• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Journal of Korean Society on Water Environment
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• v.24 no.2
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• pp.230-238
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• 2008

This study evaluated the economic aspect of the rainwater harvesting facilities by hydrologically analyzing the inflow, rainwater consumption, rainfall loss, tank storage, and overflow time series to derive the net rainwater consumption and the number of days of rainwater available. This study considers several rainwater harvesting facilities in Seoul National University, Korea Institute of Construction Technology and Daejon World Cup Stadium and the results derived are as follows. (1) Increasing the water consumption decreases the number of days of rainwater available. (2) Due to the climate in Korea, a larger tank storage does not increase the amount and the number of days of water consumption during wet season (June to September), but a little in October. (3) Economic evaluation of the rainwater harvesting facilities considered in this study shows no net benefit (private benefit). (5) Flood reduction effect of rainwater harvesting facilities was estimated very small to be about 1% even in the case that 10% of all the basin is used as the rainwater collecting area.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.3
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• pp.117-129
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• 1993

This study was conducted to get reasonable and abundant hydrological time series of monthly flows simulated by a best fitting stochastic simulation model for the establishment of rational design and the rationalization of management for agricultural hydraulic structures including reservoirs. Comparative analysis carried out for both statistical characteristics and synthetic monthly flows simulated by the multi-season first order Markov model based on Gamma distribution which is confirmed as good one in the first report of this study and by Harmonic synthetic model analyzed in this report for the six watersheds of Yeong San and Seom Jin river systems. 1.Arithmetic mean values of synthetic monthly flows simulated by Gamma distribution are much closer to the results of the observed data than those of Harmonic synthetic model in the applied watersheds. 2.In comparison with the coefficients of variation, index of fluctuation for monthly flows simulated by two kinds of synthetic models, those based on Gamma distribution are appeared closer to the observed data than those of Harmonic synthetic model both in Yeong San and Seom Jin river systems. 3.It was found that synthetic monthly flows based on Gamma distribution are considered to give better results than those of Harmonic synthetic model in the applied watersheds. 4.Continuation studies by comparison with other simulation techniques are to be desired for getting reasonable generation technique of synthetic monthly flows for the various river systems in Korea.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.108-111
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• 2003

Altimeter data from the Topex/Poseidon (T/P) were analyzed to study the sea surface circulation and its variability in the North East Asian Seas. Long term averaged T/P sea level time series data where compared with in situ sea level measurements from a float-operated type tide gauge around of south Korea and Japan. Tf data are a large contaminated by 60-day tidal aliasing effect, very near the alias periods of M2 and S2. When this 60-day effect is removed, the data agree well with the tide gauge data with 4.6 cm averaged RMS difference. The T/P derived sea level variability reveals clearly the well-known, strong current-topography such as Kuroshio. The T/P mean sea level of North Pacific (NP) was higher than Yellow Sea (YS) and East Sea (ES). The T/P sea level variability, with strong eddy and meandering, was the largest in eastern part of Japan and this variability was mainly due to the influence of bottom topography in Kuroshio Extension area.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.49.1-49.1
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• 2010

The optical lightcurves of flare events can be regarded as a direct indicator about the existence of magnetic activity in low-mass stars. Stellar flares are generated by magnetodynamic processes in the stellar interiors as on the Sun and indicate that the locally intensified active regions still exist on the photosphere. However previous photometric observations are limited to a few selected active objects because of their faintness and randomness of the flare occurrence. Based on dedicated deep (r~23), long-term (24 night) time-series monitoring of the open cluster M37 from MMT 6.5m transit survey program, we searched for flare-like transient phenomena in the 3,052 M-dwarf lightcurves with relatively high-temporal resolution (30s-90s). In order to collect all statistical significant events, we applied the change-point analysis with filtering algorithm using local statistics. We found a number of flares from 412 M-dwarf stars that are probable cluster members. Nearly half of them have periodic brightness variations with a near or distorted sinusoidal shape. With a small exception of binary cases, most of these variations appear to reflect the presence of large starspots resulting in rotational brightness modulations. We will discuss the relationship among magnetic activity indicators and dependence on spectral type.

• Wind and Structures
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• v.24 no.4
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• pp.385-403
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• 2017

In recent years, the wind energy has played an increasingly important role in national energy sector of many countries. To harvest more electric power, the wind turbine (WT) tower structure becomes physically larger, which may cause more risks during long-term operation. Associated with the great development of WT projects, the number of accidents related to large-scaled WT has also been increased. Therefore, a structural health monitoring (SHM) system for WT structures is needed to ensure their safety and serviceability during operational time. The objective of this study is to develop a hybrid damage detection method for WT tower structures by measuring vibration and impedance responses. To achieve the objective, the following approaches are implemented. Firstly, a hybrid damage detection scheme which combines vibration-based and impedance-based methods is proposed as a sequential process in three stages. Secondly, a series of vibration and impedance tests are conducted on a lab-scaled model of the WT structure in which a set of bolt-loosening cases is simulated for the segmental joints. Finally, the feasibility of the proposed hybrid damage detection method is experimentally evaluated via its performance during the damage detection process in the tested model.

