• Journal of KSNVE
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• v.11 no.2
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• pp.226-233
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• 2001

It is essential to analyze structural vibrations due to turbulent wall pressure fluctuations over a body surface which moves through a fluid, because the vibrations can be a severe source of noise affecting to passengers in airplanes and SONAR performance. Generally, this kind of problems have been solved for very simplified models, e.g. plates, which can be applied to the wavenumber domain analysis. In this paper, a finite element modeling of the walt pressure fluctuations is investigated, which can be applied to those over arbitrary smooth surfaces. It is found that the modeled wall pressure fluctuation at nodes becomes uncorrelated at higher frequencies and at lower flow speeds, and the response is over-estimated due to the aliased power. Then the frequency range available for uncorrelated loading model and two power correction schemes are presented.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.817-824
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• 2000

It is essential to analyze structural vibrations due to turbulent wall pressure fluctuations over a body surface which moves through a fluid, because the vibrations can be a severe source of noise affecting to passengers in airplanes and SONAR performance. Generally, this kind of problems have been solved for very simplified models, e.g. plates, which can be applied to the wavenumber domain analysis. In this paper, a finite element modeling of the wall pressure fluctuations over arbitrary smooth surfaces is investigated. It is found that the modeled wall pressure fluctuation at nodes becomes uncorrelated at higher frequencies and at lower flow speeds, and the response is over-estimated due to the aliased power. Finally, the frequency range available for uncorrelated loading model and two power correction schemes are presented.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.658-665
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• 2013

While the chirp signal is extensively used in radar and sonar systems for target decision in wireless communication systems, it has not been widely used for positioning in indoor environments. Recently, the IEEE 802.15.4a standard has adopted the chirp spread spectrum (CSS) as an underlying technique for low-power and low-complexity precise localization. Chirp signal based ranging solutions have been established and deployed but their ranging performance has not been analyzed in multipath environments. This paper presents a ranging performance analysis of a chirp signal and suggests a method to suppress multipath error by using a type of non-linear chirp signal. Multipath ranging performance is evaluated using a conventional linear chirp signal and the proposed non-linear chirp signal. We verify the feasibility of both methods using two-ray multipath model simulation. Our results demonstrate that the proposed non-linear chirp signal can successfully suppress the multipath error.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.13-24
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• 2024

In this paper, we analyzed the performance of normalization according to various neural network models and input-output structures. For the analysis, a simulation-based dataset for noise environments with homogeneous and up to three interfering signals was used. As a result, the end-to-end structure that directly outputs noise variance showed superior performance when using a 1-D convolutional neural network and BiLSTM model, and was analyzed to be particularly robust against interference signals. This is because the 1-D convolutional neural network and bidirectional long short-term memory models have stronger inductive bias than the multilayer perceptron and transformer models. The analysis of this paper are expected to be used as a useful reference for future research on deep learning-based normalization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.579-585
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• 2019

Increasing the detection probability of underwater targets necessitates securing the towing stability of the active towed SONAR. In this paper, to confirm the effects of tail wing fin on towing attitude and towing stability, two scale model experiments and one sea trials were conducted and the results were analyzed. The scale model tests measured the towing behavior of each of the tail fin shapes according to towing speed in a towing tank. The shape of the tail fin used in the scale model test was tested with an I-type tail fine and four Y-type tail fins, totaling five tail fins of the two kinds. The first scale model test confirmed that the Y-type tail fin was superior to the I-type tail fin in towing attitude and towing stability. The second scale model test confirmed the characteristics of the vertical tail fin height increase and the lower horizontal tail fin inclination angle application shape based on the Y-type tail fin. The shape of the application of the lower horizontal tail fin inclination angle showed the best performance. In order to verify the results of the scale model test, a full size model was constructed, sea trials were performed, and the towing attitude was measured. The results were similar to those of the scale model test.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.387-395
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• 2019

TMA (Target Motion Analysis) in passive sonar is generally conducted with the bearing only or the bearing frequency. In order to conduct TMA fast and accurately, it is essential to estimate a initial target maneuver precisely. The accuracy of TMA can be improved by using SNR (Signal to Noise Ratio) information and acoustic propagation model additionally. This method assumes that the radiated noise level of the target is known, but the accuracy of TMA can be degraded due to a mismatch between the assumed radiated noise level and the actual radiated noise level. In this paper, TMA with the acoustic propagation model, bearing measurements, and SNR information is conducted in the ocean environment of South Korea (East Sea/ Yellow Sea/ South Sea). And the performance analysis of TMA for the mismatch in the radiated noise is presented.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.46-53
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• 2005

Agency for Defense Development (ADD) developed the Submarine Acoustic Information Management System (SAIMS Version 1.0) capable of interfacing some submarine sensors in operation and predicting detection environments for sonars. The major design concepts are as follows: 1) A proper acoustic model is examined and optimized to cover wide spectra of frequency ranges for both active and passive sonars. 2) Interfacing the submarine sensors to an electric navigation chart, the system attempts to maximize the applicability of the information produced. 3) The state-of-the-art database in large area is built and managed on the system. 4) An algorithm, which is able to estimate a full sound speed profile from the limited oceanographic data, is developed and employed on the system. This paper briefly describes design concepts and algorithms embedded in the SAIMS. The applicability of the SAIMS was verified through three sea experiments in October 2003-February 2004.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.89-95
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• 2019

This paper introduces the simulation concepts and technical approach of underwater weapon system performance analysis simulator, especially focused on probabilistic target detection concepts. We calculated the signal excess (SE) value using SONAR equation, then derived the probability density function(PDF) for target presence($H_1$) or absence($H_0$) cases, respectively. With the Neyman-Pearson detector criterion, we got the probability of detection($P_D$) while satisfying the given probability of false alarm($P_{FA}$). At every instance of simulation, target detection is decided in the probabilistic perspective. With the proposed detection implementation, we improved the model fidelity so that it could support the tactical decision during the operation.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.111-120
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• 2017

In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.