• Journal of the Institute of Convergence Signal Processing
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• v.19 no.3
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• pp.139-146
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• 2018

Underwater acoustic communication can be applied to various area such as scientific, commercial and military survey using Autonomous Underwater Vehicles and Unmanned Underwater Vehicles. Underwater communication is studying very actively by advanced country like United States. But differ from wireless communication in the air, underwater acoustic communication has some difficult problems, ISI(Inter Symbol Interference) due to multipath and limit of transmission bandwidth due to slow propagation of sound wave. In this paper, SC-FDE and OFDM transmission system for the cancellation of ISI in conjunction with underwater acoustic channel modeling are applied to the underwater simulation of communication. The performance of these methods in the simulation guide to possibility of adopting in underwater acoustic communication algorithm. For this purpose, we compare SER performance of SC-FDE with that of OFDM for modelled underwater channel. Underwater channel is generated by Bellhop model. Simulation results show above 5dB SNR gain at 10-3 SER. And it demonstrate SC-FDE is efficient method for underwater acoustic communication.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.472-481
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• 2003

Low-frequency hi-static reverberation model (LHYREV-B, Low-frequency Hanyang univ. Reverberation model-Bistatic) based on the parabolic approximation for shallow water environment is presented. In this paper bistatic reverberation level is computed using the angle-independent scattering strength function and the wave-based acoustic model. The signal simulated by the LHYREV-B model is compared with the observed signals and it is shown that the LHYREV-B model provides a closer fit to the observed signals.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• The Journal of the Acoustical Society of Korea
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• v.10 no.2
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• pp.52-60
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• 1991

Sound propagation in a sloping sea bed ocean environment demonstrates ray curvature in a direction parallel to the shoreline. The theoretical analysis of this shows that an ensonified region and a shadow region are formed, and their spatial extents depend on the spatial coordinates of source and receiver, a sloping angle and sourece frequency. The purpose of this experimental study using a sloping sea bed model is to check the theoretical prediction as a part of an ongoing investigation in the ocean environment. The sloping sea bed model used in this experiment had an ideal pressure-release boundaries and a sloping angle of $220.5{\circ}$ A single frequency signal and an impulsive signal were used as omnidirectional point sources. The spatial acoustic field characteristics in the across slope direction were measured using the former and the frequency dependent field characteristics in a specific point were obtained using the latter. It has been found that the analysis for the spatial extent of shadow zone and the frequency dependent field characteristics in the across slope direction, has a good agreement with the theoretical solution.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.543-548
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• 2014

Multiple-model based speech recognition framework (MMSR) has been known to be very successful in speech recognition. Since it uses multiple hidden Markov modes (HMMs) that corresponds to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can have a close match with the test noisy speech. However, since the number of HMM sets is limited in practical use, the acoustic mismatch still remains as a problem. In this study, we experimentally determined the optimal SNR mapping between the test noisy speech and the HMM set to mitigate the mismatch between them. Improved performance was obtained by employing the SNR mapping instead of using the estimated SNR from the test noisy speech. When we applied the proposed method to the MMSR, the experimental results on the Aurora 2 database show that the relative word error rate reduction of 6.3% and 9.4% was achieved compared to a conventional MMSR and multi-condition training (MTR), respectively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.78-83
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• 2003

General algorithm is developed for the prediction of internal flow-induced noise. This algorithm is based on the integral formula derived by using the General Green Function, Lighthills acoustic analogy and Curls extension of Lighthills. Novel approach of this algorithm is that the integral formula is so arranged as to predict frequency-domain acoustic signal at any location in a duct by using unsteady flow data in space and time, which can be provided by the Computational Fluid Dynamics Techniques. This semi-analytic model is applied to the prediction of internal aerodynamic noise from a throttle valve in an automotive engine. The predicted noise levels from the throttle valve are compared with actual measurements. This illustrative computation shows that the current method permits generalized predictions of flow noise generated by bluff bodies and turbulence in flow ducts.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05c
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• pp.79-83
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• 2001

This is an investigation on the propagation characteristics of AE signal in GIS. The selection of measuring position and resonant frequency of AE sensor is one of the most important factor to optimize a diagnostic system. And natural frequency and harmonic response characteristics are indispensable to optimize those factors. So, in this investigation, we make a 3D model of 362kV GIS and make a modal and harmonic analysis to survey the vibro-acoustic property. Through the result of the analysis, we can make a further understanding on the vibro-acoustic characteristics of GIS.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.601-607
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• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2137-2142
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• 2012

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has a difficult in collecting actual underwater data. In this paper, we implemented the simulator to synthesize the active target signal, to extract feature and to classify the target in the underwater environment. In target signal synthesis, highlight and three-dimensional model are used and multi-aspect based hidden markov model is used for target classification.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.4
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• pp.310-318
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• 2004

For many industrial problems originating from aerodynamic noise, noise prediction techniques, reliable and easy to apply, would be of great value to engineers and manufacturers. General algorithm is presented for the prediction of internal flow-induced noise from quick opening throttle valve in an automotive engine. This algorithm is based on the integral formula derived by using the General Green Function, Lighthill's acoustic analogy and Curle's extension of Lighthill's. Novel approach of this algorithm is that the integral formula is so arranged as to predict frequency-domain acoustic signal at any location in a duct by using unsteady flow data in space and time, which can be provided by the Computational Fluid Dynamics Techniques. This semi-analytic model is applied to the prediction of internal aerodynamic noise from a throttle valve in an automotive engine. The predicted noise levels from the throttle valve show good agreement with actual measurements. The results show that the dipole noise is dominant in this phenomena and the origin of noise sources is attributed to the anti-vortex lines formed in the down-stream from a throttle valve. This illustrative computation shows that the current method permits generalized predictions of flow noise generated by bluff bodies and turbulence in flow ducts.