• Computers and Concrete
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• v.20 no.5
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• pp.583-593
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• 2017

Applications of fibre-reinforced polymer (FRP) composites for retrofitting, strengthening and repairing concrete structures have been expanded dramatically in the last decade. FRPs have high specific strength and stiffness compared to conventional construction materials, e.g., steel. Ease of preparation and installation, resistance to corrosion, versatile fabrication and adjustable mechanical properties are other advantages of the FRPs. However, there are major concerns about long-term performance, serviceability and durability of FRP applications in concrete structures. Therefore, structural health monitoring (SHM) and damage detection in FRP-retrofitted concrete structures need to be implemented. This paper presents a study on investigating the application of Rayleigh wave for detecting debonding defect in FRP-retrofitted concrete structures. A time-of-flight (ToF) method is proposed to determine the location of a debonding between the FRP and concrete using Rayleigh wave. A series of numerical case studies are carried out to demonstrate the capability of the proposed debonding detection method. In the numerical case studies, a three-dimensional (3D) finite element (FE) model is developed to simulate the Rayleigh wave propagation and scattering at the debonding in the FRP-retrofitted concrete structure. Absorbing layers are employed in the 3D FE model to reduce computational cost in simulating the practical size of the FRP-retrofitted structure. Different debonding sizes and locations are considered in the case studies. The results show that the proposed ToF method is able to accurately determine the location of the debonding in the FRP-retrofitted concrete structure.

• Wind and Structures
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• v.31 no.4
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• pp.363-371
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• 2020

Proper understanding of offshore wind speed variability is of essential importance in practice, which provides useful information to a wide range of coastal and marine activities. In this paper, long-term wind speed data recorded at various offshore stations are analyzed in the framework of fractal dimension analysis. Fractal analysis is a well-established data analysis tool, which is particularly suitable to determine the complexity in time series from a quantitative point of view. The fractal dimension is estimated using the conventional box-counting method. The results suggest that the wind speed data are generally fractals, which are likely to exhibit a persistent nature. The mean fractal dimension varies from 1.31 at an offshore weather station to 1.43 at an urban station, which is mainly associated with surface roughness condition. Monthly variability of fractal dimension at offshore stations is well-defined, which often possess larger values during hotter months and lower values during winter. This is partly attributed to the effect of thermal instability. In addition, with an increase in measurement interval, the mean and minimum fractal dimension decrease, whereas the maximum and coefficient of variation increase in parallel.

• Journal of The Korean Astronomical Society
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• v.39 no.4
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• pp.129-138
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• 2006

A period study of the semi-detached eclipsing binary system W Delphini based on the extensive series of minimum timings covering more than a century(109 years) indicates a cyclic(O-C) variation of the system. This variation can be explained as due either to (1) stellar magnetic activity cycles of the cool subgiant G5 secondary component of the binary with a subsurface magnetic field equals to 3 kG, or (2) a long-term orbital period increases with a rate of $1.68{\times}10^{-8}$ day/cycle caused by a mass transfer rate of $4.9{\times}10^{-8}M_{\odot}yr^{-1}$ from the less to more massive component modulated by a light time effect due to a hypothetical third body with period of $53.4{\pm}1.06$ years. The former explanation is more recommended than the later one since the obtained third body mass value($M_3=1.58\;M_{\odot}$) is quite large but it can not manifest itself observationally and also it cannot be a white dwarf. In the contrary, from the magnetic activity point of view, the obtained characteristics are in good consistent when applying Applegate(1992) mechanism. However, further precise photometric and CCD observations for minima timings with brightness determinations are needed to confirm the present solution.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.629-636
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• 2020

This study aims to identify and analyze the factors that affect foreign exchange reserves in Indonesia. We consider the variables of external debt, exchange rate, inflation, and exports as explanatory factors referring to previous studies. We apply the Autoregressive Distributed Lag approach to time-series data retrieved from the Central Bank of Indonesia (BI), the Central Bureau of Statistics (BPS), and International Monetary Funds (IMF) from January 2016 to December 2018. Our results show that foreign debt, exchange rates, inflation, and exports significantly affect the simultaneous fluctuation of foreign exchange reserves in Indonesia. Partially, foreign debt has a significant and positive effect on foreign exchange reserves. The exchange rate has a significant and negative effect on foreign exchange reserves in Indonesia. However, our findings explain that inflation does not significantly affect foreign exchange reserves in Indonesia, and exports have a significant and positive effect on foreign exchange reserves. This study is expected to be useful to policymakers in managing foreign exchange reserves, so the economy of Indonesia can grow sustainably. One of the exciting things in this study lies in the model that uses the Autoregressive Distributed Log, which can explain long-term relationships through adjusted coefficient and cointegration tests.