• Journal of Soil and Groundwater Environment
• /
• v.20 no.3
• /
• pp.74-82
• /
• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.4
• /
• pp.736-742
• /
• 2003

This paper presents the propagation path loss in a tunnel which is a kinds of underground environments. To predict propagation path loss more accurately, we choose a straight tunnel with rectangular cross-section. The simulated receiver powers that are using a hybrid waveguide model and a Ray-Tracing method, are compared with the measured ones as a function of distance between TX and RX antennas in tunnel. The attenuation value of regression analysis for measured power in the tunnel is 0.0238dB/m which is similar to the one of the EH1.2 mode, 0.0246dB/m in hybrid waveguide model. By comparing simulation with measurement in tunnels, it has been shown that the measured values are approximate to the simulated results of ray-tracing model. In the analysis of wide-band channel characteristics of the tunnel, the more the distance between TX and RX antennas in tunnel increases, RMS delay spread increases and coherence bandwidth decreases.

• KEPCO Journal on Electric Power and Energy
• /
• v.1 no.1
• /
• pp.33-38
• /
• 2015

Energy storage systems (ESSs) have been utilized widely in the world to optimize the power operation system and to improve the power quality. As lithium secondary batteries are the main power supplier for ESSs, it is very important to predict its cycle and power degradation behavior. In particular, the power, one of the hardest electrochemical properties to measure, needs lots of resources such as time and facilities. Due to these difficulties, computer modelling of lithium secondary batteries is applied to predict the DC-IR and power value during charging and discharging as a function of state of charge (SOC) by using pulse-based measurement methods. Moreover, based on the hybrid pulse power characteristics (HPPC) and J-Pulse (JEVS D 713, Japan Electric Vehicle Association Standards) methods, their electrochemical properties are also compared and discussed.

• 한국전산유체공학회:학술대회논문집
• /
• 2008.03a
• /
• pp.104-110
• /
• 2008

Detached Eddy Simulation (DES) is applied to an axisymmetric base flow at supersonic mainstream. DES is a hybrid approach to modeling turbulence that combines the best features of the Reynolds-averaged Navier-Stokes RANS) and large-eddy simulation (LES) approaches. In the Reynolds-averaged mode, the model is currently based on either the Spalart-Allmaras (S-A) turbulence model. In the large eddy simulation mode, it is based on the Smagorinski subgrid scale model. Accurate predictions of the base flowfield and base pressure are successfully achieved by using the DES methodology with less computational cost than that of pure LES and monotone integrated large-eddy simulation (MILES) approaches. The DES accurately resolves the physics of unsteady turbulent motions, such as shear layer rollup, large-eddy motions in the downstream region, small-eddy motions inside the recirculating region. Comparison of the results shows that it is necessary to resolve approaching boundary layers and free shear-layer velocity profiles from the base edge correctly for the accurate prediction of base flows. The consideration of an empirical constant CDES for a compressible flow analysis may suggest that the optimal value of empirical constant CDES may be larger in the flows with strong compressibility than in incompressible flows.

• 한국전산유체공학회:학술대회논문집
• /
• 2008.10a
• /
• pp.104-110
• /
• 2008

Detached Eddy Simulation (DES) is applied to an axisymmetric base flow at supersonic mainstream. DES is a hybrid approach to modeling turbulence that combines the best features of the Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulation (LES) approaches. In the Reynolds-averaged mode, the model is currently based on either the Spalart-Allmaras (S-A) turbulence model. In the large eddy simulation mode, it is based on the Smagorinski subgrid scale model. Accurate predictions of the base flowfield and base pressure are successfully achieved by using the DES methodology with less computational cost than that of pure LES and monotone integrated large-eddy simulation (MILES) approaches. The DES accurately resolves the physics of unsteady turbulent motions, such as shear layer rollup, large-eddy motions in the downstream region, small-eddy motions inside the recirculating region. Comparison of the results shows that it is necessary to resolve approaching boundary layers and free shear-layer velocity profiles from the base edge correctly for the accurate prediction of base flows. The consideration of an empirical constant CDES for a compressible flow analysis may suggest that the optimal value of empirical constant CDES may be larger in the flows with strong compressibility than in incompressible flows.

