• The Journal of the Acoustical Society of Korea
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• v.27 no.3E
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• pp.69-76
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• 2008

Measurements of bubble size and distribution in the surface layer of the sea, wind speed, and variation of ocean environments were made continually over a four-day period in an experiment conducted in the South Sea of Korea during 17-20 September 2007. Theoretical background of bubble population model indicates that bubble population is a function of the depth, range and wind speed and bubble effects on sound speed shows that sound speed varies with frequency. Observational evidence exhibited that the middle size bubble population fit the model very well, however, smaller ones can not follow the model probably due to their short lifetime. Meanwhile, there is also a hysteresis effect of void fraction. Observational evidence also indicates that strong changes in sound speed are produced by the presence of swarms of micro bubbles especially from 7 kHz to 50 kHz, and calculation results are consistent with the measured data in the high frequency band, but inconsistent in the low frequency band. Based on the measurements of the sound speed and high frequency transmission configuration in the bubble layer, we present an estimation of underwater acoustic channel capacity in the bubble layer.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.557-557
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• 2000

Tracking of the moving object, which is realized by the computer vision, is used for military and industrial fields. It is the application technique with imply complicated processing for understanding the input images. But, in these days, the most moving object tracking algorithms have many difficult problems. A typical problem is the increase of calculation time depending on target number. For this reason, there are many studies to solve real time processing problems and errors for background environmental change. In this paper, we used optical flow which is one of moving object tracking algorithms. It represents vector of the moving object. Optical flow estimation based on the regularization method depends on iteration method but it is very sensitive the noise. We proposed a new method using the Combinatorial Hough Transform (CHT) and Voting Accumulation in order to find optimal constraint lines. Also, we used the logical operation in order to release the operation time. The proposed method can easily and accurately extract the optical flow of moving object area and the moving information. We have simulated the proposed method using the test images. This images are included the noise. Experimental results show that the proposed method get better flow and estimate accurately the moving information.

• Korean Journal of Environmental Agriculture
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• v.33 no.1
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• pp.37-43
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• 2014

BACKGROUND: Water quality data are collected less frequently than flow data because of the cost to collect and analyze, while water quality data corresponding to flow data are required to compute pollutant loads or to calibrate other hydrology models. Regression models are applicable to interpolate water quality data corresponding to flow data. METHODS AND RESULTS: A regression model was suggested which is capable to consider flow and time variance, and the regression model coefficients were calibrated using various measured water quality data with genetic-algorithm. Both LOADEST and the regression using genetic-algorithm were evaluated by 19 water quality data sets through calibration and validation. The regression model using genetic-algorithm displayed the similar model behaviors to LOADEST. The load estimates by both LOADEST and the regression model using genetic-algorithm indicated that use of a large proportion of water quality data does not necessarily lead to the load estimates with smaller error to measured load. CONCLUSION: Regression models need to be calibrated and validated before they are used to interpolate pollutant loads, as separating water quality data into two data sets for calibration and validation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.90-96
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• 2008

This paper presents an algorithm of a single chip acoustic speech enhancement for telematics device. The algorithm consists of two stages, i.e. noise reduction and echo cancellation. An adaptive filter based on cross spectral estimation is used to cancel echo. The external background noise is eliminated and the clear speech is estimated by using MMSE log-spectral magnitude estimation. To be suitable for use in consumer electronics, we also design a low cost, high speed and flexible hardware architecture. The performance of the proposed speech enhancement algorithms were measured both by the signal-to-noise ratio(SNR) and recognition accuracy of an automatic speech recognition(ASR) and yields better results compared with the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.25-32
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• 2022

IR-UWB radar has been regarded as the most promising technology for non-contact respiration and heartbeat monitoring because of its ability of detecting slight motion even in submillimeter range. Measuring heart rate is most challenging since the chest movement by heartbeat is quite subtle and easily interfered with by a random body motion or background noise. Additionally, periodic sampling can be limited by the performance of computer that handles the radar signals. In this paper, we deploy Lomb-Scargle periodogram method that estimates heart rate even with irregularly sampled data and uneven signal amplitude. Lomb-Scargle periodogram is known as a method for finding periodicity in irregularly-sampled and noisy data set. We also implement a motion detection scheme in order to make the heart rate estimation pause when a random motion is detected. Our scheme is implemented using Novelda's X4M03 radar development kit and its corresponding drivers and Python packages. Experimental results show that the estimation with Lomb-Scargle periodogram yield more accurate heart rate than the method of measuring peak-to-peak distance.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.594-610
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• 2023

The horizontal-to-vertical spectral ratio (HVSR) has been widely applied to evaluate ground characteristics such as site response and thickness of the soft sedimentary layer on top of the bedrock via dominant frequencies and amplification factors of microtremors. Eight seismic stations were selected to investigate the HVSR results at the surface and at varying depths, and their variations due to wind speeds. These stations are equipped with seismic sensors on the surface and downhole(s) at depths. The borehole data analysis reveals that the geological condition at burial depth influences the HVSR results. Their dominant frequencies indicate the entire thickness of the soft layer, not the thickness to the bottom or top of the soft sedimentary layer from the seismometer burial depth. Analysis of the background noise observed at the surface showed that the resonance frequency estimation varied with wind speed changes. In the studied cases, the background noise observed in the sedimentary layer at depths of 20 to 66 meters yielded stable and consistent resonance frequency estimation regardless of wind speed fluctuations. The results of the seismic sensors buried deeper than 100 meters are unstable. The result indicates that the background noise from the buried seismometer at shallow depths (~0.3 m) under light wind conditions (wind speeds less than 3 m/s) is sufficient to achieve the purpose of the HVSR analysis.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.6-15
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• 2012

Purpose : The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and $T_2{^*}$ quantification in the liver. Materials and Methods: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of $T_2{^*}$and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a $B_0$ field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and $T_2{^*}$ from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. Results: After correction, in the phantom experiments, the estimated $T_2{^*}$ and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 ${\mu}T/m$ with increased homogeneity in $T_2{^*}$ and fat fractions obtained. Conclusion: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.