• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1507-1520
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• 2014

In this paper, we show that there is a positive correlation between the level and the volatility of interest rate and thus suggest that a proper interest rate volatility coefficient (IRVC), a factor used in evaluating the interest rate risk that insurers are exposed to, should be chosen in accordance with the level of interest rate. To this end, we calculate the historical volatility of interest rate using data on government bond yields and show a proportionate relationship between interest rate and historical volatility. The review of exponential Vasicek (EV) and Cox-Ingersoll-Ross (CIR) models for interest rate also confirms the positive correlation between them. The estimation of IRVC by EV and CIR models are 0.9 and 1.1, respectively, which are much smaller than the one under the current risk-based capital (RBC) requirement. We provide modified IRVCs reflecting the level of interest by the two interest rate models. Using modified IRVCs can be a more reasonable method to evaluate the interest rate risk that insurers face.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.72-77
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• 1999

In this paper, we propose a wavelet VQ approach to modeling VBR broadcast video traffic. The proposed method decomposes video traffic into two parts via wavelet transformation, and models each part separately. The first part, which is modeled by an AR(1) process, serves to capture the long-term trend of the traffic; the second part, classified via vector quantization, addresses the short-term behavior of the traffic. Compared with other VBR video models, our model has three advantages. First, it allows the separate modeling of long- and short-term behavior of the video traffic; second, it preserves the periodic coding structure in traffic data; and third, it provides an unified approach for the frameand slice-level traffic modeling. We demonstrate the validity of our model by statistical measurements and network performance simulation.

• Journal of Information Technology Applications and Management
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• v.30 no.3
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• pp.1-13
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• 2023

Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.151-152
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and Stochastic Volatility Models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.835-841
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and stochastic volatility models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• The Journal of Fisheries Business Administration
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• v.40 no.3
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• pp.169-187
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• 2009

This study analyzes the long and short term effect of exchange rate on the import of Korea's fisheries focussed on main fisheries imported from China. The estimation models consist of the following contents. The first model consists of one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{CHO}$) and three independent variables-${RP_t}^{CHO}$, $EXC_t$ and $GDP_t$. The second one-one dependent variable-import quantity of fisheries imported from China(${JMQ_t}^{NAG})$ and three independent variables-${RP_t}^{NAG}$, $EX_t$ and $GDP_t$. the third one-one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{AH}$) and three independent variables-${RP_t}^{AH}$, $EX_t$ and $GDP_t$. the forth one-one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{KO}$) and three independent variables-${RP_t}^{KO)$, $EX_t$ and $GDP_t$. the last one is made up of one dependent variable-import quantity of fisheries imported from China(${IMQ_t}^{GAL}$) and three independent variables-, ${RP_t}^{GAL}$, $EX_t$ and $GDP_t$. and. The estimation results show that exchange rate of the independent variables are statistically significant in only the first model. The figure is elastic. Especially, the effect of exchange rate in first model is grater than that of the. However, the effect of exchange rate, one of independent variables in the ECM, is not statistically significant.

• Speech Sciences
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• v.7 no.1
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• pp.17-29
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• 2000

The STC(Sinusoidal Transform Coding) is a vocoding technique that uses a sinusoidal speech model to obtain high- quality speech at low data rate. It models and synthesizes the speech signal with fundamental frequency and its harmonic elements in frequency domain. To reduce the data rate, it is necessary to represent the sinusoidal amplitudes and phases with as small number of peaks as possible while maintaining the speech quality. As a basic research to develop a low-rate speech coding algorithm using the sinusoidal model, in this paper, we investigate the speech quality depending on the number of sinusoids. By varying the number of spectral peaks from 5 to 40 speech signals are reconstructed, and then their qualities are evaluated using spectral envelope distortion measure and MOS(Mean Opinion Score). Two approaches are used to obtain the spectral peaks: one is a conventional STFT (Short-Time Fourier Transform), and the other is a multiresolutional analysis method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.179-180
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• 2023

In this study, the performance of the prediction model was compared and analyzed using DNN and LSTM learning models to predict the amount of dry shrinkage of the concrete. As a result of the analysis, DNN model had a high error rate of about 51%, indicating overfitting to the training data. But, the LSTM learning model showed a relatively higher accuracy with an error rate of 12% compared to the DNN model. Also, the Pre_LSTM model which preprocess data, showed the performance with an error rate of 9% and a coefficient of determination of 0.887 in the LSTM learning model.

• Nutrition Research and Practice
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• v.4 no.5
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• pp.375-382
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• 2010

The precise effects of protein intake on fractional synthesis rate (FSR) of muscle protein are still under debate. The sample size of these studies was small and the conclusions in young and elderly subjects were inconsistent. To assess the effect of dietary protein intake on the FSR level, we conducted a meta-analysis of controlled protein intake trials. Random-effects models were used to calculate the weighted mean differences (WMDs). Ten studies were included and effects of short-term protein intake were evaluated. In an overall pooled estimate, protein intake significantly increased the FSR (20 trials, 368 participants; WMD: 0.025%/h; 95%CI: 0.019-0.031; P < 0.0001). Meta-regression analysis suggested that the protein dose was positively related to the effect size (regression coefficient = 0.108%/h; 95%CI: 0.035, 0.182; P = 0.009). A subgroup analysis indicated that protein intake significantly increased FSR when the protein dose was ${\leq}$ 0.80 g/kg BW (16 trials, 308 participants; WMD: 0.027%/h; 95%CI: 0.019-0.031; P < 0.0001), but did not affect FSR when the protein dose was > 0.80 g/kg BW (4 trials, 60 participants; WMD: 0.016%/h; 95%CI: 0.004-0.029; P = 0.98). In conclusion, this study is the first integrated results showing that a short-term protein intake is effective at improving the FSR of muscle protein in the healthy elderly as well as young subjects. This beneficial effect seems to be dose-dependent when the dose levels of protein range from 0.08 to 0.80 g/kg BW.