• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1707-1720
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• 2023

With the increase in satellite time series data, the utility of remote sensing data is growing. In the analysis of time series data, the relative positional accuracy between images has a significant impact on the results, making image registration essential for correction. In recent years, research on image registration has been increasing by applying deep learning, which outperforms existing image registration algorithms. To train deep learning-based registration models, a large number of image pairs are required. Additionally, creating a correlation map between the data of existing deep learning models and applying additional computations to extract registration points is inefficient. To overcome these drawbacks, this study developed a data augmentation technique for training image registration models and applied it to OffsetNet, a registration model that predicts the offset amount itself, to perform image registration for KOMSAT-2, -3, and -3A. The results of the model training showed that OffsetNet accurately predicted the offset amount for the test data, enabling effective registration of the master and slave images.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.773-783
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• 2009

The effects of rainfall input uncertainty on predictions of stream flow are studied based extended GLUE (Generalized Likelihood Uncertainty Estimation) approach. The uncertainty in the rainfall data is implemented by systematic/non-systematic rainfall measurement analysis in Weida catchment, Germany. PDM (Probability Distribution Model) rainfall runoff model is selected for hydrological representation of the catchment. Using general correction procedure and DUE(Data Uncertainty Engine), feasible rainfall time series are generated. These series are applied to PDM in MC(Monte Carlo) and GLUE method; Posterior distributions of the model parameters are examined and behavioural model parameters are selected for simplified GLUE prediction of stream flow. All predictions are combined to develop ensemble prediction and 90 percentile of ensemble prediction, which are used to show the effects of uncertainty sources of input data and model parameters. The results show acceptable performances in all flow regime, except underestimation of the peak flows. These results are not definite proof of the effects of rainfall uncertainty on parameter estimation; however, extended GLUE approach in this study is a potential method which can include major uncertainty in the rainfall-runoff modelling.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.199-209
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• 2021

By removing the increasing trend that long-term time series average of rice yield due to technological advancement of rice variety and cultivation management, we tried to improve the rice yield estimation model which developed earlier using MODIS NDVI and meteorological data. A multiple linear regression analysis was carried out by using the NDVI derived from MYD13Q1 and weather data from 2002 to 2019. The model was improved by analyzing the increasing trend of rime-series rice yield and removing it. After detrending, the accuracy of the model was evaluated through the correlation analysis between the estimated rice yield and the yield statistics using the improved model. It was found that the rice yield predicted by the improved model from which the trend was removed showed good agreement with the annual change of yield statistics. Compared with the model before the trend removal, the correlation coefficient and the coefficient of determination were also higher. It was indicated that the trend removal method effectively corrects the rice yield estimation model.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.155-164
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• 2016

Algal blooms not only destroy fish habitats but also diminish biological diversity of ecosystem which results into water quality deterioration of 4 major rivers in South Korea. The relationship between algal bloom and environmental factors had been analyzed through the cross-correlation function between concentration of chlorophyll a and other environmental factors. However, time series of cross-correlations can be affected by the stochastic structure such auto-correlated feature of other controllers. In order to remove external effect in the correlation analysis, the pre-whitening procedure was implemented into the cross correlation analysis. The modeling process is consisted of a series of procedure (e.g., model identification, parameter estimation, and diagnostic checking of selected models). This study provides the exclusive correlation relationship between algae concentration and other environmental factors. The difference between the conventional correlation using raw data and that of pre-whitened series was discussed. The process implemented in this paper is useful not only to identify exclusive environmental variables to model Chl-a concentration but also in further extensive application to configure causality in the environment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.35-47
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• 2020

Box-office prediction is important to movie stakeholders. It is necessary to accurately predict box-office and select important variables. In this paper, we propose a multivariate time series classification and important variable selection method to improve accuracy of predicting the box-office. As a research method, we collected daily data from KOBIS and NAVER for South Korean movies, selected important variables using Random Forest and predicted multivariate time series using Deep Learning. Based on the Korean screen quota system, Deep Learning was used to compare the accuracy of box-office predictions on the 73^rd day from movie release with the important variables and entire variables, and the results was tested whether they are statistically significant. As a Deep Learning model, Multi-Layer Perceptron, Fully Convolutional Neural Networks, and Residual Network were used. Among the Deep Learning models, the model using important variables and Residual Network had the highest prediction accuracy at 93%.

• Journal of the korean academy of Pediatric Dentistry
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• v.49 no.3
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• pp.274-284
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• 2022

The purpose of this study was to analyze the trend of the number of births in Gwangju and the number of outpatients in Pediatric Dentistry at Chonnam National University Dental Hospital over the past 10 years (2010 - 2019) and predict the next year using time series analysis. The number of births showed an unstable downward trend with monthly variations, with the highest in January and the lowest in December. The average number of births in 2020 was predicted to be 682 (595 to 782, 95% CI), and the actual number of births was an average of 610. The number of outpatients was relatively stable, showing a month-to-month variation, with highest in August and the lowest in June. The average number of patients in 2020 was predicted to be 603 (505 to 701, 95% CI), and the average number of actual visits was 587. Despite the decrease in the number of births, the number of outpatients was expected to increase somewhat. Due to the special situation of COVID-19, the actual number of births and patients was to be slightly lower than the predicted values, but it was that they were within the predicted confidence interval. Time series analysis can be used as a basic tool to prepare for the low fertility era in the field of pediatric dentistry.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.5
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• pp.259-267
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• 2006

Hydraulic conductivity is the rate of water flux on hydraulic gradient. The van Genuchten Mualem (VGM) model is frequently used for describing unsaturated state of soils, that is composed with the function of soil water potential and soil water content and requests various parameters. This study is to get the value of VGM parameters used Rosetta computer program based on neural network analysis method and to calculate VGM parameters. VGM parameters included Ko(effective saturated hydraulic conductivity), ${\theta}r$(residual soil water content), ${\theta}s$(saturated soil water content), L, n and m. The unsaturated hydraulic conductivity at 10 kPa was calculated by using Rosetta program. Unsaturated hydraulic conductivities of 17 soil series at 1, 3, 5, 7 kPa were also obtained by applying saturated hydraulic conductivity by disk tension infiltrometer based on Gardner and Wooding's equation. Water flow at the water potential of 3 kPa was very low except Namgye, Hagog, Baegsan, Sangju, Seogcheon, Yesan soil series. Unsaturated hydraulic conductivity at 1 kPa showed the highest value for Samgag soil series and was in order of Yesan, Hwabong, Hagog and Baegsan soil series. Those of Gacheon, Seocheon and Ugog soil series were very low. When the value by VGM was compared with the value by disc tension infiltrometer, there was a tendency with exponential function to soils without gravel but there was no tendency to soils including gravel. Conclusively, it would be limited that VGM model for unsaturated hydraulic conductivity analysis applies to Korean agricultural land including gravel and having steep slope, shallow soil depth.