• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.4
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• pp.253-287
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• 2018

This paper studies the global stability of a class of discrete-time pathogen infection models with latently infected cells. The rate of pathogens infect the susceptible cells is taken as bilinear, saturation and general. The continuous-time models are discretized by using nonstandard finite difference scheme. The basic and global properties of the models are established. The global stability analysis of the equilibria is performed using Lyapunov method. The theoretical results are illustrated by numerical simulations.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.5
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• pp.385-390
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• 1999

Surface corona discharge characteristics of a dc corona charged ferroelectric pellet barrier have been investigated experimentally. Electric charges stored on the surfaces of the ferroelectric pellets by a dc corona discharge provide partial electric fields on the surfaces of the ferroelectric pellets, which could generate surface corona discharges on the ferroelectric pellets. This system utilizes both the surface discharges on the ferroelectric pellet barrier and the corona discharge between corona tip and mesh electrode. Positive and negative dc voltages were applied to the tip to generate partial discharges, and corona currents were estimated to investigate the buildup charge on ferroelectric pellets as a function of the applied time and the charge relaxation time constants of ferroelectric pellets. As a result, in the case of the negative corona discharge with the ferroelectric pellet barrier, the mean corona current and ozone generation increase greatly, and the surface discharges on the ferroelectric pellets can be fenerated efficiently. It is also found that, charge relaxation time, dielectric constants offerroelectric pellets, polarity of applied voltage and applied time affected to the surface discharges among the ferroelectric pellets.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.318-325
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• 2008

In this paper, a dynamic backoff adjustment method of IEEE 802.15.4 is proposed for time-critical sporadic data in a noisy factory environment. For this, a superframe of IEEE 802.15.4 is applied to a real-time mixed data (periodic data, sporadic data, and non real-time message) transmission in factory communication systems. To guarantee a channel access of real-time sporadic(non-periodic) data, a transmission method using the dynamic backoff is applied to wireless control networks. For the real-time property, different initial BE, CW parameters are used for the dynamic backoff adjustment method. The simulat-ion results show an enhancement of the real-time performance of sporadic emergency data. The proposed method provides the channel access of real-time sporadic data efficiently, and guarantee real-time transmission simultaneously within a limite-d timeframe.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.6
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• pp.1002-1006
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• 2007

Twenty four broilers (Ross) and 24 ducklings (Cherry berry) aged 45days were stunned and killed by conventional neck cut to evaluate the meat characteristics and fatty acid composition of breast meat. Breast meats were removed from each carcass at different post-mortem times. After complete processing, the breast meats were then placed in a polythene bag and kept in a cold storage room at $4^{\circ}C$ for 7 days. The pH of meat samples at different post-mortem times, and meat characteristics and fatty composition at different storage times were evaluated. No significant differences were found in pH at different post-mortem times except at 30 min postmortem, where duck breast showed significantly lower pH than chicken breast. As expected, duck breast meat had significantly higher redness (a*), but lower lightness (L*) value compared to chicken breast. During whole storage time, the a* value remained constant in duck breast. Cooking loss (%) was higher in duck breast compared to chicken breast during the whole storage time. Shear force decreased with increasing storage time in both chicken and duck breast meat, moreover, it decreased rapidly in duck breast compared to chicken breast. The TBARS values increased with increasing storage time in both duck breast and chicken breast meat and was significantly higher in duck breast. The fatty acids (%) C14:0, C16:0, C16:1, C18:2 and C18:3 were significantly higher while C18:0 was significantly lower in duck breast compared to chicken. SFA was increased, while USFA and MUSFA decreased only in duck breast during the 7 day storage time.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.161-164
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• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• Transactions on Electrical and Electronic Materials
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• v.8 no.6
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• pp.278-282
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• 2007

In the complex insulation system that is used in extra high voltage(EHV) devices, according to the trend for electric power equipment of high capacity and reduction of its size, macro interfaces between two different bulk materials which affect the stability of insulation system exist inevitably. In this paper, the dielectric strength decrease of the macro interfaces between epoxy and ethylene propylene diene terpolymer(EPDM) was estimated by using the applied voltage to breakdown time characteristics. Firstly, the AC short time dielectric strength of specimens was measured at room temperature. Then, the breakdown time was measured under the applied constant voltage which is 70% of short time breakdown voltage. With these processes, the life exponent n was determined by inverse power law, and the long time breakdown voltage can be evaluated. The best condition of the interface was LOS(low viscosity(350 cSt) silicone oil spread specimen). When 30 years last on the specimens, the breakdown voltage was estimated 44% of the short time breakdown voltage.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1088-1099
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• 2021

Time-frequency analysis technique is an effective analysis tool for non-stationary processes. In the field of reactor neutron noise, the time-frequency analysis method has not been thoroughly researched and widely used. This work has studied the time-frequency analysis of the reactor neutron noise experimental signals under bubble disturbance and control rod vibration. First, an experimental platform was established, and it could be employed to reactor neutron noise experiment and data acquisition. Secondly, two types of reactor neutron noise experiments were performed, and valid experimental data was obtained. Finally, time-frequency analysis was conducted on the experimental data, and effective analysis results were obtained in the low-frequency part. Through this work, it can be concluded that the time-frequency analysis technique can effectively investigate the core dynamics behavior and deepen the identification of the unstable core process.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.155-161
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• 2001

In this study, we analyzed the $NO_X$ removal efficiency and particle size distribution by the pulsed corona discharge process and investigated the effect of several process variables. The NO removal efficiencies and the particle characteristics were measured and analyzed as the function of initial concentrations of NO, $H_2O$, and $NH_3$, applied voltage, pulse frequency and residence time. As the frequency of applied voltage increases, or as the applied voltage increases or as the residence time increases, the NO removal efficiency increases. The change of initial $NH_3$ and $H_2O$ concentrations do not affect the NO removal efficiency significantly. The particle concentration and size increases with the increases of initial NO concentration, residence time and applied voltage.

• Journal of Aerospace System Engineering
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• v.14 no.3
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• pp.17-23
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• 2020

In this paper, the efficient periodic unsteady flow analysis is developed by using a Chebyshev collocation operator applied to the time differential term of the governing equations. The partial implicit time integration method was also applied in the governing equation for a fluid, which means flux terms were implicitly processed for a time integration and the time derivative terms were applied explicitly in the form of the source term by applying the Chebyshev collocation operator. To verify this method, we applied the 1D unsteady Burgers equation and the 2D oscillating airfoil. The results were compared with the existing unsteady flow frequency analysis technique, the Harmonic Balance Method, and the experimental data. The Chebyshev collocation operator can manage time derivatives for periodic and non-periodic problems, so it can be applied to non-periodic problems later.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).