• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2213-2220
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• 2017

This paper proposes an efficient approach for hardware implementation of moving object detection (MOD) processor using effective Gaussian mixture learning (EGML)-based background subtraction method. Arithmetic units used in background generation were implemented using LUT-based approximation to reduce hardware complexity. Hardware resources used for both background subtraction and Gaussian probability density calculation were shared. The MOD processor was verified by FPGA-in-the-loop simulation using MATLAB/Simulink. The MOD performance was evaluated by using six types of video defined in IEEE CDW-2014 dataset, which resulted the average of recall value of 0.7700, the average of precision value of 0.7170, and the average of F-measure value of 0.7293. The MOD processor was implemented with 882 slices and block RAM of $146{\times}36kbits$ on Virtex5 FPGA, resulting in 60% hardware reduction compared to conventional design based on EGML. It was estimated that the MOD processor could operate with 75 MHz clock, resulting in real-time processing of $800{\times}600$ video with a frame rate of 39 fps.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Geophysics and Geophysical Exploration
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• v.9 no.2
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• pp.139-147
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• 2006

We had conducted a numerical modeling to investigate seismic properties of gas hydrate with field parameters acquired over the East sea in 1998. We used a 2-D staggered grid finite difference method to generate synthetic elastic seismograms for multi-channel seismic survey, OBC (Ocean Bottom Cable) survey and VCS (Vertical Cable Seismic) survey. The results of this study showed that the method using staggered grid yielded stable results and could be used to seismic imaging. We could find out the high amplitude anomaly and the phase reversal phenomenon of reflection wave at interface between the gas hydrate layer and free gas layer such a BSR (Bottom Simulating Reflector) which is the evidence for existence of gas hydrate in seismic reflection data. And we computed the reflection coefficients at the incident angles corresponding to offset distance with the synthetic seismograms. The reflection coefficients acquired from the numerical modeling were nearly consistent with the reflection coefficient computed by Shuey's equation.

• Geophysics and Geophysical Exploration
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• v.20 no.4
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• pp.216-225
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• 2017

Recently, steel casing became an interesting issue when applying controlled-source electromagnetic (EM) method to various fields because effects of steel casing on EM responses are not negligible. This study employed an approach that approximates the steel casing as a series of electric dipole sources in order to develop the numerical algorithm for the efficient simulation of EM responses in the presence of steel casing. After verifying the validity of the developed algorithm, we analyze effects of steel casing on EM responses with the synthetic model simulating geothermal reservoir environment. The analysis showed that the effects of steel casing on EM responses are localized near the casing and increase as the transmitter becomes close to the casing. In addition, through the analysis on the EM responses by the injection of clean water, we confirm that the effects of casing are negligible when interpreting the after-injection data acquired using the transmitter located far enough from the casing. Considering the difference in EM responses between before and after injection in inversion, the effects of the casing can be neglected although after-injection data shows considerable difference due to the close distance between the transmitter and casing. To investigate this kind steel casing effect, the precise analysis on EM responses should be preceded. The algorithm introduced in this study will contribute to the reliable calculations of EM responses distorted by the conductive steel casing.

• The Journal of the Acoustical Society of Korea
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• v.33 no.2
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• pp.87-93
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• 2014

If one identifies the detailed distribution of pressure and axial velocity at a source plane, the position and strength of major noise sources can be known, and the propagation characteristics in axial direction can be well understood to be used for the low noise design. Conventional techniques are usually limited in considering the constant source characteristics specified on the whole source surface; then, the source activity cannot be known in detail. In this work, a method to estimate the pressure and velocity field distribution on the source surface with high spatial resolution is studied. The matrix formulation including the evanescent modes is given, and the nearfield measurement method is proposed. Validation experiment is conducted on a wide duct system, at which a part of the source plane is excited by an acoustic driver in the absence of airflow. Increasing the number of evanescent modes, the prediction of pressure spectrum becomes further precise, and it has less than -25 dB error with 26 converged evanescent modes within the Helmholtz number range of interest. By using the converged modal amplitudes, the source parameter distribution is restored, and the position of the driver is clearly identified at kR = 1. By applying the regularization technique to the restored result, the unphysical minor peaks at the source plane can be effectively suppressed with the filtering of the over-estimated pure radial modes.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.77-85
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• 2001

This paper proposes a method of time-scale modification of polyphonic audio signals based on a sinusoidal model. The signals are modeled with sinusoidal component and noise component. A multiresolution filter bank is designed which splits the input signal into six octave-spaced subbands without aliasing and sinusoidal modeling is applied to each subband signal. To alleviate smearing of transients in time-scale modification a dynamic segmentation method is applied to subbands which determines the analysis-synthesis frame size adaptively to fit time-frequency characteristics of the subband signal. For extracting sinusoidal components and calculating their parameters matching pursuit algorithm is applied to each analysis frame of subband signal. In accordance with spectrum analysis a psychoacoustic model implementing the effect of frequency masking is incorporated with matching pursuit to provide a resonable stop condition of iteration and reduce the number of sinusoids. The noise component obtained by subtracting the synthesized signal with sinusoidal components from the original signal is modeled by line-segment model of short time spectrum envelope. For various polyphonic audio signals the result of simulation shows suggested sinusoidal modeling can synthesize original signal without loss of perceptual quality and do more robust and high quality time-scale modification for large scale factor because of representing transients without any perceptual loss.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.57-65
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• 2021

