• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.673-680
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• 2012

Long-term GPS data analysis was performed in order to analyze the seasonal variation of tropospheric delay and the success rate of the ambiguity resolution. For this analysis, a total of 57 stations including 10 IGS stations in East Asia were processed together with double-differenced observables using Bernese GPS Software V5.0. The time span for this study ranges from 2002.0 to 2012.5 (10.5 years). The average baseline length is 339.0 km and the maximum reaches up to 2,000 km. The analysis is focused on two things: the annual variation of the tropospheric delay and the ambiguity resolution rate. The tropospheric delay is closely related to the weather condition, especially relative humidity, therefore it was estimated that the maximum would be in summer, while reaching its minimum in winter with the apparent seasonal variations. On the contrary, however, the success rate of the ambiguity resolution shows the opposite pattern: its maximum was in winter and minimum in summer. The fact seems to be induced by the surrounding conditions; that is, the trees thick with leaves near the GPS antenna interfere with GPS signals in summer. This seems to confirm partly that there is a distinct trend in the decreasing success rate since 2006 because the trees are growing every year. It is necessary to eliminate the factors that degrade the GPS quality and the tropospheric modeling for Korea needs to be studied further.

• Journal of Daesoon Thought and the Religions of East Asia
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• v.2 no.1
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• pp.57-83
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• 2022

Numbers structure reality and define the way people live. Both in Daoism and in Daesoon Jinrihoe they signify key concepts, notably the cardinal numbers from one through nine that classify different dimensions of the cosmos. Beyond these, the number twenty-four plays an important role. In a temporal mode, it marks the divisions or seasonal periods of the year. Consisting of fifteen days each, these periods signal (and are named after) changes in dominant weather patterns and the position of the sun. Generally activated in the body through particular seasonal activities and dietary prescriptions, in Daoism they are also the root of a series of healing exercises and certain refinement practices of internal alchemy. In Daesoon Jinrihoe, moreover, they are activated by chanting a specific incantation that invokes the twenty-four divine rulers of the divisions, originally a group of Tang Dynasty officials that in nature and function resemble the spirit generals of the early Celestial Masters. Beyond this, the number twenty-four also applies to space. Not unlike the twenty-eight lunar stations or mansions, traditional cosmology acknowledges twenty-four directions, made up of six constellations each in the four cardinal directions, complete with starry deities and divine generals. Their powers are activated with the help of written characters rather than vocal incantations, using techniques common both in Daoism and Daesoon Jinrihoe.

• Ocean Science Journal
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• v.43 no.4
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• pp.195-208
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• 2008

This paper evaluates whether a thermodynamic ocean-carbon model can be used to predict the monthly mean global fields of the surface-water partial pressure of $CO_2$ ($pCO_{2SEA}$) from sea surface salinity (SSS), temperature (SST), and/or nitrate ($NO_3$) concentration using previously published regional total inorganic carbon ($C_T$) and total alkalinity ($A_T$) algorithms. The obtained $pCO_{2SEA}$ values and their amplitudes of seasonal variability are in good agreement with multi-year observations undertaken at the sites of the Bermuda Atlantic Timeseries Study (BATS) ($31^{\circ}50'N$, $60^{\circ}10'W$) and the Hawaiian Ocean Time-series (HOT) ($22^{\circ}45'N$, $158^{\circ}00'W$). By contrast, the empirical models predicted $C_T$ less accurately at the Kyodo western North Pacific Ocean Time-series (KNOT) site ($44^{\circ}N$, $155^{\circ}E$) than at the BATS and HOT sites, resulting in greater uncertainties in $pCO_{2SEA}$ predictions. Our analysis indicates that the previously published empirical $C_T$ and $A_T$ models provide reasonable predictions of seasonal variations in surface-water $pCO_{2SEA}$ within the (sub) tropical oceans based on changes in SSS and SST; however, in high-latitude oceans where ocean biology affects $C_T$ to a significant degree, improved $C_T$ algorithms are required to capture the full biological effect on $C_T$ with greater accuracy and in turn improve the accuracy of predictions of $pCO_{2SEA}$.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• Ocean and Polar Research
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• v.33 no.spc3
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• pp.385-395
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• 2011

