• Applied Chemistry for Engineering
• /
• v.26 no.2
• /
• pp.205-209
• /
• 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
• /
• v.28 no.2
• /
• pp.133-141
• /
• 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
• /
• v.19 no.3
• /
• pp.203-212
• /
• 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
• /
• v.10 no.6
• /
• pp.149-159
• /
• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.175-186
• /
• 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 for 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 data, 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.

• Tunnel and Underground Space
• /
• v.18 no.1
• /
• pp.44-57
• /
• 2008

In this study, a 2D finite-element analysis, using the SEEP/W program, was carried out to estimate the amount of groundwater flawing into a tunnel, as well as the groundwater tables around wetland areas during and after a tunnel excavation through rock mass. Four sites along the Wonhyo-tunnel in Cheonseong Mountain (Gyeongnam, Korea) were analysed, where the model damain of the tunnel included both wetland and fault zone. The anisotropy of the hydraulic conductivities of the rock mass was calculated using the DFN model, and then used as an input parameter for the cantinuum model. Parametric study on the influencing factors was perofrmed to minimize uncertainties in the hydraulic properties. Moreover, the volumetric water content and hydraulic conductivity functions were applied ta the model to reflect the ability of a medium ta store and transport water under both saturated and unsaturated conditions. The conductivity of fault zone was assumed ta be $10^{-5}m/sec\;or\;10^{-6}m/sec$ and the conductivity of grouting zone was assumed as 1/10, 1/50 or 1/100 of the conductivity of rock mass. Totally $6{\sim}8$ cases of transient flow simulation were peformed at each site. The hydraulic conductivities of fault zone showed a significant influence on groundwater inflow when the fault zone crossed the tunnel. Also, groundwater table around wetland maintained in case that the hydraulic conductivity of grouting zone was reduced ta be less than 1/50 of the hydraulic conductivity of rock mass.

• Atmosphere
• /
• v.23 no.1
• /
• pp.93-102
• /
• 2013

Atmospheric transport pathway of an air mass is an important constraint controlling the chemical properties of the air mass observed at a designated location. Such information could be utilized for understanding observed temporal variabilities in atmospheric concentrations of long-lived chemical compounds, of which sinks and/or sources are related particularly with natural and/or anthropogenic processes in the surface, and as well as for performing inversions to constrain the fluxes of such compounds. The Lagrangian particle dispersion model FLEXPART provides a useful tool for estimating detailed particle dispersion during atmospheric transport, a significant improvement over traditional "single-line" trajectory models that have been widely used. However, those without a modeling background seeking to create simple back-trajectory maps may find it challenging to optimize FLEXPART for their needs. In this study, we explain how to set up, operate, and optimize FLEXPART for back-trajectory analysis, and also provide automatization programs based on the open-source R language. Discussions include setting up an "AVAILABLE" file (directory of input meteorological fields stored on the computer), creating C-shell scripts for initiating FLEXPART runs and storing the output in directories designated by date, as wells as processing the FLEXPART output to create figures for a back-trajectory "footprint" (potential emission sensitivity within the boundary layer). Step by step instructions are explained for an example case of calculating back trajectories derived for Anmyeon-do, Korea for January 2011. One application is also demonstrated in interpreting observed variabilities in atmospheric $CO_2$ concentration at Anmyeon-do during this period. Back-trajectory modeling information introduced in this study should facilitate the creation and automation of most common back-trajectory calculation needs in atmospheric research.

• Korean Chemical Engineering Research
• /
• v.53 no.6
• /
• pp.723-729
• /
• 2015

The moment analysis of cyclic adenosine monophosphate (cAMP) was performed using chromatograms that were obtained with the pulse input method from an octadecyl silica (ODS) high-performance liquid chromatography (HPLC) column. The general rate (GR) model was employed to calculate the first absolute moment and the second central moment. Three important coefficients for moment analysis, which are molecular diffusivity ($D_m$), external mass transfer coefficient ($k_f$), and intra-particle diffusivity ($D_e$), were estimated by the Wilke-Chang equation, Wilson-Geankoplis equation, and comparing van Deemter equation to theoretical plate number equation, respectively. Experiments were conducted by various conditions of flow rates, methanol volume ratio of the mobile phase, and solute concentration. After the moment analysis, results were organized by van Deemter plots. Also van Deemter coefficients were compared each other to effect $H_{ax}$, $H_f$, and $H_d$ on height equivalent to a theoretical plate (HETP, $H_{total}$). The value of intraparticle diffusion ($H_d$) was the primary factor which makes for HETP whereas external mass transfer ($H_f$) was disregardable factor.

• Korean Journal of Remote Sensing
• /
• v.29 no.4
• /
• pp.415-421
• /
• 2013

A new approach to retrieve the single-scattering albedo (SSA) of Asian dust plume, mixed with pollution particles, using multi-wavelength Raman lidar system was suggested in this study. Asian dust plume was separated as dust and non-dust particle (i.e. spherical particle) by the particle depolarization ratio at 532 nm. The vertical profiles of optical properties (the particle extinction coefficient at 355 and 532 nm and backscatter coefficient at 355, 532 and 1064 nm) for non-dust particle were used as input parameter for the inversion algorithm. The inversion algorithm provides the vertical distribution of microphysical properties of non-dust particle only so that the estimation of the SSA for the Asian dust in mixing state was suggested in this study. In order to estimate the SSA for the mixed Asian dust, we combined the SSA of non-dust particles retrieved by the inversion algorithms with assumed the SSA of 0.96 at 532 nm for dust. The retrieved SSA of Asian dust plume by lidar data was compared with the Aerosol Robotics Network (AERONET) retrieved values and showed good agreement.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.5 no.2
• /
• pp.16-24
• /
• 2002

Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform. The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.