• 정보과학회 논문지
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• 제41권12호
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• pp.1066-1074
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• 2014

본 논문에서는 시공간 정보를 포함하는 트윗 스트림에서 비정상적인 이벤트에 대한 상관관계를 사용자에게 시각적으로 분석하는 방법을 다양한 실험을 통하여 제안한다. 제안하는 방법으로는 트윗에서 토픽 모델링을 수행한 다음 계절요인과 추세요인을 반영한 시계열 분석 기법을 이용하여 비정상적인 이벤트 후보군을 추출한다. 추출된 토픽이 포함되어 있는 데이터를 대상으로 다시 한 번 토픽을 추출하여 시계열 분석을 수행한 다음 앞서 추출한 토픽과의 상관관계를 분석하여 비정상적인 이벤트를 탐지할 수 있도록 하였다. 비정상 이벤트를 탐지하는 모든 과정에 시각적 분석 방법을 이용하여 단순한 수치 정보가 아닌 시각적 패턴 형태로 나타냄으로써 사용자는 직관적으로 비정상 이벤트의 동향과 주기적인 패턴을 분석할 수 있도록 하였다. 실험은 2014년 1월 1일부터 2014년 6월 30일까지 국내에서 발생한 트윗을 대상으로 2개의 사건[경주 마우나 리조트 붕괴 사건(2014.02.17.), 진도 여객선 침몰 사건(2014.04.16.)]에 대해 시각적 분석 시스템을 적용하여 사용자는 쉽게 데이터를 분석하고 이해할 수 있음을 보였다.

• 응용통계연구
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• 제19권2호
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• pp.319-330
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• 2006

현실세계에서 관찰되는 시그널(signal)은 다양한 주파수(frequency)들의 시그널로 혼합되어 있는 경우가 많다. 예를 들어 태양 흑점 자료의 경우 약 11년 주기와 85년 주기로 변동한다는 사실은 널리 알려져 있다. 또한 경제 시계열 자료의 경우는 통상적으로 계절요인(seasonal component), 순환요인(cyclic component) 그리고 장기적인 추세요인(long-term trend)으로 분해하여 분석한다. 이러한 시계열 자료를 구성요소별로 분해하는 것은 오래된 주제중 하나이다. 전통적인 시계열자료 분석기법으로 스펙트럴 분석기법 등이 널리 사용되고 있으나 시계열 자료들이 비정상(nonstationary)일 경우에는 적용하기 어렵다. Huang et. al(1998)은 경험적 모드분해법(empirical mode decomposition)이라고 하는 자료적응적인(data-adaptive) 방법을 제안하였는데, 비정상성(nonstationarity)에 대한 강건성(robustness)으로 여러 분야에 널리 응용되고 있다. 그러나 Huang et. at(1998)은 잡음(error)에 의해 오염된 자료에 대한 구체적인 처리방법은 제시하지 못하고 있다. 본 논문을 통하여 효율적인 잡음제거 방법을 제안하고자 한다.

• 한국산림과학회지
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• 제98권2호
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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.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2001년도 추계학술대회 논문집
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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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• 제29권1호
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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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• 제12권6호
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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.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2010년도 한국우주과학회보 제19권1호
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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.

• 한국농림기상학회지
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• 제7권1호
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• pp.57-65
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• 2005

토양은 리터의 축적, 분해, 뿌리호흡을 포함하는 토양호흡을 통해 많은 양의 탄소를 저장 또는 방출할 수 있다. 이러한 토양의 주요한 탄소 및 양분의 공급원은 낙엽낙지의 유입과 유입된 낙엽낙지의 분해이며 생태계로 돌아가는 영양의 약 60%가 낙엽에서 유래되는 것으로 알려져 있다. 본 연구의 목적은 천연자연림으로 잘 보존된 경기도 광릉시험림의 온대낙엽활엽수림의 리터 생산량과 분해속도가 토양호흡의 계절적 변 이에 미치는 영향을 파악하고 낙엽층과 토양호흡간의 관련성을 찾아 생태계에서의 탄소순환을 이해하기 위한 것이다. 광릉 낙엽활엽수림에서 2003년 생산된 리터의 총량은 1489 Cm/sup -2/ yr/sup -1/ 이었으며 조사지의 우점종인 졸참나무, 서어나무, 까치박달의 순수 낙엽의 총량은 1189 Cm/sup -2/ yr/sup -1/이었다. 낙엽의 분해율은 조사기간인 1년 동안 졸참나무는 24.2%(k = 0.28), 서어나 무는 25.7％(k = 0.30), 까치박달은 33.0%(k = 0.46)이었 다. AOCC를 이용한 연속적인 토양호흡 측정결과 연간 토양호흡량은 629.69 Cm/sup -2/ yr/sup -1/이었으며 이 중 리터 분해가 차지하는 비율은 약 5％인 309 Cm/sup -2/ yr/sup -1/이었다. 토양호흡의 동절기 최저값은 7.4±1.4g Cm/sup -2/ month/sup -1/ 이었으며 이 시기의 리터 분해속도도 0.8g Cm/sup -2/ month/sup -1/로 최저값을 나타내었다. 하절기에는 토양 호흡과 리터 분해속도 모두 증가하여 111.5 ± 16.2g Cm/sup -2/ month/sup -1/와 11.4g Cm/sup -2/ month/sup -1/로 최고값을 보였다. 토양호흡 중 리터 분해속도가 차지하는 비율은 식물 지하부의 생장이 활발해지는 5-6월에는 4.3%로 감소하였으며 리터의 생산량이 증가하는 11- 12월에는 23.5%로 증가하였다. 회귀분석결과 리터 분해속도와 토양호흡과는 r²= 0.63의 상관관계를 가지고 있었다.

• 대한산업공학회지
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• 제28권1호
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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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• 제54권10호
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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.