• Journal of Korea Water Resources Association
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• v.50 no.4
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• pp.253-261
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• 2017

A lot of nonstationary frequency analyses have been studied in recent years as the nonstationarity occurs in hydrologic time series data. In nonstationary frequency analysis, various forms of probability distributions have been proposed to consider the time-dependent statistical characteristics of nonstationary data, and various methods for parameter estimation also have been studied. In this study, we aim to introduce a parameter estimation method for nonstationary Gumbel distribution using ensemble empirical mode decomposition (EEMD); and to compare the results with the method of maximum likelihood. Annual maximum rainfall data with a trend observed by Korea Meteorological Administration (KMA) was applied. As a result, both EEMD and the method of maximum likelihood selected an appropriate nonstationary Gumbel distribution for linear trend data, while the EEMD selected more appropriate nonstationary Gumbel distribution than the method of maximum likelihood for quadratic trend data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.357-368
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• 2012

The purpose of this study is to empirically examine the impact of enterprise R&D investment on international trade in Korea under the new Normal Era. In order to test whether the time series data of trade variables are stationary or not, we put in operation unit root test and cointegration test. Based on VECM (Vector Error Correction Model), we also apply impulse response functions and variance decomposition to estimate the dynamic effects in the short-run and long-run. The results show that the relationship between enterprise R&D investment and international trade (export and import) exists in the long-run as well as in the short-run. The results of applying impulse response functions and variance decomposition also indicate that the impact of enterprise R&D investment on international trade is positive, and a significant portion of fluctuations in the trade variable is explained by enterprise R&D investment. Therefore, enterprise R&D investment must be continuously increased to improve economic growth with promoting trading competition power in Korea under the new Normal Era.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.3
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• pp.167-174
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• 2000

To investigate the effect of amount of nitrogen application on decomposition of barley straw, growth and yield of rice in paddy field of double cropping, this study was conducted to Jeonbuk series at the Honam area from 1997 to 1998. Carbon persistence of barley straw was lowered while nitrogen persistence rate was increased as increasing amount of nitrogen application and carbon -nitrogen ratio was not decreased as increasing amount of nitrogen application. Soil microflora under barley straw application was high in order of actinomycetes>cellulosedecomposer>bacteria>fungi. Nitrogen starvation under barley straw application showed at tillering stage of rice, but this was not appeared in plot of N $144kg\;ha^{-1}$ application. Plant height, culm length and ear length of rice plant by barley straw application were short, but those of N $108kg\;ha^{-1}$ application was not different from compared with none-application barley straw. Rice yield of N $108kg\;ha^{-1}$ applied barley straw was smiliar to none-application barley straw, but that of N 90. $144kg\;ha^{-1}$ was highly decreased.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.87-95
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• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1187-1199
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• 2012

A hybrid forecasting scheme based on wavelet decomposition coupled to a support vector machine model is presented for water demand series that exhibit nonlinear behavior. The use of wavelet transform followed by the SVM model of each leading component is explored as a model for water demand data. The proposed forecasting model yields better results than a traditional ARIMA time series forecasting model in terms of self-prediction problem as well as reproducing the properties of the observed water demand data by making use of the advantages of wavelet transform and SVM model. The proposed model can be used to substantially and significantly improve the water demand forecasting and utilized in a real operation.

• Journal of Astronomy and Space Sciences
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• v.16 no.2
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• pp.241-254
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• 1999

Differential time-series observations of BL Camelopardalis classified as a double mode SX Phoenicis type variable were secured with a charge coupled device. The observed photometric data was reduced using the IRAF Package and the differential magnitudes were obtained through aperture photometry. The periods of BL Cam were analyzed with the Generalized Least-Square Method by Vanicek (1971) and the Fourier Decomposition Method. It was found that the first and second period of BL Cam were 0.0391 day respectively which lead the period ratio of P1/P0=0.81. This period ratio is much different from 0.78 determined by other investigators and also much more larger than that of other double-mode SX Phe type variables. In addition, this period ratio is much different from the value expected from the relation between the metallicity and period ratio. From these results, it can be confirmed that BL Cam is the most extreme case among all double-mode SX Phe type variables.

• Journal of the Microelectronics and Packaging Society
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• v.24 no.1
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• pp.67-73
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• 2017

An analysis of thermal degradation of epoxy based adhesive performed by thermogravimetry tests are presented in this study. Six different heating rates were employed for the weight change measurements. Based on the data, an Arrhenius type modeling equation was developed by calculating activation energies and proportional constants, and $n^{th}$ polynomial function was adopted to predict the weight change rates. The prediction results by the modeling was compared with the data using the average activation energy. It was found that the activation energy at the each heating rate was not same due to the different degradation kinetics, especially at the high heating rate. To overcome this pitfall, a new approach using exponential function series was introduced and employed. The calculation results showed very good agreements with the test data regardless of the heating rates.

• Bulletin of the Korean Chemical Society
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• v.35 no.1
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• pp.218-224
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• 2014

In order to explore new coordination frameworks with novel designed 3-nitrophthalic acid and the same N-donor ancillary ligand, a series of novel coordination complexes, namely, $[Cd_2(3-NPA)_2(TBZ)_2(H_2O)_2]{\cdot}2H_2O$(1), $[Zn_2(3-NPA)_2(TBZ)_2]$(2), $[Zn_2O(3-NPA)(TBZ)(H_2O)]_n$(3), $[Co(3-NPA)(TBZ)(H_2O)]_n$(4) (3-$NPAH_2$ = 3-nitrophthalic acid), have been hydrothermally synthesized through the reaction of 3-nitrophthalic acid with divalent transition-metal salts in the presence of N-donor ancillary coligand (TBZ = thiabendazole). As a result of various coordination modes of the versatile 3-$NPAH_2$ and the coligand TBZ, these complexes exhibit structural diversity. X-ray structure analysis reveals that 1 and 2 are 0D molecular rings, while 3 and 4 are one-dimensional (1D) infinite chain polymers. And the weak O-H${\cdots}$O hydrogen bonds and C-H${\cdots}$O nonclassical hydrogen bonds as well as ${\pi}-{\pi}$ stacking also play important roles in affecting the final structure where complexes 1, 3 and 4 have 3D supramolecular architectures, while complex 2 has a 2D supramolecular network. Also, IR spectra, fluorescence properties and thermal decomposition process of complexes 1-4 were investigated.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.11
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.