• Journal of Digital Convergence
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• v.13 no.10
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• pp.287-293
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• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.277-283
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• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.494-503
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• 2002

A motion vector re-estimation algorithm for image downscaling in discrete consine transform domain is presented. Kernel functions are difined using SAD (Aum of Absolute Difference) and edge information of a macroblock. The proposed method uses these kernel functions to re-estimate a new motion vector of the downscaled image. The motion vectors from the incoming bitstream of transcoder are reused to reduce computation burden of the block-matching motion estimation, and we also reuse the given motion vectors. Several experiments in this paper show that the computation efficiency and the PSNR (Peak Signal to Noise Ratio) and better than the previous methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1039-1046
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• 2017

Campus microgrid is designed and built by considering not only power generation but also power consumption management as connected microgrid type because the main goal of the campus microgrid is to save power consumption costs. There are many functions to achieve the goal and they are mainly to use generation-based functions such as islanding operation for peak management and for emergency events. In power distribution operation, Conservation Voltage Reduction (CVR) is applied in order to reduce power consumption. The CVR is defined as a function for load consumption reduction by voltage reduction in order to reduce peak demands and energy consumption. However, application of CVR to microgrid is difficult because the microgrid cannot control a tap of transformer in a substation and the microgrid normally is not designed with phase modifying equipment like a step-voltage-regulator which can control voltage in power distribution system operation. In addition, an impact of the CVR is depended on load characteristics such as a normal load, a rated power, and synchronous motors. Therefore, this paper proposes an application of CVR using linear voltage control based AVR in campus microgrid with power consumption reduction considering characteristics of load and component in the microgrid. The proposed system can be applied to each buildings by a configuration of power distribution cables; and the application results and CVR factor are presented in this paper.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• Journal of agriculture & life science
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• v.50 no.5
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• pp.173-182
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• 2016

This study was aimed to identify the phenotypic relationships between calving interval and lactation curve parameters in Korean Holstein cow. The data of 36,505 lactation records was obtained from the Dairy Herd Improvement program run by Dairy Cattle Improvemnet Center of National Agricultural Federation of Korea. All lactation records were collectied from the multiparous cows calving between 2011 to 2013. The estimated lactation curves were drawn using Wood model based on actual milk yield records, and NLIN Procedure of SAS program (ver. 9.2). General linear multivariate models for calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day, and peak yield included fixed effects of calving year-season (spring, summer, fall and winter) and parity(2, 3 and 4). For calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day and peak yield, all two fixed effect(calving year-season, parity) were significant(p<0.05). The estimated lactation functions using Wood model for 2, 3, and 4 parity were yt=24.66t0.175e-0.00302t, yt=24.69t0.192e-0.00334t, and yt=24.22t0.200e-0.00341t, respectively. Phenotypic correlation (partial residual correlation) between calving interval and 305-d milk yield, A, b, c, persistency, peak day, and peak yield were 0.093, -0.014, 0.028, -0.046, 0.099, 0.085, and 0.052, respectively. To conclude, if calving interval increase then ascent to peak, persistency, peak day and peak yield are increase, and descent after peak is decrease. So, total 305-d milk yield is increase.

• Investigative Magnetic Resonance Imaging
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• v.4 no.2
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• pp.128-135
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• 2000

Purpose : The aim of this study is to develope the Magnetic Resonance Spectroscopy(MRS) data processing S/W which plays an important role as a diagnostic tool in clinical field. Materials and methods : Post-processing software of MRS based on graphical user interface(GUI) under windows operating system of personal computer(PC) was developed using MATLAB(Mathwork, U.S.A.). This tool contains many functions to increase the quality of spectrum data such as DC correction, zero filling, line broadening, Gauss-Lorentzian filtering, phase correction, etc. And we obtained the normal human brain $^1H$ MRS data from parietal white matter, basal ganglia and occipital grey matter region using 1.5T Gyroscan ACS-NT R6 (philips, Amsterdam, Netherland) MRS package. The analysis of the MRS peaks were performed by obtaining the ratio of peak area. Results : The peak ratios of NAA/Cr, Cho/Cr, MI/Cr for the different MRS machines have a little different values. But these peak ratios were not significantly different between different echo time MRS peak ratios in the same machine (p<0.05). Conclusion : MRS post-processing S/W based on GUI using PC was developed and applied to the analysis of normal human brain $^1H$ MRS. This independent MRS processing job increases the performance and throughput of patient scan of main console. Finally, we suggest that the database for normal in-yivo human MRS data should be obtained before clinical applications.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.4
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• pp.590-596
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• 1999

The effect of evaporative cooling in alleviating seasonal variations of dairy cows raised in AlMarai Dairy Farms in the Kingdom of Saudi Arabia were studied using milking record collected during the period of 1991 to 1996. The data included 13303 and 8137 records represented winter and summer calving seasons. Evaporative cooling system improved production for cows calved in summer. The least square means of milk yield were 9631 and 9556 liter for cows calved in winter and summer seasons but no significant differences (p<0.05) were observed between the yield of two seasons. No significant effect of season on calving under evaporative cooling on most of the biweekly points of the lactation curve. The farm, parity and milk level showed a significant effect on the shape of the curve. Functions of the lactation curve like initial yield, 305 MY, peak yield, time of peak and duration were estimated for each phase of the lactation curve.

• Bulletin of the Korean Chemical Society
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• v.28 no.10
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• pp.1689-1696
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• 2007

Equilibrium molecular dynamics (EMD) simulations are used to evaluate the transport coefficients of argonkrypton mixtures at two liquid states (state A: 94.4 K and 1 atm; state B: 135 K and 39.5 atm) via modified Green-Kubo formulas. The composition dependency of the volume at state A obeys close to the linear model for ideal liquid mixture, while that at state B differs from the linear model probably due to the high pressure. The radial distribution functions for the Ar-Kr mixture (x = 2/3) show a mixing effect: the first peak of g11 is higher than that of g(r) for pure Ar and the first peak of g22 is lower than that of g(r) for pure Kr. An exponential model of engineering correlation for diffusion coefficient (D) and shear viscosity (η) is superior to the simple linear model for ideal liquid mixtures. All three components of thermal conductivity (λpm, λtm, and λti) at state A and hence the total thermal conductivity decrease with the increase of x. At state B, the change in λtm is dominant over those in λpm and λti, and hence the total thermal conductivity decrease with the increase of x.