• Korean Journal of Optics and Photonics
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• v.17 no.5
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• pp.383-390
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• 2006

The SharkHartmann wavefront sensors are the most popular devices to measure wavefront in the field of adaptive optics. The Shack-Hartmann sensors measure the centroids of spot irradiance distribution formed by each corresponding micro-lens. The centroids are linearly proportional to the local mean slopes of the wavefront defined within the corresponding sub-aperture. The wavefront is then reconstructed from the evaluated local mean slopes. The uncertainty of the Shack-Hartmann sensor is caused by various factors including the detector noise, the limited size of the detector, the magnitude and profile of spot irradiance distribution, etc. This paper investigates the noise propagation in two major centroid evaluation algorithms through computer simulation; 1st order moments of the irradiance algorithms i.e. center of gravity algorithm, and correlation algorithm. First, the center of gravity algorithm is shown to have relatively large dependence on the magnitudes of noises and the shape & size of irradiance sidelobes, whose effects are also shown to be minimized by optimal thresholding. Second, the correlation algorithm is shown to be robust over those effects, while its measurement accuracy is vulnerable to the size variation of the reference spot. The investigation is finally confirmed by experimental measurements of defocus wavefront aberrations using a Shack-Hartmann sensor using those two algorithms.

• Journal of the Korean Geophysical Society
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• v.5 no.1
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• pp.9-18
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• 2002

We performed a long-term monitoring of self-potential(SP) using the Cu-CuSO₄non-polarizable electrode and copper-clad electrodes(CCE) in a test site in order to analyze the effects of surrounding environmental noises such as temperature, rainfall and soil moisture content on the electrodes. Analysis of the temperature dependence of the non-polarizable electrodes showed that is temperature coefficient was about +0.5 mV/°Fwhen its end was exposed to atmosphere while it was less than +0.5 mV/℃ when submerged into the subsurface, which reflects that there exists an 8 to 11 hour lag between temperatures at the depth of 15 cm and atmosphere. CCE was independent of atmospheric temperature in subsurface but showed temperature coefficient of 1.0 mV/℃ when exposed to atmosphere. Drifts of 1 to 2 mV recorded with the non-polarizable electrode directly related to the soil moisture content when it was buried in subsurface. Drift with CCE also showed similar trend to the soil moisture content, and 5 mV drift was recorded according to 5% of daily variation. The soil moisture content had strong effects on the measurement with CCE in rainfall since the flow potential is generated on the surface of the electrode.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.479-487
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• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1709-1718
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• 2021

The waveform data of the Airborne Bathymetric LiDAR (ABL; LiDAR: Light Detection And Ranging) system provides data with improved accuracy, resolution, and reliability compared to the discrete-return data, and increases the user's control over data processing. Furthermore, we are able to extract additional information about the return signal. Waveform decomposition is a technique that separates each echo from the received waveform with a mixture of water surface and seabed reflections, waterbody backscattering, and various noises. In this study, a new waveform decomposition technique based on a Gaussian model was developed to improve the point extraction performance from the ABL waveform data. In the existing waveform decomposition techniques, the number of decomposed echoes and decomposition performance depend on the peak detection results because they use waveform peaks as initial values. However, in the study, we improved the approximation accuracy of the decomposition model by adding the estimated potential peak candidates to the initial peaks. As a result of an experiment using waveform data obtained from the East Coast from the Seahawk system, the precision of the decomposition model was improved by about 37% based on evaluating RMSE compared to the Gaussian decomposition method.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1179-1192
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• 2018

In order to detect the Antarctic Polar Front (PF) among the main fronts in the Southern Ocean, this study is based on the combinations of satellite-based sea surface temperature (SST) and height (SSH) observations. For accurate PF detection, we classified the signals as front or non-front grids based on the Bayesian decision theory from daily SST and SSH datasets, and then spatio-temporal synthesis has been performed to remove primary noises and to supplement geographical connectivity of the front grids. In addition, sea ice and coastal masking were employed in order to remove the noise that still remains even after performing the processes and morphology operations. Finally, we selected only the southernmost grids, which can be considered as fronts and determined as the monthly PF by a linear smoothing spline optimization method. The mean positions of PF in this study are very similar to those of the PFs reported by the previous studies, and it is likely to be well represents PF formation along the bottom topography known as one of the major influences of the PF maintenance. The seasonal variation in the positions of PF is high in the Ross Sea sector (${\sim}180^{\circ}W$), and Australia sector ($120^{\circ}E-140^{\circ}E$), and these variations are quite similar to the previous studies. Therefore, it is expected that the detection approach for the PF position applied in this study and the final composite have a value that can be used in related research to be carried out on the long term time-scale.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.1049-1055
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• 2018

