• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.1
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• pp.82-90
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• 2012

Four different buildings having various wall construction are analyzed for the effect of wall mass on the thermal performance and inside building air and wall temperature transient and also for calculating the energy consumption load. This analytical study was motivated by the experimental work of Burch et al. An analytical solution of one-dimensional, linear, partial differential equations is obtained using the Laplace transform method, Bromwich and modified Bromwich contour method. A simple dynamic model using steady state analysis as simplified methods is developed and results of energy consumption loads are compared with results obtained using the analytical solution. Typical Meteorological Year data are processed to yield hourly average monthly values. This study is conducted using weather data from two different locations in Korea: Daegu having severe weather in summer and winter and Jeju having mild weather almost all year round. There is a significant wall mass effect on the thermal performance of a building in mild weather condition. Buildings of heavyweight construction with insulation show the highest comfort level in mild weather condition. A proportional controller provides the higher comfort level in comparison with buildings using on-off controller. The steady state analysis gives an accurate estimate of energy load for all types of construction. Finally, it appears that both mass and wall insulation are important factors in the thermal performance of buildings, but their relative merits should be decided in each building by a strict analysis of the building layout, weather conditions and site condition.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.131-136
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• 1994

In the present study, in order to make an in vitro model of gastric mucosa for physiological and pharmacological studies, we established two immortalized gastric surface mucous cell lines (GSM06 and GSM10), which produce periodic acid-Schiff (PAS)-and concanavalin A (Con A)-positive glycoproteins, from a primary culture of gastric fundic mucosal cells of adult transgenic mice harboring a temperature-sensitive simian virus 40 large T-antigen gene 〔1]. Gastric fundic mucosal cells were isolated as a modification of a previously described method for rats by Schepp et al. (2). The isolated gastric fundic mucosal cells were cultured in DME/F12 medium supplemented with 2% fetal bovine serum (FBS), 1% ITES (consisting of 2 mg/1 insulin, 2 mgg/1 transferrin, 0.122 mg/1 ethanolamine and 0.00914 mg/1 sodium selenite) and 10 ng/ml recombinant epidermal growth factor (EGF) in a collagen-coated culture dish. To remove fibroblastic cells from the culture, gastric mucosal cells were incubated in the culture medium containing dispase (25 U/ml) for 24 h. The cells, uncontaminated with fibroblastic cells, were then cloned by colony formation. In our series of three attempts, two cell lines (GSM06 and GSM10) have been established at last. The cells proliferated, attached to the dish ana grew until confluent monolayers were formed, and maintained tight contact with neighboring cells. Both GSM06 and GSM10 cells have now been in culture for more than 9 months with regular passaging. The either cell produced

• Journal of Microbiology and Biotechnology
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• v.16 no.7
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• pp.1132-1138
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• 2006

Three types of xylanases (EC 3.2.1.8) were detected in the strain Aspergillus niger A-25, one of which, designated as XynIII, also displayed ${\beta}-(l,3-1,4)-glucanase$ (EC 3.2.1.73) activity, as determined by a zymogram analysis. XynIII was purified by ultrafiltration and ion-exchange chromatography methods. Its apparent molecular weight was about 27.9 kDa, as estimated by SDS-PAGE. The purified XynIII could hydrolyze birchwood xylan, oat spelt xylan, lichenin, and barley ${\beta}-glucan$, but not CMC, avicel cellulose, or soluble starch under the assay conditions in this study. The xylanase and ${\beta}-(l,3-1,4)-glucanase$ activities of XynIII both had a similar optimal pH and pH stability, as well as a similar optimal temperature and temperature stability. Moreover, the effects of metal ions on the two enzymatic activities were also similar. The overall hydrolytic rates of XynIII in different mixtures of xylan and lichenin coincided with those calculated using the Michaelis-Menten model when assuming the two substrates were competing for the same active site in the enzyme. Accordingly, the results indicated that XynIII is a novel bifunctional enzyme and its xylanase and ${\beta}-(l,3-1,4)-glucanase$ activities are catalyzed by the same active center.

