• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1040-1043
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• 2001

This paper describes the principle behind Crank Angle Analysis, as implemented by Bruel ＆ Kjaer in the Non-Stationary Spatial Transformation of Sound Fields (NS-STSF) system. The NS-STSF system combines a Time Domain Holography measurement on for example an engine with two simultaneously recorded Tacho signals. The Tacho signals provide the crankshaft angle and the RPM at the instant of each instantaneous output (snap-shot) from Time Domain Holography. As a result, the system allows precise analysis of the temporal and spatial relation between the acoustical emission (or the vibration pattern) and the mechanical events during an engine cycle. Some results from a measurement on a DaimlerChrysler engine are presented.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.223-226
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• 2005

In order to acquire 3 components data which has the good signal to noise ratio with only one shot, 3-C geophones were used, As a result, the vertical component showed the distinct first arrival of P-wave, and the horizontal component was improved the signal to noise ratio of S-wave, while was attenuated P-wave. The 2-D Poisson's ratio section was computed from P- and S-wave cell velocities included velocity tomograms of the P- and S-waves. The Poisson's ratio values were computed in the range of $0.2{\~}0.3$. With one shot, we can obtain 2-D distribution of dynamic Poisson's ratio as well as velocity tomograms of P- and S-waves.

• Korean Journal of Optics and Photonics
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• v.3 no.3
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• pp.161-166
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• 1992

The s-polarized laser beam is incident with an angle ~$-30^{\circ}$ to a uniformly rotating rough metal surface and the degree of second order temporal coherence $g_{s}^{(2)}(\tau)$ of the backscattered wave, which has the same polarization with the incident laser beam, is measured. The contribution of shot noise involved in the measurement of $g_{s}^{(2)}(0)$ is subtracted from the photoelectric signal to obtain the accurate value of $g_{s}^{(2)}(0)$.At each scattering angle$\theta_{s}$에서$g_{s}^{(2)}(\tau)$ is almost consistent with the function {1+exp($-\tau^2/\tau_0^2$)}, which is the same result with the case of the laser speckle formed by scattering on the rotating ground glass suface. In addition, a peak in the angular distribution of $\tau_0$ is observed with the maximum at$\theta_s=34^{\circ}$.It is found that the rough metallic scattering with multiple scattering over than 10% has the same function of the degree of second order temporal coherence with that of the ground glass surface scattering where the multiple scattering is ignorably small.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.844-854
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• 2021

Semi-supervised learning (SSL) and few-shot learning (FSL) have shown impressive performance even then the volume of labeled data is very limited. However, SSL and FSL can encounter a significant performance degradation if the diversity gap between the labeled and unlabeled data is high. To reduce this diversity gap, we propose a novel scheme that relies on an autoencoder for generating pseudo examples. Specifically, the autoencoder is trained on a specific class using the available labeled data and the decoder of the trained autoencoder is then used to generate N samples of that specific class based on N random noise, sampled from a standard normal distribution. The above process is repeated for all the classes. Consequently, the generated data reduces the diversity gap and enhances the model performance. Extensive experiments on MNIST and FashionMNIST datasets for SSL and FSL verify the effectiveness of the proposed approach in terms of classification accuracy and robustness against adversarial attacks.

• Corrosion Science and Technology
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• v.12 no.2
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• pp.93-101
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• 2013

The object of this work is to establish an electrochemical noise(EN) measurement technique combined with a direct current potential drop(DCPD) method for monitoring of localized corrosion cracking of nickel-based alloy, and to analyze its mechanism. The electrochemical current and potential noises were measured under various conditions of applied stress to a compact tension specimen in a simulated primary water chemistry of a pressurized water reactor. The amplitude and frequency of the EN signals were evaluated in both time and frequency domains based on a shot noise theory, and then quantitatively analyzed using statistical Weibull distribution function. From the spectral analysis, the effect of the current application in DCPD was found to be effectively excluded from the EN signals generated from the localized corrosion cracking. With the aid of a microstructural analysis, the relationship between EN signals and the localized corrosion cracking mechanism was investigated by comparing the shape parameter of Weibull distribution of a mean time-to-failure.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.37.3-38
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• 2021

Radiation pressure noise of photon and photon shot noise are quantum noise limitation in interferometric gravita-tional wave detectors. Since relationship between the two noises is position and momentum of the Heisenberg uncertainty principle, quantum non-demolition (QND) technique is required to reduce the two noises at the same time. Frequency dependent squeezing using a filter cavity is one of realistic solutions for QND measurement and experimental results show that its cutting-edge performance is sufficient to apply to the current gravitational wave detectors. A 300m filter cavity is under construction at adv-LIGO. KAGRA (gravitational wave detector in Japan) has also started international collaboration to build a filter cavity. Recently we joined the filter cavity project for KAGRA. Current status of squeezing and filter cavity research at KASI and details of the KAGRA filter cavity project will be presented.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.93-98
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• 2023

As semiconductor devices become highly integrated and process difficulty increases, the need for highly sensitive sensors that can detect micro leaks is increasing. However, the noise contained in the CCD sensor itself acts as an obstacle to detecting fine leaks. In this study, integration time was changed for each condition, the sensor was cooled to 0℃, and the dark voltage level was measured to confirm through experiment the characteristics of the temporal noise included in the CCD sensor, a component of OES (Optical Emission Spectroscopy). When integration time was reduced from 30msec to 10msec, the dark voltage level decreased by about 20.5 % from an average of 151.5mV to 120.5mV. In the case of cooling device, Peltier elements were selected because of their simple structure and small size. During temperature cooling, the target temperature was controlled to within ±0.5℃ through PID control. When cooled from 20℃ to 0℃ using this cooling device, it was confirmed that the dark voltage level decreased by about 7% from an average of 147.0mV to 137.0mV.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.54-65
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• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.