• Applied Chemistry for Engineering
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• v.26 no.4
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• pp.495-504
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• 2015

This work investigated the characteristics of a packed-bed plasma reactor system and the performances of the plasma reactors connected in series or in parallel for the decomposition of ethylene. Before the discharge ignition, the effective capacitance of the ${\gamma}$-alumina packed-bed plasma reactor was larger than that of the reactor without any packing, but after the ignition the effective capacitance was similar to each other, regardless of the packing. The energy of electrons created by plasma depends mainly on the electric field intensity, and was not significantly affected by the gas composition in the range of 0~20% (v/v) oxygen (nitrogen : 80~100% (v/v)). Among the various reactive species generated by plasma, ground-state atomic oxygen and ozone are understood to be primarily involved in oxidation reactions, and as the electric field intensity increases, the amount of ground-state atomic oxygen relatively decreases while that of nitrogen atom increases. Even though there are many parameters affecting the performance of the plasma reactor such as a voltage, discharge power, gas flow rate and residence time, all parameters can be integrated into a single parameter, namely, specific input energy (SIE). It was experimentally confirmed that the performances of the plasma reactors connected in series or in parallel could be treated as a function of SIE alone, which simplifies the scale-up design procedure. Besides, the ethylene decomposition results can be predicted by the calculation using the rate constant expressed as a function of SIE.

• Fire Science and Engineering
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• v.31 no.1
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• pp.26-35
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• 2017

Since the Northridge earthquake (1994) and Kobe earthquake (1995), the concept of performance-based design has been actively introduced to design major structures and buildings. Recently, the seismic design code was established for fire protection facilities. Therefore, the important fire protection facilities should be designed and constructed according to the seismic design code. Accordingly, uniform hazard spectra (UHS), with annual exceedance probabilities, corresponding to the performance level, such as operational, immediate occupancy, life safety, and collapse prevention, are required for performance-based design. Using the method of probabilistic seismic hazard analysis (PSHA), the uniform hazard spectra for 5 major cities in Korea with a recurrence period of 500, 1,000, and 2,500 years corresponding to frequencies of (0.5, 1.0, 2.0, 5.0, 10.0)Hz and PGA, were analyzed. The expert panel was comprised of 10 members in seismology and tectonics. The ground motion prediction equations and several seismo tectonic models suggested by 10 expert panel members in seismology and tectonics were used as the input data for uniform hazard spectrum analysis. According to sensitivity analysis, the parameter of spectral ground motion prediction equations has a greater impact on the seismic hazard than seismotectonic models. The resulting uniform hazard spectra showed maximum values of the seismic hazard at a frequency of 10Hz and also showed the shape characteristics, which are similar to previous studies and related technical guides for nuclear facilities.

• Korean Chemical Engineering Research
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• v.56 no.4
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• pp.568-576
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• 2018

In order to investigate the adsorption characteristics of the antibiotics trimethoprim (TMP) by activated carbon (WCAC) prepared from waste citrus peel, the effects of operating parameters on the TMP adsorption were investigated by using a response surface methodology (RSM). Batch experiments were carried out according to a four-factor Box-Behnken experimental design with four input parameters : concentration ($X_1$: 50-150 mg/L), pH ($X_2$: 4-10), temperature ($X_3$: 293-323 K), adsorbent dose ($X_4$: 0.05-0.15 g). The experimental data were fitted to a second-order polynomial equation by the multiple regression analysis and examined using statistical methods. The significance of the independent variables and their interactions was assessed by ANOVA and t-test statistical techniques. Statistical results showed that concentration of TMP was the most effective parameter in comparison with others. The adsorption process can be well described by the pseudo-second order kinetic model. The experimental data of isotherm followed the Langmuir isotherm model. The maximum adsorption amount of TMP by WCAC calculated from the Langmuir isotherm model was 144.9 mg/g at 293 K.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• Economic and Environmental Geology
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• v.39 no.3 s.178
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• pp.255-268
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• 2006

Recent 22-year (1981-2002) meteorological data of 58 Korea Meteorological Adminstration (KMA) station were analyzed to investigate spatial and temporal variation of surface air temperature (SAT) and ground surface temperature (GST) in Korea. Based on the KMA data, multiple linear regression (MLR) models, having two regression variables of latitude and altitude, were presented to predict mean surface air temperature (MSAT) and mean ground surface temperature (MGST). Both models showed a high accuracy of prediction with $R^2$ values of 0.92 and 0.94, respectively. The prediction of MGST is particularly important in the areas of geothermal energy utilization, since it is a critical parameter of input for designing the ground source heat pump system. Thus, due to a good performance of the MGST regression model, it is expected that the model can be a useful tool for preliminary evaluation of MGST in the area of interest with no reliable data. By a simple linear regression, temporal variation of SAT was analyzed to examine long-term increase of SAT due to the global warming and the urbanization effect. All of the KMA stations except one showed an increasing trend of SAT with a range between 0.005 and $0.088^{\circ}C/yr$ and a mean of $0.043^{\circ}C/yr$. In terms of meteorological factors controlling variation of GST, the effects of solar radiation, terrestrial radiation, precipitation, and snow cover were also discussed based on quantitative and qualitative analysis of the meteorological data.

