• Ocean and Polar Research
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• v.42 no.3
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• pp.195-210
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• 2020

The observed time series from the Korea Ocean Research Stations (KORS) in the Yellow and East China Seas (YECS) have various sources of noise, including bio-fouling on the underwater sensors, intermittent depletion of power, cable leakage, and interference between the sensors' signals. Besides these technical issues, intricate waves associated with background tidal currents tend to result in substantial oscillations in oceanic time series. Such technical and environmental issues require a regionally optimized automatic quality control (QC) procedure. Before the achievement of this ultimate goal, we examined the approach of the Ocean Observatories Initiative (OOI)'s standard QC to investigate whether this procedure is pertinent to the KORS. The OOI QC consists of three categorized tests of global/local range of data, temporal variation including spike and gradient, and sensor-related issues associated with its stuck and drift. These OOI QC algorithms have been applied to the water temperature time series from the Ieodo station, one of the KORS. Obvious outliers are flagged successfully by the global/local range checks and the spike check. Both stuck and drift checks barely detected sensor-related errors, owing to frequent sensor cleaning and maintenance. The gradient check, however, fails to flag the remained outliers that tend to stick together closely, as well as often tend to mark probably good data as wrong data, especially data characterized by considerable fluctuations near the thermocline. These results suggest that the gradient check might not be relevant to observations involving considerable natural fluctuations as well as technical issues. Our study highlights the necessity of a new algorithm such as a standard deviation-based outlier check using multiple moving windows to replace the gradient check and an additional algorithm of an inter-consistency check with a related variable to build a standard QC procedure for the KORS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.599-605
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• 2009

Changes of atmospheric pressures cause short- and long-term crustal deformations and thus become error sources in the site positions estimated from space geodesy equipments. In this study, we computed daily displacements due to the atmospheric pressure loading (ATML) at the 14 permanent GPS sites operated by National Geographic Information Institute. And the 10-year GPS data collected at those stations were processed to create a continuous time series of the height estimate. Then, we corrected for the ATML from the GPS height time series to see if the correction changes the site velocity and improves the precision of the time series. While the precision improved by about 4% on average, the velocity change was not significant at all. We also investigated the overall characteristics of the ATML in the southern Korean peninsula by computing the ATML effects at the inland grid points with a $0.5^{\circ}{\times}0.5^{\circ}$ spatial resolution. We found that ATML displacements show annual signals and those signals can be fitted with sinusoidal functions. The amplitudes were in the range of 3-4 mm, and they were higher at higher latitudes and lower at the costal area.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.2
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• pp.124-132
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• 2018

This research presents the results of an investigation on the characteristic of multi-component blended high fluidity mortars. The binder was blended ordinary Portland cement(OPC), ground granulated blast furnace slag(GGBFS), calcium sulfoaluminate(CSA) and ultra rapid setting cement(URSC). The GGBFS was replaced by OPC from 30%(P7 series), 50%(P5 series) and 70%(P3 series), CSA and URSC was 10% or 20% mass. The superplasticizer of polycarboxylate type were used. A constant water-to-binder ratio(w/b)=0.35 was used for all mixtures. Test were conducted for mini slump, setting time, V-funnel, compressive strength and drying shrinkage. According to the experimental results, the contents of superplasticizer, V-funnel and compressive strength increases with an increase in CSA or URSC contents for all mixtures. Moreover, the setting time and drying shrinkage ratio decrease with and increase in CSA or URSC. CSA decreased dry shrinkage but URSC had less effect. However, the mixed binders of CSA and URSC had a large effect of reducing drying shrinkage by complementary effect. This is effective for improving the initial strength of URSC, and CSA is effective for the expansion and improvement of long-term strength.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.505-510
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• 2014

Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.

• Journal of Korea Water Resources Association
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• v.49 no.11
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• pp.913-922
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• 2016

A long-term precipitation record is typically required for establishing the reliable water resources plan in the watershed. However, the observations in the hourly precipitation data are not always consistent and there are missing values within the time series. This study aims to develop a hourly rainfall simulator for extending rainfall data, based on the well-known Neyman-Scott Rectangular Pulse Model (NSRPM). Moreover, this study further suggests a multisite hourly rainfall simulator to better reproduce areal rainfalls for the watershed. The proposed model was validated with a network of five weather stations in the Uee-stream watershed in Seoul. The proposed model appeared a reasonable result in terms of reproducing most of the statistics (i.e. mean, variance and lag-1 autocovariance) of the rainfall time series at various aggregation levels and the spatial coherence over the weather stations.

• Journal of the Korea Institute of Building Construction
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• v.14 no.6
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• pp.631-637
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• 2014

Recently, following the application of modular housing techniques to not only residential sector, but also to business sector, the scope of modular housing market b expanding. In the case of other developed countries, such markets are entering into the maturity stage, though the market in Korea is not fully formed yet. Thus, it is difficult to check its trend to estimated mid- to long-term prospects of the market. In this context, the study predicted demand of the modular housing market by using a non-linear prediction model based on time series analysis. To get the prospects for the modular housing market, the quantity of housing supply was estimated based on the estimated quantity of newly built housings, and assumed that a portion of the supplied quantity would be the demand for modular housings. Based on the assumption of demand for modular housings, several scenarios were analyzed and the prospects of the modular housing market was obtained by utilizing the non-linear prediction model.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.773-779
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• 2019

We developed the automatic ground deformation monitoring system using Sentinel-1 satellites which is operating by European Space Agency (ESA) for the Korea Peninsula's ground disaster monitoring. Ground deformation occurring over a long-term period are difficult to monitoring because it occurred in a wide area and required a large amount of satellite data for analysis. With the development of satellites, the methods to regularly observe large areas has been developed. These accumulated satellite data are used for time series ground displacement analysis. The National Disaster Management Research Institute (NDMI) established an automation system for all processes ranging from acquiring satellite observation data to analyzing ground displacement and expressing them. Based on the system developed in this research, ground displacement data on the Korean Peninsula can be updated periodically. In the future, more diverse ground displacement information could be provided if automated small regional analysis systems, multi-channel analysis method, and 3D analysis system techniques are developed with the existing system.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.31-39
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• 2016

In this study, the time series of the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region were analyzed and they showed a strong trend of increase until recently. To determine the existence of a climate regime shift in this time series, the statistical change-point analysis was applied and it was found that the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region increased sharply since 1993. Therefore, in order to examine the cause of the sharp increase of the days with $25^{\circ}C$ or higher temperatures in the Jeju region, the differences between the averages of 1994~2013 and the averages of 1974~1993 were analyzed for the large-scale environment. In the Korean Peninsula including the Jeju region, precipitable water and total cloud cover decreased recently due to the intensification of strong anomalous anticyclones near the Korean Peninsula in the top, middle and bottom layers of the troposphere. As a result of this, the number of days with $25^{\circ}C$ or higher temperatures in the Jeju region could increase sharply in recent years. Furthermore, in the analysis of sensible heat net flux and daily maximum temperatures at 2 m, which is the height that can be felt by people, the Korean Peninsula was included in the positive anomaly region. In addition, the frequency of typhoons affecting the Korean Peninsula decreased recently, which reduced the opportunities for air temperature drops in the Jeju region.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.