• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.148-158
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• 2015

It is difficult to test a detection system in underwater acoustics channel environments. The system can be evaluated by using simulation analysis tool. In this paper, a simulation tool is proposed to analyze the effectiveness of underwater acoustics detection based on database for real environments. First, the underwater environment is built based on HYCOM underwater environment database. Then, a multipath characteristic is considered through calculating underwater acoustics propagation path/pressure based on the ray theory. Also, hydrophone thermal noise and underwater ambient noise are considered to reflect underwater noise characteristics.

• 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.

• Advances in aircraft and spacecraft science
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• v.3 no.3
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• pp.259-269
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• 2016

In this work results from an underwater experiment on flow-induced noise in the interior of a towed body generated from a surrounding turbulent boundary layer are presented. The measurements were performed with a towed body under open sea conditions at towing depths below 100 m and towing speeds ranging from 2.4 m/s to 6.2 m/s (4 kn to 12 kn). Focus is given in the experiments to the relation between (outer) wall pressure fluctuations and the (inner) hydroacoustic near-field on the reverse side of a flat plate. The plate configuration consists of a sandwich structure with an (thick) outer polyurethane layer supported by an inner thin layer from fibre-reinforced plastics. Parameters of the turbulent boundary layer are estimated in order to analyse scaling relations of wall-pressure fluctuations, interior hydroacoustic noise, and the reduction of pressure fluctuations through the plate.

• 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 the Korea Society for Simulation
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• v.25 no.3
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• pp.15-28
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• 2016

It is necessary to analyze underwater acoustics channel(UAC) modeling and simulation for underwater weapon system development and acquisition. In order to analyze UAC, there are underwater acoustics propagation numerical analysis models(Ray theory, Parabolic equation, Normal-mode, Wavenumber integration). However, If these models are used for multiple frequency signal analysis, they are inaccurate to calculate result of analysis effectiveness and restricted for signal processing and analysis. In this paper, to overcome this problem, we propose simple/multiple frequency signal analysis model of the Pseudospectral Time-Domain Method synthetic environment UAC applied to underwater environment noise model as like as realistic underwater environment. In order to confirm the validation of the model, we performed the 9 scenarios simulation(4 scenarios of single frequency signal, 4 scenarios of multiple frequency signal, 1 scenario of single/multiple frequency signal like submarine radiated noise) for validation and confirmed the validation of this model through the simulation model.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.6
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• pp.569-574
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• 2011

In research vessels or naval ships, airborne noise from machineries such as diesel engine is the major source of underwater noise at low speed. In this paper, effect of engine noise on underwater noise is studied by considering two paths; sound radiation from hull plate and direct airborne noise transmission through hull plate. SEA (Statistical energy analysis) is used to predict hull plate vibration induced by engine noise, where SEA model consists of only two subsystems; engine room air space and hull plate. The pressure level in water is calculated from sound radiation by plate. Engine noise transmission through hull plate is obtained by assuming plane wave propagation in air-limp plate-water system. Two effects are combined and compared to the measurement, where speaker is used as a source in engine room and sound pressure levels in engine room and water are measured. The hydrophone is located 1 m away from the hull plate. It is found below 1000 Hz, prediction overestimates underwater sound pressure level by 5 to 12 dB.

• 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.

• Journal of the Society of Naval Architects of Korea
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• v.41 no.2
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• pp.21-32
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• 2004

The non-cavitating noise of underwater propeller is considered numerically in this study. The main purpose is to analyze non-cavitating noise from underwater propellers in various operating conditions with different configurations. Noise is predicted by using time-domain acoustic analogy, boundary element method, and computational hydro-acoustics. The flow field is analyzed with potential-based panel method, and then time-dependant pressure data are used as the input for Focus Williams-Hawkings formulation to predict far field acoustics. Furthermore, boundary element method and computational hydro-acoustics are also considered to investigate duct propeller and ducted multi-stage propeller to consider the reflection and diffraction of sound waves. With this methodology, noise intensity and directivity of each noise sources could be well analyzed.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.1
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• pp.24-33
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• 2004

Recently, ship acoustics takes more important role in acoustic stealth and classification of underwater targets. In this paper, a theoretical concept is proposed for M＆S-based test and evaluation of ship acoustics. The concept is based on two different approaches: on Top-Down method which emphasizes the survivability of the ship and on Bottom-Up method which considers acoustic characteristics of the ship-equipments. In order to improve the effectiveness of M＆S-based T＆E for ship acoustics, it is recommended in this paper to compromise the two approaches as adequate.