Time-series sediment traps were deployed at 1,000 m water depth of the northwestern subtropical Pacific from July 2009 to June 2010, with the aim of understanding temporal and spatial variations of sinking-particle fluxes. The opening and closing of the traps was synchronized at 18-day periods for 20 events. Total mass fluxes showed distinct seasonal variations with high values for the summer-fall seasons and relatively low values for winter-spring. This seasonal variation at two stations was characterized by a distinct difference in $CaCO_3$ fluxes between the two seasons. The enhanced $CaCO_3$ flux in the summer - fall seasons might be attributed to an increased planktonic foraminiferal flux. Total mass flux at FM10 station was nearly 50% higher than that at FM1 station. The difference in $CaCO_3$ fluxes between two stations contributed nearly 70% of the difference of total mass fluxes. The $CaCO_3$ flux was a major component controlling temporal and spatial variation of sinking - particle fluxes in the western subtropical Pacific Ocean.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.933-951
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• 2009

Climate change of global warming may affect the water circulation in Korea. Rainfall is occurred with complex of multiple climatic indices. Therefore, the rainfall is one of the most significant index due to climate change in the process of water circulation. In this research, multiple time series data of rainfall events were extracted to represent the rainfall characteristics. In addition, the occurrence of rainfall time series analyzed by annual, seasonal and monthly data. Analysis method used change analysis of mean and standard deviation and trend analysis. Also, changes in rainfall characteristics and the relative error was calculated during the last 10 years for comparison with past data. At the results, significant statistical results weren't showed by randomness of rainfall data. However, amount of rainfall generally increased last 10 years, and number of raining days had trend of decrease. In addition, seasonal and monthly changes in the rainfall characteristics can be found to appear differently.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.331-343
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• 2015

We propose a distributed medium access control protocol for cognitive radio networks to opportunistically utilize multiple channels. Under the proposed protocol, cognitive radio nodes forecast and rank channel availability observing primary users' activities on the channels for a period of time by time series analyzing using smoothing models for seasonal data by Winters' method. The proposed approach protects primary users, mitigates channel access delay, and increases network performance. We analyze the optimal time to sense channels to avoid conflict with the primary users. We simulate and compare the proposed protocol with the existing protocol. The results show that the proposed approach utilizes channels more efficiently.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.488-488
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• 2015

Intermittent streamflow is common phenomenon in arid and semi-arid regions. To manage water resources of intermittent streamflows, stochactic simulation data is essential; however the seasonally stochastic modeling for intermittent streamflow is a difficult task. In this study, using the periodic Markov chain model, we simulate intermittent monthly streamflow for occurrence and the periodic gamma autoregressive and copula models for amount. The copula models were tested in a previous study for the simulation of yearly streamflow, resulting in successful replication of the key and operational statistics of historical data; however, the copula models have never been tested on a monthly time scale. The intermittent models were applied to the Colorado River system in the present study. A few drawbacks of the PGAR model were identified, such as significant underestimation of minimum values on an aggregated yearly time scale and restrictions of the parameter boundaries. Conversely, the copula models do not present such drawbacks but show feasible reproduction of key and operational statistics. We concluded that the periodic Markov chain based the copula models is a practicable method to simulate intermittent monthly streamflow time series.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1511-1523
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• 2016

The increasing trend of the five violent crimes (murder, robbery, rape, violence, theft) in Korea is not independent of social and economic factors. Several social science research have discussed about this issue but most of them do not properly reflect the nature of the time-series data. Based on several time series models, we studied about the endogenous factors (time, seasonal and cycle factors) and exogenous factors (economical, social change and crime control factors) on violent crime occur in Korea. Autocorrelation were also taken into account. Through this study, we want to help to make preventive policy by explaining the cause of violent crime and predicting the future incidence of it.