• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• 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.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.183-188
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• 2009

It is necessary to determine the amount of carbon dioxide ($CO_2$) absorbed by plants and released from forest floor into atmosphere, to gain a better understanding how forests participate in the global carbon cycle. Soil $CO_2$ efflux, litter production, and decomposition were investigated in Q. variabilis and P. densiflora stands in the vicinity of Gwangju, Chonnam province. Soil $CO_2$ efflux was measured using Infrared Gas Analyzer (IRGA) at midday of the 10th day at every month over 12-month period, to quantify seasonal and annual budgets of soil $CO_2$ efflux. Soil temperature and soil moisture were measured at the same time. Seasonal soil $CO_2$ efflux in Q. variabilis and P. densiflora were the highest in summer season. In August, maximum soil $CO_2$ efflux in Q. variabilis and P. densiflora was 7.49, $4.61CO_2{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, respectively. Annual $CO_2$ efflux in each stand was 1.77, $1.67CO_2kg{\cdot}m^{-2}$ respectively. Soil $CO_2$ efflux increased exponentially with soil temperature and related strongly in Q. variabilis ($r^2$=0.96), and in P. densiflora ($r^2$=0.91). Litter production continued throughout the year, but showed a peak on November and December. Annual litter production in the Q. variabilis and P. densiflora stands were $613.7gdw{\cdot}m^{-2}{\cdot}yr^{-1}$ and $550.5gdw{\cdot}m^{-2}{\cdot}yr^{-1}$.$yr^{-1}$, respectively. After 1 year, % remaining mass of Q. variabilis and P. densiflora litter was 48.2, 57.1%, respectively. The soil $CO_2$ efflux rates in this study showed clear seasonal variations. In addition, the temporal variation in the $CO_2$ efflux rates was closely related to the soil temperature fluctuation rather than to variations in the soil moisture content. The range of fluctuation of soil $CO_2$ efflux and litter decomposition rate showed similar seasonal changes. The range of fluctuation of soil $CO_2$ efflux and litter decomposition rate was higher during summer and autumn than spring and winter.

• Proceedings of the KSR Conference
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• 2001.10a
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• pp.111-116
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• 2001

This paper introduces how to adopt the X-12-ARIMA to decompose the railway passenger demand of the Korea National Railroad Especially, selecting on proper filters is focused. The trend filter is identical to the low pass filter in the signal Processing field, and so the seasonal filter is to band pass filter too. Some considerations, selecting a filter, are provided from the view-point of the spectrum analysis. The technique introduced in this paper will be adopted to the project that is to develope the forecasting system of Korea railway passenger demand which is a part of the high speed rail information system.

• Journal of Forest and Environmental Science
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• v.29 no.1
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• pp.38-48
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• 2013

Decomposition of plant material is an important component in the study of forest ecosystem because of its critical role in nutrient cycling. Different tree species has different nutrient release patterns, which are related to leaf litter quantitative traits and seasonal environmental factors. The quantitative traits of leaf litter are important predictors of decomposition and decomposition rates increase with greater nutrient availability in the forest ecosystems. At the ecosystem level, litter quantitative traits are most often related to the physical and chemical characteristics of the litter, for example, leaf toughness and leaf mass per unit area, and lignin content tannin and total phenolics. Thus, the analysis of litter quantitative traits and decomposition are highly important for the understanding of nutrient cycling in forest ecosystems. By studying the role of litter quantitative traits on decomposition and nutrient cycling in forest ecosystems will provide a valuable insight to how quantitative traits influence ecosystem nutrient dynamics. Such knowledge will contribute to future forest management and conservation practices.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Bulletin of the Korean Space Science Society
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• 2010.04a
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• pp.34.2-34.2
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• 2010

We built 7 potential extra-terrestrial planets including the full 3D Earth model with various surface types and 6 planet models, each with uniform surface characteristics. The surface types include ice, tundra, forest, grass, ground and ocean. We then imported these 7 planets into integrated ray tracing(IRT) model to compute their disk averaged spectra and to understand the spectral behavior depending on the geometrical view, illumination phase and seasonal change. The IRT computation show that the 6 planets with uniform surfaces exhibit clear spectral differences from that of the Earth. We then built a phase and seasonal DAS database for the 6 uniform surface planets and used them for parametric spectral decomposition technique to derive the Earth DAS. This computation resulted in the first potential solution to the surface type ratio of the Earth compared to the measured earth surface type ratio. The computational details and the implications are discussed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.1
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• pp.57-65
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• 2005

In a forest ecosystem, the major source of soil carbon input is from litterfall and its decomposition. To understand the effect of litterfall and litter decomposition on seasonal variation of soil respiration and litter decomposition rates were measured in temperate deciduous forest in Korea. Annual litterfall collected from litter trap (1m x 1m) were 147.5 ± 8.2g Cm/sup -2/ yr/sup -1/ in 2003. About 47% of litterfall were Quercus serrata leaf followed by Carpinus laxiflora leaf (27 %), Carpinus cordata leaf (7 %), and others, such as other leaf, bark, branch, and acorn, were 20%. The decomposition rate was the highest in C. cordata (33.03%, k = 0.46), followed by C. laxiflora (25.73%, k = 0.30), and Q. serrata (24.17%, k = 0.28). The continuous measurement of soil respiration from January 2004 to December 2004 was carried out using AOCC (Automatic Open-Closed multi-Chamber system). The annual soil respiration rate was 629.6g Cm/sup -2/ yr/sup -1/ and the litter decomposition was 30.0g Cm/sup -2/ yr/sup -1/. The portion of litter decomposition rate on soil respiration rate was about 5%. From January to February, when the soil respiration rate was the lowest, about 11 % of soil respiration (7.4 ± l.4g Cm/sup -2/ month/sup -1/) were effected by litter decomposition rate (0.8g Cm/sup -2/ month/sup -1/). The highest soil respiration rate (111.5 ± 16.2g Cm/sup -2/ month/sup -1/) and litter decomposition rate (11.4g Cm/sup -2/ month/sup -1/) were showed in July to August. According to the regression analysis between soil respiration rate and litter decomposition, the soil respiration rate were related to litter decomposition with the correlations (r = 0.63).

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.63-75
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• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3878-3887
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• 2022

For the future energy-mix policy for carbon neutrality, demand for the capability of load-follow operation has emerged in nuclear power plants in order to accommodate the intermittency of renewable energy. The short-term decay heat analysis is also required to evaluate the decay heat level varied by the power level change during the load-follow operation, which is a very important parameter in terms of short-term decay heat removal during a grace time. In this study, the short-term decay heat level for 10 days after the shutdown was evaluated for both seasonal and daily load-follow cases. Additionally, the nuclide-wise contribution to the accumulated decay heat for 10 days was analyzed for further understanding of the short-term decay heat behavior. The result showed that in the seasonal case, the decay heat level was mainly determined by the power level right before the shutdown and the amount of each nuclide was varied with the power variation due to the long variation interval of 90 days. Whereas, in the daily case, the decay heat level was strongly impacted by the average power level during operation and meaningful mass variations for those nuclides were not observed due to the short variation interval of 0.5 days.