• Journal of Ecology and Environment
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• v.33 no.2
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• pp.165-173
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• 2010

Soil respiration ($R_S$) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in $R_S$. The principal objectives of this study were to evaluate the relationship between $R_S$ rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of $R_S$ using ST and SWC. We have been measuring $R_S$, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of $R_S$, we compared a simple exponential regression (flux = $ae^{bt}$Eq. [1]) and two polynomial multiple-regression models (flux = $ae^{bt}{\times}({\theta}{\nu}-c){\times}(d-{\theta}{\nu})^f:$ Eq. [2] and flux = $ae^{bt}{\times}(1-(1-({\theta}{\nu}/c))^2)$: Eq. [3]) that included two variables (ST: t and SWC: ${\theta}{\nu}$) and that utilized hourly data for $R_S$. In general, daily mean $R_S$ rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and $R_S$ rates on periods of a day based on the hourly $R_S$ data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST $\times$ SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the $R^2$ from 0.71 to 0.78.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.459-466
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• 2006

Numerical values of thermodynamic properties such as temperature, pressure, dryness, volume, enthalpy and entropy are required in numerical analysis on evaluating the thermal performance. But the steam table itself cannot be used without modelling. From this point of view the neural network with function approximation characteristics can be an alternative. the multi-layer neural networks were made for saturated vapor region and superheated vapor region separately. For saturated vapor region the neural network consists of one input layer with 1 node, two hidden layers with 10 and 20 nodes each and one output layer with 7 nodes. For superheated vapor region it consists of one input layer with 2 nodes, two hidden layers with 15 and 25 nodes each and one output layer with 3 nodes. The proposed model gives very successful results with ${\pm}0.005%$ of percentage error for temperature, enthalpy and entropy and ${\pm}0.025%$ for pressure and specific volume. From these successful results, it is confirmed that the neural networks could be powerful method in function approximation of the steam table.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.9
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• pp.808-812
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• 2006

In this dissertation, Ru-Zr metal gate electrode deposited on two kinds of dielectric were formed for MOS capacitor. Sample co-sputtering method was used as a alloy deposition method. Various atomic composition was achieved when metal film was deposited by controlling sputtering power. To study the characteristics of metal gate electrode, C-V(capacitance-voltage) and I-V(current-voltage) measurements were performed. Work function and equivalent oxide thickness were extracted from C-V curves by using NCSU(North Carolina State University) quantum model. After the annealing at various temperature, thermal/chemical stability was verified by measuring the variation of effective oxide thickness and work function. This dissertation verified that Ru-Zr gate electrodes deposited on $SiO_{2}\;and\;ZrO_{2}$ have compatible work functions for NMOS at the specified atomic composition and this metal alloys are thermally stable. Ru-Zr metal gate electrode deposited on $SiO_{2}\;and\;ZrO_{2}$ exhibit low sheet resistance and this values were varied with temperature. Metal alloy deposited on two kinds of dielectric proposed in this dissertation will be used in company with high-k dielectric replacing polysilicon and will lead improvement of CMOS properties.

• Journal of Environmental Science International
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• v.1 no.1
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• pp.47-51
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• 1992

Volatile organic rompounds(VOCs) present in the VOCs-contaminated water are released to air while showering and their air concentrations depend on the shower parameters, resulting in the variation of the VOCs breath concentration. The present study evaluated the key shower parameters(water temperature and inhalation duration) that affect the inhalation exposure to air chloroform while showering, by determining chloroform breath concentration. The chloroform breath concentrations increased with water temperature and inhalation duration increase. The two inhalation exposure conditions which resulted in the greatest chloroform breath contentration difference were a 5 min-inhalation exposure with warm water and a 15 min-inhalation exposure with hot water. The chloroform breath concentration was almost three times higher after later exposure. The mathematical model analyzing the relationship between two key shower parameters and breath concentration normalized to water concentration fits quite Ivell with the experimental data at a probability of p : 0.0001.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.677-680
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• 2005

In numerical analysis on the thermal performance of the heat exchanger with phase change fluids, the numerical values of thermodynamic properties are needed. But the steam table should be modeled properly as the direct use of thermodynamic properties of the steam table is impossible. In this study the function approximation characteristics of neural networks was used in modeling the saturated vapor region of refrigerant R12. The neural network consists of one input layer with one node, two hidden layers with 10 and 20 nodes each and one output layer with 7 nodes. Input can be both saturation temperature and saturation pressure and two cases were examined. The proposed model gives percentage error of ${\pm}$0.005% for enthalpy and entropy, ${\pm}$0.02% for specific volume and ${\pm}$0.02% for saturation pressure and saturation temperature except several points. From this results neural network could be a powerful method in function approximation of saturated vapor region of R12.