows that the water surface fluctuation is important factor determining the performance of the underwater acoustic transmission.

• The Journal of the Acoustical Society of Korea
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• v.33 no.5
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• pp.273-281
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• 2014

This paper analyzes the effect of a thermocline on the long-range acoustic signal propagation using the experimental data acquired in the shallow water near Jeju island. Temperature and salinity measurement data in Korea Oceanographic Data Center (KODC) show that the seasonal thermocline exists near Jeju island, and, under the thermocline, the bottom loss property strongly affects the long-range propagation of acoustic signal along the down-ward refractive paths. We estimate the bottom loss under the thermocline using experiment data obtained near Jeju island in May, 2013. The result shows that the estimated bottom losses are below 3 dB and the higher level signal is received at the deeper receiver depths. This shows that the acoustic trapping under the thermocline can be a viable long-range signal transmission channel in the shallow water with a thermocline.