This paper proposes the Signal Quality Indicator (SQI) to certify the wired communication channels connected between sub-systems embedded in guided flight systems. The communication signals can be distorted due to the poor interconnections of wired channels, the interference from the signals passing through the adjacent routed channels, and additive white Gaussian noises. As the ways to find out the condition of the communication channels, we present the Hamming distance based SQI (H-SQI) and the Euclidean distance based SQI (E-SQI). Two SQIs are compared in terms of the SQI resolution performance and the required number of hardware resources for implementations. The E-SQI requires the 10 times FPGA resources and an additional analog-digital converter over the H-SQI in spite of its outstanding SQI resolution performance. Moreover, the H-SQI could have the enough SQI resolution performance to find out the channel condition by increasing the oversampling rate, so the H-SQI is the more adequate than the E-SQI for the SQI of the guided flight systems.

• Geophysics and Geophysical Exploration
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• v.22 no.3
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• pp.107-115
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• 2019

Recently, since earthquakes have happened frequently in Gyeongju and Pohang areas in Korea, the earthquake detection research gets lots of attention. Geophysical monitoring data have been changed during the earthquake activity because the huge amount of energy is accumulated. The change of telluric current can be predicted by both of piezoelectric and electrokinetic effects before or during the earthquake occurrence, and if the change value exceeds the conventional telluric current noise, we can measure changes in the electric field associated with earthquakes. In this study, we have self-developed and verified the system that can monitor the telluric current. In order to verify our telluric current monitoring system, we installed lines of 40 m (E-W direction) and 28 m (N-S direction) on the site in Pohang. The telluric currents were sampled at 1 kHz for about a month. We have compared and analyzed the data of earthquake signals and electrical noises based on the earthquakes that occurred during the monitoring period. We have monitored if there were significant signals related to the earthquake on measured time series data. Through this study, we will suggest the direction of continuous research in the future.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.352-362
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• 2019

The old residences and shops in the backside blocks are affected by the traffic noises from the main road. The noise of the backside roads is affected by the following factors such as the height of the roadside buildings, the distance between the road and the backside streets, distance among adjacent roadside buildings, and the difference of the adjacent building heights. The both noise levels on the road and the backside street were measured simultaneously in 15 urban blocks of a city which can be categorized into two types of roadside building plans ; 1) one single building along the street, 2) buildings arranged on one axis beside the road. As the results, there is no significant noise reduction due to the width of the buildings in general. However, in the cases of buildings arranged on one axis beside the road, it was found that the average noise reduction was 12 dB(A) on the basis of the building height of 4 m. Also, it was analysed that for each 4 m increase in the building height, noise reduction occurred by 2 dB(A) beyond building height of 4 m. In general, it was proved that the noise of the back streets is mainly affected by the lowest height of the roadside buildings. It was found that noise is increased by 1 dB(A) for each 4 m increase of the height difference between adjacent buildings. Also, It was revealed that for each 0.5 m increase in the distance between roadside building, noise reduction decreased by 1 dB(A).

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.309-316
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• 2019

In this paper, a low-jitter delay-locked loop that compensates for local clock skew is presented. The proposed DLL consists of a phase splitter, a phase detector(PD), a charge pump, a bias generator, a voltage-controlled delay line(VCDL), and a level converter. The VCDL uses self-biased delay cells using current mode logic(CML) to have insensitive characteristics to temperature and supply noises. The phase splitter generates two reference clocks which are used as the differential inputs of the VCDL. The PD uses the only single clock from the phase splitter because the PD in the proposed circuit uses CMOS logic that consumes less power compared to CML. Therefore, the output of the VCDL is also converted to the rail-to-rail signal by the level converter for the PD as well as the local clock distribution circuit. The proposed circuit has been designed with a $0.13-{\mu}m$ CMOS process. A global CLK with a frequency of 1-GHz is externally applied to the circuit. As a result, after about 19 cycles, the proposed DLL is locked at a point that the control voltage is 597.83mV with the jitter of 1.05ps.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.