• 대한공업교육학회지
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• v.33 no.2
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• pp.273-286
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• 2008

Underwater acoustic sensor array can detect acoustic signal in underwater and the sensor array can be mounted in each left, right or front side of the UUV(Unmanned Underwater Vehicle). The sensor array could be conformal array and effected turbulent boundary layer flow noise. Therefore, in this paper numerical simulations were performed to know the how the outer flow noise affect the hydrophone which embedded in the elastomer. Corcos wall pressure model was used as turbulent boundary layer flow noise and this model was applied to the frequency density function. Characteristics of transfer function according the kx wave number were simulated and design parameters were thickness of elastomer, density, and modulus of elasticity. Based on the simulation results when increasing the thickness of elastomer noise reduction was increased. This results can be applied to the design of conformal array of UUV.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.419-425
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• 2022

This paper aims to evaluate the acoustic detection performance for the deployment of the source and receiver positions of a bi-static sonar. In contrast with a mono-static sonar, a bi-static sonar has a large amount of computation and complexity for deployment. Therefore, in this study, we propose an assessment method that reduces the amount of computation while considering the variability of the ocean environment as a method to apply to the placement of the source and receiver of a bi-static sonar. First, we assume the representative ocean environment in the shallow and deep water. The signal excess is calculated with the source to receiver ranges and sensor depths. And the result is compared with the proposed assessment method of acoustic detection performance.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.10-18
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• 2009

A moving surface vessel generates a ship wake which contains a cloud of micro-bubbles with radii ranging between $8{\sim}200{\mu}m$. Such micro-bubbles can be detected by active sonar system for more than ten minutes depending on the size and speed of the surface vessel. In this paper, a reverberation model for the ship wake is presented. The developed model consists of the acoustic scattering model due to the distribution of the micro-bubbles and the kinematic model for the moving active sonar. The acoustic scattering model is based on the volume integration, where the volume scattering strengths are obtained from the spatial distribution of micro-bubbles. Since the directivity and look-direction of active sonar are important factors for moving active sonar, the kinematic model utilizes the Euler transformation to obtain the relative motion between the global and local coordinates. In order to verify the developed model, a series of sea experiment was executed in September 2007 to obtain the spatial-temporal distribution of a bubble cloud, and analyzed to be compared with the simulation results.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.74-82
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• 2016

In this paper, a new automatic speech recognition (ASR) was proposed where ultrasonic doppler signals were used, instead of conventional speech signals. The proposed method has the advantages over the conventional speech/non-speech-based ASR including robustness against acoustic noises and user comfortability associated with usage of the non-contact sensor. In the method proposed herein, 40 kHz ultrasonic signal was radiated toward to the mouth and the reflected ultrasonic signals were then received. Frequency shift caused by the doppler effects was used to implement ASR. The proposed method employed multi-channel ultrasonic signals acquired from the various locations, which is different from the previous method where single channel ultrasonic signal was employed. The PCA(Principal Component Analysis) coefficients were used as the features of ASR in which hidden markov model (HMM) with left-right model was adopted. To verify the feasibility of the proposed ASR, the speech recognition experiment was carried out the 60 Korean isolated words obtained from the six speakers. Moreover, the experiment results showed that the overall word recognition rates were comparable with the conventional speech-based ASR methods and the performance of the proposed method was superior to the conventional signal channel ASR method. Especially, the average recognition rate of 90 % was maintained under the noise environments.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.133-141
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• 2017

The hybrid acoustic sensor for implantable hearing aid has the structure in which a sound pressure based acoustic sensor (ECM) and a vibration based acceleration sensor are combined. This sensor combines the low frequency sensitivity of an acoustic sensor with the high frequency sensitivity of an acceleration sensor, allowing the acquisition of a wide range of sound from low to high frequency. In this paper, an acceleration sensor for use in a hybrid acoustic sensor has been proposed. The acceleration sensor captures the vibration of the tympanic membrane generated by the acoustic signal. The size of the proposed acceleration sensor was determined to diameter of 3.2 mm considering the anatomical structure of the tympanic membrane and the standard of ECM. In order to make the hybrid acoustic sensor have high sensitivity and wide bandwidth characteristics, the aim of the resonance frequency of the acceleration sensor is to be generated at about 3.5 kHz. The membrane of the acceleration sensor derives geometric structure through mathematical model and finite element analysis. Based on the analysis results, the membrane was implemented through a chemical etching process. In order to verify the frequency characteristics of the implemented membrane, vibration measurement experiment using external force was performed. The experiment results showed mechanical resonance of the membrane occurred at 3.4 kHz. Therefore, it is considered that the proposed acceleration sensor can be utilized for a hybrid acoustic sensor.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.455-471
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• 2013

