• Journal of Wetlands Research
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• v.23 no.2
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• pp.144-153
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• 2021

In recently, the green algae bloom is one of the most severe challenges. The seven days prior prediction is in operation to issues the water quality warning, but it also needs a longer time of prediction to take preemptive measures. The objective of the study is to establish a method to conduct a 3-month prior prediction of Chl-a concentration in the Daechong Lake and tested its applicability as a supplementary of current water quality warning. The historical record of water quality in the Daechong Lake and seasonal forecasting of ECMWF were obtained, and its time-series characteristics were analyzed. The Chl-a forecasting model was established using a correlation between Chl-a concentration and meteorological factor and NARX model, and its efficiency was compared.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.746-755
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• 2014

Compressive sensing (CS) has drawn much interest as a new sampling technique that enables signals to be sampled at a much lower than the Nyquist rate. By noting that the block-based compressive sensing can still keep spatial correlation in measurement domain, in this paper, we propose a novel encoding technique for measurement data obtained in the block-based CS of natural image. We apply discrete wavelet transform (DWT) to decorrelate CS measurements and then assign a proper quantization scheme to those DWT coefficients. Thus, redundancy of CS measurements and bitrate of system are reduced remarkably. Experimental results show improvements in rate-distortion performance by the proposed method against two existing methods of scalar quantization (SQ) and differential pulse-code modulation (DPCM). In the best case, the proposed method gains up to 4 dB, 0.9 dB, and 2.5 dB compared with the Block-based CS-Smoothed Projected Landweber plus SQ, Block-based CS-Smoothed Projected Landweber plus DPCM, and Multihypothesis Block-based CS-Smoothed Projected Landweber plus DPCM, respectively.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.19 no.2
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• pp.99-108
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• 2014

One year long time-series current data were obtained at two stations (K1 and K2) located in the Usan Trough in the area north of Ulleungdo in the East Sea from September 2006. The observed data reveal enhanced seafloor flows in both stations with variabilities of about 20 days which is possibly governed by the topographic Rossby wave. After February 2007, strong flow in the upper layer in St. K1 appears throughout the mooring period and this is due to the passage of the warm eddy comparing with satellite sea surface temperature data. During this period, no significant correlation between the current in the upper layer and those in two deep layers is shown indicating the eddy does not affect flows in the deep ocean. It is also observed that the flow direction rotates clockwise with depth in both stations except for the upper of the K1. This implies that the deep flow does not parallel to the isobaths exactly and it has a downwelling velocity component. The possibility of the flow from the Japan Basin to the Ulleung Basin across the Usan Trough is not evidenced from the data.

• Child Health Nursing Research
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• v.6 no.1
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• pp.32-50
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• 2000

A descriptive exploratory design was used in this study to evaluate the effects of sponge bathing on physiological(heart rate, heart period, vagal tone, oxygen saturation, respiration) and behavioral responses in newly born 40 preterm infants from intensive care unit of S University Hospital in Seoul. Data has been collected from October, 1997 to March, 1999. The infants were between 27-33 weeks gestational age at birth, and were free of congenital defects. The subjects entered the protocol when they were medically stable (determined by initiation of feeding and discontinuation of all respiratory support) but still receiving neonatal intensive care. The infants' physiologic parameters were recorded a 10 - minute before, during, and after bathing. Continuous heart rate data were recorded on a notebook computer from the infant's EKG monitor. The data were digitized off-line on software(developed by Lee and Chang in Wavelet program) which detected the peak of the R wave for each heart beat and quantified sequential R-R intervals in msec(i.e. heart periods). Heart period data were edited to remove movement artifact. Heart period data were quantified as : 1) mean heart period; 2) vagal tone. Vagal tone was quantitfied with a noninvasive measure developed by Porges(1985) in Mxedit software. To determine behavioral status, tools were developed by Scafidi et al(1990) were used. Collected data were analyzed with the SPSS program using paried t-test, ANOVA, and Pearson correlation. The result were as follow. 1. The results of the ANOVAs indicated that vagal tone were signifcantly lower during bathing than baseline and post-bathing. There were significant differences in heart period and heart rate levels across the bathing. But the mean oxygen saturations and respirations were no differences. Also, there were no significant differences on behavioral sign, motor activity, behavioral distress, weight changes, morbidity, and hospitalization period. 2. To evaluate the relation between vagal tone and subsequent parameters, the two groups (the high group had 19 subjects and low group had 21subjects) were divided by the mean baseline vagal tone. Vagal tone measured prior to bathing were significantly associated with respiration before bathing, vagal tone during bathing, and the magnitude of change in both vagal tone. But, other subsequent reactivities were no differences in two groups. 3. Correlations were also calculated between vagal tone and the subsequent physiological reactivities from baseline through after- bathing. Correlations were significant between baseline vagal tone and baseline heart rate, between baseline vagal tone and baseline heart period, between baseline vagal tone and oxygen saturation after bathing. In summary, the bathing in this study showed a stressful stimulus on premature infants through there was significance in the physiological parameters. In addition, our study represents the documentation that vagal tone reactivity in response to clearly defined external stimulation provides an index of infant's status.