• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.867-872
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• 2005

This study introduces a method to evaluate the probability of a specific area to be affected by a drought of a given severity and shows its potential for investigating agricultural drought characteristics. The method is applied to Gyeonggi as a case study. The proposed procedure includes Standard Precipitation Index(SPI) time series, which are linearly transformed by the Empirical Orthogonal Functions(EOF) method, These EOFs are extended temporally with AutoRegressive Moving Average(ARMA) method and spatially with Kriging method. By performing these simulations, long time series of SPI can be simulated for each designed grid cell in whole Gyeonggi area. The probability distribution functions of the area covered by a drought and the drought severity are then derived and combined to produce drought severity-area-frequency(SAF) curves.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.19-27
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• 1998

To achieve the required services in the next-generation cellular telephone systems, the size of the cell become smaller and/or is of mixed macrocells and microcells. For more efficient system control, We make use of the mobile position and velocity information, provided that the mobility information is relatively accurate. In this paper, we propose an improved version of path-loss measurement algorithm introduced in literature[11]. The microcellular structure with severe multipath fading, reflection and refraction make mobile position and velocity estimation very difficult. In the proposed method, the pre-recorded path-loss informations, called the discrete position data base, are searched to estimate the position. Velocity estimation is obtained as a difference of the position values with respect to the time difference. Moving average filter is applied to smooth the estimated velocity and to reduce the error in the estimates. We also propose a method to simplify system implementation by reducing search area for discrete area database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1420-1428
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• 2017

It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.

• Journal of Korea Multimedia Society
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• v.11 no.10
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• pp.1325-1337
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• 2008

The difference of environments between training and recognition is the major reason of degradation of speech recognition. To solve this mismatch of environments, various noise processing methods have been studied. Among them, ERN(log-Energy dynamic Range Normalization) and SEN(Silence Energy Normalization) for normalization of log energy features show better performance than others. However, these methods have a problem that they can hardly achieve normalization for the relatively higher values of log energy features and the environmental mismatch caused by this problem becomes bigger especially in low SNR environments. To solve these problems, we propose applying ARMA filter as post-processing for smoothing log energy features by calculating the moving average in auto-regression scheme. From the recognition results conducted on Aurora 2.0 DB, the proposed method shows improved recognition results comparing with conventional methods.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.382-391
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• 2020

In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.159-169
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• 2009

The goal of this paper is to get the result of the forecasting for the numbers of a high-school graduate by a moving average method and the statistical analysis for numbers of the limit of matriculation on the most colleges and universities in Daegu city and Gyoungbook until 2026 year. Recently, the decrease of the number of a high-school graduate have influences on the number of limit matriculation. The future of most colleges and universities in Daegu city and Gyoungbook is hanging in the balance after the crisis of the serious decrease of the number of a high-school graduate until 2026 year.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.101-107
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• 2012

With the foot-and-mouth disease problems emerging as a serious social issue, this study set out to analyze the problems with the current setting of preventive zones against epidemics and find ways to minimize damage through preventive measures. For those purposes, the study analyzed the outbreaks of the foot-and-mouth disease and assumed that the disease would be transmitted via vehicles along the roads based on the network map of national roads and boundaries among administrative districts to conduct network analysis. The analysis results were then used to estimate spread time, whose results were then categorized according to lineal road distance and actual road distance. Then lineal moving speed and actual moving speed on the road were obtained according to the national roads and administrative districts to analyze the problems with the current method of setting preventive zones against the foot-and-mouth disease. As for spread speed around the areas where the foot-and-mouth disease broke out, the average lineal spread speed was 53.9km/day, and the average spread speed on the road was 71.1km/day, which indicates there are problems with the current method of setting preventive zones against epidemics.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.25-31
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• 2014

Gas metal arc welding (GMAW) is currently the most widely used arc welding processes in the industry because of its high metal deposition rate, flexibility and low cost. It is attractive for high-productivity manufacturing applications and is well suited to automatic or robotic welding. Welding voltage and current have a significant impact on the weld bead. However, welding voltage and current are changed variously according to welding condition and user environment, and prediction is impossible. To determine the welding conditions, the welding current and voltage are applied to the appropriate data analysis techniques. In this paper, we used the moving average filter to the welding voltage and current data, and normal and abnormal welding waves were distinguished.

• Korean Journal of Agricultural Science
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• v.41 no.3
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.555-561
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• 2000

The remote field eddy current (RFEC) inspection was performed on the ductile cast iron pipes with nominal outer diameter of 100mm, which were machined with various shapes and sizes of defects. Ductile cast iron pipes which are used as water supply pipe have the non-uniform thickness and asymmetric cross section due to relatively high degree of allowable errors during the manufacturing processes. These characteristics of ductile cast in pipes cause the long range background noises in RFEC signals along the pipe. In this study, tile machined defects in pipes were effectively classified by the moving window average (MWA) method which eliminated the long-range noise. The voltage plane polar plots (VPPP) method was used to quantitatively evaluate the depth and circumferential degree of defects. The VPPP signatures showed that the angle between defect signature and the normalized in-phase component on the VPPP is linear to the depth of defects. The nondestructive RFEC technique proved to be capable of quantitatively evaluating the machined defects of underground water supply pipe.