We propose a novel method to detect abnormal data of specific symptoms using deep learning in air pollution measurement system. Existing methods generally detect abnomal data by classifying data showing unusual patterns different from the existing time series data. However, these approaches have limitations in detecting specific symptoms. In this paper, we use DeepLab V3+ model mainly used for foreground segmentation of images, whose structure has been changed to handle one-dimensional data. Instead of images, the model receives time-series data from multiple sensors and can detect data showing specific symptoms. In addition, we improve model's performance by reducing the complexity of noisy form time series data by using 'piecewise aggregation approximation'. Through the experimental results, it can be confirmed that anomaly data detection can be performed successfully.

• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.78-86
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• 2019

This paper describes the stiffness optimization of the torsion spring hinge of the large SAR antenna considering the deployment performance. A large SAR antenna is folded in a launch environment and then unfolded when performing a mission in orbit. Under these conditions, it is very important to find the proper stiffness of the torsion spring hinge so that the antenna panels can be deployed with minimal impact in a given time. If the torsion spring stiffness is high, a large impact load at the time of full deployment damages the structure. If it is weak, it cannot guarantee full deployment due to the deployment resistance. A multi-body dynamics analysis model was developed to solve this problem using RecurDyn and the development performance were predicted in terms of: development time, latching force, and torque margin through deployment analysis. In order to find the optimum torsion spring stiffness, the deployment performance was approximated by the response surface method (RSM) and the optimal design was performed to derive the appropriate stiffness value of the rotating springs.

• Korean Journal of Food Science and Technology
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• v.8 no.1
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• pp.19-32
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• 1976

The laboratory Mejus as well as home-made Meju and improved Meju received from Korea were ripened in the brine for up to 8 months and the changes is the chemical composition during the process were determined and the differences between the types of Meju were compared. On the basis of the amino acid pattern, the changes in the protein quality of soybean during the process was evaluated. No significant changes in the general chemical composition of Meju were noticed during the ripening for 8 months. However, the nitrogen solubility of Meju increased for $13{\sim}29%$ to $66{\sim}78%$ during 8 month ripening of the Meju-brine mixture. The concentration of free amino-N to the total-N increased from $4{\sim}7%$ in Meju to $29{\sim}35%$ in the 8month ripened mixture. The concentration of amino-N to the total-N increased from $1{\sim}4%$ in Meju to $5{\sim}14%$ in the 8month ripened mixture and the changes varied with the type of Meju used. Remarkable changes in the amino acid pattern of soybean were occured during the ripening process. The concentration of methionine decreased to the half of original Meju during the first month of ripening. Arginine and histidine were destroyed rapidly by the ripening longer than 1 month. A considerable amount of ornithine was synthesized during the ripening. The amino acid pattern of Meju did change drastically during the ripening longer than 3 months and the changes varied with the type of Meju. The retention of the nutrients in soybean during 8 month ripening of the laboratory 3 month Meju in the brine was 49% for carbohydrates, 107% for crude fat, 93% for crude protein and 74% for the total amino acid. Histidine, arginine and methionine and 74% for the total amino acid. Histidine, arginine and methionine were the most damaged during the process, retaining only 25%, 27% and 49% of the contents in raw soybean, respectively, whereas lysine retained 79%. By the separation of the 8 month ripened mixture, approximately 60% of crude protein, all of crude fat and 80% of carbohydrates in the mixture were retained in soypaste. Soypaste contained higher concentrations of amino acids per 16gN compared to soysauce, except for lysine. The most limiting amino acid of the protein was the S-containing amino acids in all cases studied, whereas the second limiting amino acid varied from valine in soybean to threonine in most of Mejus and the brine mixtures, lysine in most of soypastes and tryptophan in some of soysauces. According to the protein quality evaluation made by the reference of the FAO provisional pattern of amino acid, the chemical score of raw soybean was 82, which was reduced to 77 by cooking and further reduced to $71{\sim}74$ by Meju fermentation. At the eighth month of ripening the chemical score of the Meju-brine mixtures were reduced to $51{\sim}66. After the separation, the chemical score of soypaste ranged from 60 to 71, whereas that of soysauce varied from 45 to 57. Generally, the products made from improved Meju recorded the highest score, whereas those made from homemade Meju showed the poorest protein quality. The essential amino acid index(EAAI) of the samples was similar to the chemical score, but it appeared to fit the overall changes in the amino acid pattern during the process better than the chemical score.