• Current Optics and Photonics
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• v.7 no.1
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• pp.90-96
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• 2023

We numerically solve the nonlinear Schrödinger equation for pulse propagation in a passively mode-locked Yb:KGW laser. The soliton-like pulse formation as a result of balanced negative group-delay dispersion (GDD) and nonlinear self-phase modulation is analyzed. The cavity design and optical parameters of a previously reported high-power Yb:KGW laser were adopted to compare the simulation results with experimental results. The pulse duration and energy obtained by varying the small-signal gain or GDD reproduce the overall tendency observed in the experiments, demonstrating the reliability and accuracy of the model simulation and the optical parameters.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.331-339
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• 2016

This paper suggests an estimation method of the source signal and the channel impulse response (CIR) using ray-based blind deconvolution (RBD) in the underwater acoustic channel environment where Doppler effect exists by the relative motion between source and receiver. It is difficult to estimate the CIR on Doppler effect by the matched filter with a highly Doppler-sensitive waveform such as the m-sequence signal because Doppler shift can severely degrade the correlation between the received signal corrupted by Doppler effect and the original source signal. In this study, the Doppler-shifted source-signal's phase is estimated using the RBD, and the received signal is compensated by it to obtain the Doppler-corrected CIR. It is verified that using the matched filter with the received signal from the experimental data fails to estimate the CIR while the obtained CIR by the suggested method has the similarity to the propagation path of the ray model. Also, the results show that the reconstructed source signal using the RBD has the better Doppler shift compensation than the Doppler-shifted source signal derived from scattering function.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.33-49
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• 2016

Structural Health Monitoring (SHM) has been attracting numerous research efforts around the world because it targets at monitoring structural conditions and performance to prevent catastrophic failure, and to provide quantitative data for engineers and infrastructure owners to design a reliable and economical asset management strategy. In the past decade, with supports from Australian Research Council (ARC), Cooperative Research Center for Infrastructure and Engineering Asset Management (CIEAM), CSIRO and industry partners, intensive research works have been conducted in the School of Civil, Environmental and Mining Engineering, University of Western Australia and Centre for Infrastructural Monitoring and Protection, Curtin University on various techniques of SHM. The researches include the development of hardware, software and various algorithms, such as various signal processing techniques for operational modal analysis, modal analysis toolbox, non-model based methods for assessing the shear connection in composite bridges and identifying the free spanning and supports conditions of pipelines, vibration based structural damage identification and model updating approaches considering uncertainty and noise effects, structural identification under moving loads, guided wave propagation technique for detecting debonding damage, and relative displacement sensors for SHM in composite and steel truss bridges. This paper aims at summarizing and reviewing the recent research advances on SHM of civil infrastructure in Western Australia.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.51-56
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• 2014

TETRA(TErrestrial Trunked RAdio) provide specialized disaster radio communication services as a standard European for digital TRS(Trunked Radio System). Especially, DMO(Direct Mode Operation) feature is used effectively in the radio shadow areas which base station does not propagate radio signal because it can communicate directly with terminal to terminal without base station's relay function. However, to effectively used DMO feature, radio coverage prediction information should be provided to users. Therefore in this paper, we were calculated link budget of TETRA DMO terminals which were distributing and operating in the country and then predicted reaching distance about radio propagation to be applied with path loss model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7B
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• pp.789-794
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• 2011

We analyze the capacity of CDMA base stations in the subway environment. We investigate the characteristics of multipath fading, cell structures, and propagation environment in the subway, analyze signal to noise ratio, sectorization gain, path-loss exponent, frequency reuse factor, and obtain the link capacity of a base station in the subway. We measure the peakedness factor and reveal that base stations in the subway have peaked traffic. We use Neal-Wilkinson model to obtain the Erlang capacity instead of Erlang-B model based on Poisson traffic.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.63-75
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• 2010

In this paper, the equalizer techniques that is able to adopt the digital on-channel repeater for 8VSB-based DTV system has been analyzed and we propose an effective equalizer structure which can reduce the error propagation phenomenon by the feedback signal and improve the receiver performance at the same time. In order to confirm the effective cancellation of the feedback signal, the multi-level Correlation LMS scheme is proposed through the analysis of conventional basic LMS based DFE and Correlation LMS algorithm and as compared with the conventional method, we can confirm the reduction of error propagation. When performing the computer simulation, as the Brazil channel model which is very popular for DTV broadcasting system is adopted, the result is drawn by comparing and analysing the equalizer algorithm. We have examine the symbol error rate which is in the range of 15~25dB of operation receipt SNR and MSE(Mean Square Error) in the DTV broadcasting system. As a result of comparing with the existing method, the signal-noise ratio which is necessary for maintain the bit error correction ability that the means of proposal is same is reduced by about 2~5dB, and in the rate of convergence through the MSE, we found the reduction of needed time.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.83-91
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• 2001

This paper proposes the Focused beamforming to estimate the location of target residing near to the observation platform in the underwater environment. The Focused beamforming technique provides the location of target by the coherent summation of a series of incident spherical waveforms considering distinct propagation delay times at the sensor array. But due to the movement of the observation platform and the variation of the underwater environment, the shape of the sensor array is no longer to be linear but it becomes distorted as the platform moves. Thus the Focused beamforming should be peformed regarding to the geometric shape variation at each time. To estimate the target location, the artificial image plane comprised of cells is constructed, and the delays are calculated from each cell where the target could be proximity to sensors for the coherent summation. After the coherent combining, the beam pattern can be obtained through the Focused beamforming on the image plane. Futhermore to compensate the variation of the shape of the sensor array, the paper utilizes the Nth-order polynomial approximation to estimate the shape of the sensor array obeying the water pulley modeling. Simulation results show the performance of the Focused beamforming for different frequency bands of the radiated signal.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.6
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• pp.464-476
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• 1998

Determination of location, shape and size of a flaw from its eddy current testing signal is one of the fundamental issues in eddy current nondestructive evaluation of steam generator tubes. Here, we propose an approach to this problem; an inversion of eddy current flaw signal using neural networks trained by finite element model-based synthetic signatures. Total 216 eddy current signals from four different types of axisymmetric flaws in tubes are generated by finite element models of which the accuracy is experimentally validated. From each simulated signature, total 24 eddy current features are extracted and among them 13 features are finally selected for flaw characterization. Based on these features, probabilistic neural networks discriminate flaws into four different types according to the location and the shape, and successively back propagation neural networks determine the size parameters of the discriminated flaw.