• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2277-2284
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• 2013

In this study, a groundwater table fluctuation method is suggested to predict groundwater level by means of groundwater table fluctuation due to recharge and discharge under unsteady condition. This model analyzes groundwater variation characteristics by using reaction factor related with groundwater flow and specific yield related with recharge. For the test of this model, measured groundwater level at JD Yongdam 1 and JW Konghang for 5 years (2006-2010) were used. At JD Yongdam 1, the estimated specific yield was 0.023, and the estimated reaction factor was 0.039. At JW Konghang, the estimated specific yield was 0.009 and the estimated reaction factor was 0.028, respectively. This model can estimate recharge and saturated parameters, thus it is expected that this model would be the proper tool for checking the parameter of hydrologic model and percolation features.

• Journal of the Korean Ceramic Society
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• v.47 no.2
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• pp.199-204
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• 2010

A dual-phase ceramic membrane consisting of gadolinium-doped ceria (GDC) as an oxygen ion conducting phase and $MnCo_2O_4$ as an electron conducting phase was fabricated by sintering a GDC and $MnCo_2O_4$ powder mixture. The $MnCo_2O_4$ was found to maintain its spinel structure at temperatures lower than $1200^{\circ}C$. (Mn,Co)(Mn,Co)$O_4$ spinel, manganese and cobalt oxides formed in the sample sintered at $1300^{\circ}C$ in an air atmosphere. XRD analysis revealed that no reaction phases occurred between GDC and $MnCo_2O_4$ at $1200^{\circ}C$. The electrical conductivity did not exhibit a linear relationship with the $MnCo_2O_4$ content in the composite membranes, in accordance with percolation theory. It increased when more than 15 vol% of $MnCo_2O_4$ was added. The oxygen permeation fluxes of the composite membranes increased with increasing $MnCo_2O_4$ content and this can be explained by the increase in electrical conductivity. However, the oxygen permeation flux of the composite membranes appeared to be governed not only by electrical conductivity, but also by the microstructure, such as the grain size of the GDC matrix.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.5
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• pp.314-320
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• 2021

Controlling ambient humid condition through high performance humidity sensors has become important for various fields, including industrial process, food storage, and the preservation of historic remains. Although aerosol deposited humidity sensors using ceramic BaTiO₃ (BT) material have been widely studied because of their longtime stability, there remain critical disadvantages, such as low sensitivity, low linearity, and slow response/recovery time in case of the sensors fabricated at room temperature. To achieve superior humidity sensing properties even at room temperature condition, BT-Cu composite films utilizing aerosol deposition (AD) process have been proposed based on the percolation theory. The BT-Cu composite films showed gradually improved sensing properties until the Cu concentration reached 15 wt% in the composite film. However, the excessive Cu (above 30 wt%) containing BT-Cu composite films showed a rapid decrease of the sensing properties. The results of observed surface morphology of the AD fabricated composite films, to figure out the metal filler effect, showed correlation between surface topography as well as size and the amount of open pores according to the metal filler content. Overall, it is very important not only dielectric constant of the humidity sensing films but also microstructures, because they affect either the variation range of capacitance by ambient humidity or adsorption/desorption of ambient humidity onto/from the humidity sensing films.

• 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.