Active sonar has been commonly used to detect targets existing in the shallow water. When a signal is transmitted and returned back from a target, it has been distorted by various properties of acoustic channel such as multipath arrivals, scattering from rough sea surface and ocean bottom, and refraction by sound speed structure, which makes target detection difficult. It is therefore necessary to consider these channel properties in the target signal simulation in operational performance system of active sonar. In this paper, a monostatic active sonar system is considered, and the target echo, reverberation, and ambient noise are individually simulated as a function of time, and finally summed to simulate a total received signal. A 3-dimensional highlight model, which can reflect the target features including the shape, position, and azimuthal and elevation angles, has been applied to each multipath pair between source and target to simulate the target echo signal. The results are finally compared to those obtained by the algorithm in which only direct path is considered in target signal simulation.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.554-561
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• 2003

Results of the numerical simulation and experimental data analysis for identification of mode cutoff frequency and estimation of sound speed of seabed from the spectrum of acoustic signal received at fixed source-receiver range are presented. Model simulations for Pekeris waveguide show that the frequency-dependent propagation loss and interference pattern are closely related to mode cutoff frequencies and it could be possible to the identify them from the changes of interference pattern. The concept considered at numerical simulations is applied to signals acquired at sea test. Cutoff frequency and sound speed of seabed are estimated from the interference pattern of measured signal. Propagation loss predicted using the estimated sound speed of seabed as model input parameter shows similar estimation result compared to propagation loss derived from measured data.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.500-510
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• 2023

Passive sonar signals mainly contain both normal and abnormal signals. The abnormal signals mixed with normal signals are primarily detected using an AutoEncoder (AE) that learns only normal signals. However, existing AEs may perform inaccurate detection by reconstructing distorted normal signals from mixed signal. To address these limitations, we propose an abnormal signal detection model based on a Recurrent Neural Network (RNN) and vector quantization. The proposed model generates a codebook representing the learned latent vectors and detects abnormal signals more accurately through the proposed search process of code vectors. In experiments using publicly available underwater acoustic data, the AE and Variational AutoEncoder (VAE) using the proposed method showed at least a 2.4 % improvement in the detection performance and at least a 9.2 % improvement in the extraction performance for abnormal signals than the existing models.

• Journal of the Korean Magnetics Society
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• v.27 no.2
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• pp.63-69
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• 2017

Naval ships are particularly required to maintain acoustic and magnetic silence due to their operational characteristics. Among them, underwater magnetic field signals derived by ships are likely to be detected by threats such as surveillance systems and mine systems at close distance. In order to increase the survivability of the vessels, various techniques for reducing the magnetic field signal are being studied and it is necessary to consider not only the magnitude of the magnetic field signal but also the gradient of it. In this paper, we use the commercial electromagnetic finite element analysis tool to predict the induced magnetic field signal of ship's scaled model, and arrange the degaussing coil. And the optimum degaussing current of the coil was derived by applying the particle swarm optimization algorithm considering the gradient constraint. The validity of the optimal degaussing technique is verified analytically by comparing the magnetic field signals after the degaussing with or without gradient constraint.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.131-137
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• 1994

Detection of grinding trouble occuring during the grinding process is classified into two types, i.e, based on the quantitative and qualitative knowledge. But, since the grinding operation is especially related with a large amount of functional parameters, it is actually defficult to cope with the grinding troubles occuring during process. Therefore, grinding trouble-shooting has difficulty in satisfying the requirement from the user. To cope with the grinding troubles occuring during the process, the application of neural network is on effective way. In this study, we identify the four parameters derived from the AE(Acoustic Emission) signals and present the grinding trouble-shooting system utilizing a back-propagation model of the neural network.