• Applied Chemistry for Engineering
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• v.26 no.2
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• pp.205-209
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• 2015

CCU (Carbon Capture & Utilization) has a potential technology for the reduction and usage of carbon dioxide which is greenhouse gas emitting from a fossil fuel buring. To decompose the carbon dioxide, a three phase gliding arc plasma-catalytic reactor was designed and manufactured. Experiments of carbon dioxide reduction was performed by varying the gas flow rate with feeding the $CO_2$ only as well as the input power, the catalyst type and steam supply with respect to the injection of the mixture of $CO_2$ and $CH_4$. The $CO_2$ decomposition rate was 7.9% and the energy efficiency was $0.0013L/min{\cdot}W$ at a $CO_2$ flow rate of 12 L/min only. Carbon monoxide and oxygen was generated in accordance with the destruction of carbon dioxide. When the injection ratio of $CH_4/CO_2$ reached 1.29, the $CO_2$ destruction and $CH_4$ conversion rates were 37.8% and 56.6% respectively at a power supply of 0.76 kW. During the installation of $NiO/Al_2O_3$ catalyst bed, the $CO_2$ destruction and $CH_4$ conversion rates were 11.5% and 9.9% respectively. The steam supply parameter do not have any significant effects on the carbon dioxide decomposition.

• The Korean Journal of Physiology
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• v.28 no.2
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• pp.133-141
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• 1994

The discharge patterns and peripheral nerve inputs to cardiovascular neurons were investigated in rostral ventrolateral medulla (RVLM) and raphe nucleus of cats. The data from the two were compared to determine their roles in cardiovascular regulation and the endogenous analgesic system. Animals were anesthetized with ${\alpha}-chloralose$ and single cell activities were recorded by carbon-filament microelectrode and their relationships with cardiovascular activity were analyzed. In RVLM area, a total of thirty-three cells were identified as cardiovascular neurons. During one cardiac cycle, the mean discharge rate of the neurons was $1.96{\pm}0.29$ and the peak activity was observed 45 ms after the systolic peak of arterial blood pressure. Thirteen cells could be activated antidromically by stimulation of the the $T_2$ intermediolateral nucleus. Forty-three raphe neurons were identified as cardiovascular neurons whose mean discharge rate during one cardiac cycle was $1.02{\pm}0.12$. None of these cells could be activated antidromically. Study of the interval time histogram of RVLM neurons revealed that the time to the first peak was $128{\pm}20.0\;ms$, being shorter than the period of a cardiac cycle. The same parameter found from the raphe neurons was $481{\pm}67.2\;ms$, which was much longer than the cardiac cycle length. Of seventeen RVLM neurons examined ten received only the peripheral $A{\delta}-afferent$ inputs, whereas six RVLM neurons received both $A{\delta}-$ and C-inputs; the remaining one cell received an inhibitory peripheral C-input. In contrast, nine of eleven raphe neurons were found to receive $A{\delta}-inputs$ only. We conclude that the main output of cardiovascular regulatory influences are mediated through the RVLM neurons. The cardiovascular neurons in the raphe nucleus appear to serve as interneurons transferring cardiovascular afferent information to the raphespinal neurons mediating the endogenous analgesic mechanisms.

• Tunnel and Underground Space
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• v.19 no.3
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• pp.203-212
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• 2009

The prediction of near field behavior around an underground high-level radioactive waste repository is important for the repository design as well as the safety assessment. In this study, a sensitivity analysis for seven parameters consisted of design parameters and material properties was carried out using a three-dimensional finite difference code. From the sensitivity analysis, it was found that the effects of borehole spacing, tunnel spacing, cooling time and rock thermal conductivity were more significant than the other parameters. For getting a statistical distribution of buffer and rock temperatures around the repository, an artificial neural network, backpropagation, was applied. The reliability of the trained neural network was tested with the cases with randomly chosen input parameters. When the parameter variation is within ${\pm}10%$, the prediction from the network was found to be reliable with about a 1% error. It was possible to calculate the temperature distribution for many cases quickly with the trained neural network. The buffer and rock temperatures showed a normal distribution with means of $98^{\circ}C$ and $83.9^{\circ}C$ standard deviations of $3.82^{\circ}C$ and $3.67^{\circ}C$, respectively. Using the neural network, it was also possible to estimate the required change in design parameters for reducing the buffer and rock temperatures for $1^{\circ}C$.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.149-159
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• 1998

The reasonable prediction of time-dependent deformation of prestressed concrete(PSC) box girder bridges is very important for accurate construction as well as good serviceability. The long-term behavior is mostly influenced by the probabilistic characteristic of creep and shrinkage. This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box been taken into account - model uncertainty, parameter variation and environmental condition. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measure are examined to quantify the sensitivity of the outputs of each of the input variables. These are rank correlation coefficient(RCC), partical rank correlation coefficient(PRCC) and standardiozed rank regression coefficient(SRRC) computed on the ranks of the observations. Three creep and shrinkage models - i. e., ACI model. CEB-FIP model and the model in Korea Highway Bridge Specification - are studied. The creep model uncertainy factor and the relative humidity appear to be the most dominant factors with regard to the model output uncertainty.