• Journal of the Korean Magnetics Society
• /
• v.26 no.3
• /
• pp.81-85
• /
• 2016

This paper introduces a performance integration method to predict variation characteristic of a performance function of electromagnetic machines or devices due to manufacturing tolerances. A normalized performance function space and a hybrid mean value technique are adapted to effectively predict mean and variance, which can identify probabilistic distribution of the performance function. To verify the effectiveness and accuracy of the proposed method, a mathematical problem and a loudspeaker model are tested, and numerical results are compared with those of existing methods such as Monte Carlo simulation and univariate dimension reduction method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.11
• /
• pp.5392-5418
• /
• 2017

The primary aim of this paper is to propose a non-linear regression based technique for mapping different network Quality of Service (QoS) factors to an integrated end-user Quality of Experience (QoE) or Mean Opinion Score (MOS) value for an online video streaming service on a mobile phone. We use six network QoS factors for finding out the user QoE. The contribution of this paper is threefold. First, we investigate the impact of the network QoS factors on the perceived video quality. Next, we perform an individual mapping of the significant network QoS parameters obtained in stage 1 to the user QoE based upon a non-linear regression method. The optimal QoS to QoE mapping function is chosen based upon a decision variable. In the final stage, we evaluate the integrated QoE of the system by taking the combined effect of all the QoS factors considered. Extensive subjective tests comprising of over 50 people across a wide variety of video contents encoded with H.265/HEVC and VP9 codec have been conducted in order to gather the actual MOS data for the purpose of QoS to QoE mapping. Our proposed hybrid model has been validated against unseen data and reveals good prediction accuracy.

• Journal of Information Processing Systems
• /
• v.8 no.3
• /
• pp.459-470
• /
• 2012

Recently, high-throughput technologies such as the two-hybrid system, protein chip, Mass Spectrometry, and the phage display have furnished a lot of data on protein-protein interactions (PPIs), but the data has not been accurate so far and the quantity has also been limited. In this respect, computational techniques for the prediction and validation of PPIs have been developed. However, existing computational methods do not take into account the fact that a PPI is actually originated from the interactions of domains that each protein contains. So, in this work, the information on domain modules of individual proteins has been employed in order to find out the protein interaction relationship. The system developed here, WASPI (Web-based Assistant System for Protein-protein interaction Inference), has been implemented to provide many functional insights into the protein interactions and their domains. To achieve those objectives, several preprocessing steps have been taken. First, the domain module information of interacting proteins was extracted by taking advantage of the InterPro database, which includes protein families, domains, and functional sites. The InterProScan program was used in this preprocess. Second, the homology comparison with the GO (Gene Ontology) and COG (Clusters of Orthologous Groups) with an E-value of $10^{-5}$, $10^{-3}$ respectively, was employed to obtain the information on the function and annotation of each interacting protein of a secondary PPI database in the WASPI. The BLAST program was utilized for the homology comparison.

• Journal of agriculture & life science
• /
• v.46 no.2
• /
• pp.9-17
• /
• 2012

In this study, in addition to the green tea - wood fiber hybrid composite boards of previous researches, to make effective use of saw dust of domestic cypress tree with functionalities and application as interior materials, eco-friendly hybrid composite boards were manufactured from wood fiber, green tea and saw dust of cypress tree. We investigated the effect of the component ratio of saw dust and green tea on dynamic MOE (modulus of elasticity). Dynamic MOE was within 1.41~1.65 GPa, and showed the highest value in wood fiber : green tea : saw dust = 50 : 40 : 10 of the component ratio, and had the lowest value in 50 : 30 : 20 of component ratio. These values were 1.4~1.6 times higher than static bending MOE of wood fiber - saw dust - green tea hybrid composite boards, and were 2.0~2.9 times lower than those of green tea - wood fiber hybrid composite boards reported in the previous researches. From the results of correlation regression analyses between dynamic MOE and static strength performances, a very high correlation coefficients were obtained, therefore it was found that static bending strength performances can be estimated with a high reliability from dynamic MOE.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.50 no.10
• /
• pp.701-708
• /
• 2022

Since the propellant tank structure of the projectile is mainly subjected to a compressive force, there is a high risk of damage due to buckling. Large and lightweight structures such as propellant tank have a complex manufacturing process. So it requires a non-destructive test method to predict buckling load to use the structure after testing. Many studies have been conducted on Vibration Correlation Technique(VCT), which predicts buckling load using the relationship between compressive load and natural frequency, but it requires a large compressive load to predict the buckling load accurately, and it tends to decrease prediction accuracy with increasing internal pressure in structure. In this paper, we analyzed the causes of the decrease in prediction accuracy when internal pressure increases and proposed a method increasing prediction accuracy under the low compressive load for being usable after testing, through VCT combined testing and FEA result. The prediction value by the proposed method was very consistent with the measured actual buckling load.