• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.256-261
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• 2010

In this paper, a new nonlinear predictor using general regression neural network(GRNN) is proposed for the subband coding of moving pictures. The performance of a proposed nonlinear predictor is compared with BMA(Block Match Algorithm), the most conventional motion estimation technique. As a result, the nonlinear predictor using GRNN can predict well more 2-3dB than BMA. Specially, because of having a clustering process and smoothing noise signals, this predictor well preserves edges in frames after predicting the subband signal. This result is important with respect of human visual system and is excellent performance for the subband coding of moving pictures.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.505-517
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• 2016

The structural integrity of a dual-purpose metal cask currently under development by the Korea Radioactive Waste Agency (KORAD) was evaluated, through numerical simulations and a model test, under high-speed missile impact reflecting targeted aircraft crash conditions. The impact conditions were carefully chosen through a survey on accident cases and recommendations from literature. In the impact scenario, a missile flying horizontally hits the top side of the cask, which is freestanding on a concrete pad, with a velocity of 150 m/s. A simplified missile simulating a commercial aircraft engine was designed from an impact loade-time function available in literature. In the analyses, the dynamic behavior of the metal cask and the integrity of the containment boundary were assessed. The simulation results were compared with the test results for a 1:3 scale model. Although the dynamic behavior of the cask in the model test did not match exactly with the prediction from the numerical simulation, other structural responses, such as the acceleration and strain history during the impact, showed very good agreement. Moreover, the containment function of the cask survived the missile impact as expected from the numerical simulation. Thus, the procedure and methodology adopted in the structural numerical analyses were successfully validated.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.11
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• pp.876-882
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• 2014

This study investigates an efficient method to estimate the total acoustic radiation power of submerged circular cylindrical structures. Since the acoustic radiation power of submerged vehicles can be changed during the operation, the estimation for its monitoring onboard is required to accomplish the missions. The total acoustic radiation power is estimated using the measured velocity and the calculated radiation efficiency of the surface which consists of submerged rectangular plate elements. Experiments are carried out to validate the estimation approach. Comparisons of the estimation results with the measurements show that they are in a good agreement for the mid-high frequency range and match well for the cases of different excitation locations which correspond to the different operation modes of underwater vehicles as well. Therefore, this estimation method can be applied effectively to the development of the radiated noise monitoring-system.

• Steel and Composite Structures
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• v.33 no.4
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• pp.583-594
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• 2019

In the areas highly exposed to earthquakes, concrete-filled steel tube columns (CFSTCs) are known to provide superior structural aspects such as (i) high strength for good seismic performance (ii) high ductility (iii) enhanced energy absorption (iv) confining pressure to concrete, (v) high section modulus, etc. Numerous studies were reported on behavior of CFSTCs under axial compression loadings. This paper presents an analytical model to predict ultimate load capacity of CFSTCs with circular sections under axial load by using multivariate adaptive regression splines (MARS). MARS is a nonlinear and non-parametric regression methodology. After careful study of literature, 150 comprehensive experimental data presented in the previous studies were examined to prepare a data set and the dependent variables such as geometrical and mechanical properties of circular CFST system have been identified. Basically, MARS model establishes a relation between predictors and dependent variables. Separate regression lines can be formed through the concept of divide and conquers strategy. About 70% of the consolidated data has been used for development of model and the rest of the data has been used for validation of the model. Proper care has been taken such that the input data consists of all ranges of variables. From the studies, it is noted that the predicted ultimate axial load capacity of CFSTCs is found to match with the corresponding experimental observations of literature.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.6
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• pp.599-614
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• 2014

When tunnelling with TBM (Tunnel Boring Machine), accessibility to tunnel face is very limited because tunnel face is mostly occupied by a bunch of machines. Existing techniques that can predict ground condition ahead of TBM tunnel are extremely limited. In this study, the TBM Resistivity Prediction (TRP) system has been developed for predicting anomalous zone ahead of tunnel face utilizing electrical resistivity. The applicability and prediction accuracy of the developed system has been verified by performing field tests at subway tunnel construction site in which an EPB (Earth Pressure Balanced) shield TBM was used for tunnelling work. The TRP system is able to predicts the location, thickness and electrical properties of anomalous zone by performing inverse analysis using measured resistivity of the ground. To make field tests possible, an apparatus was devised to attach electrode to tunnel face through the chamber. The electrode can be advanced from the chamber to the tunnel face to fully touch the ground in front of the tunnel face. In the 1st field test, none of the anomalous zone was predicted, because the rock around the tunnel face has the same resistivity and permittivity with the rock ahead of tunnel face. In the 2nd field test, 5 m thick anomalous zone was predicted with lower permittivity than that of the rock around the tunnel face. The test results match well with the ground condition predicted, respectively, from geophysical exploration, or directly obtained either from drilling boreholes or from daily observed muck condition.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Computers and Concrete
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• v.8 no.2
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• pp.125-142
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• 2011

In the present paper, a numerical simulation method based on mesoscopic composite structure of concrete, the truss network model, is developed to evaluate the diffusivity of concrete in order to account for the microstructure of concrete, the binding effect of chloride ions and the chloride concentration dependence. In the model, concrete is described as a three-phase composite, consisting of mortar, coarse aggregates and the interfacial transition zones (ITZs) between them. The advantage of the current model is that it can easily represent the movement of mass (e.g. water or chloride ions) through ITZs or the potential cracks within concrete. An analytical method to estimate the chloride diffusivity of mortar and ITZ, which are both treated as homogenious materials in the model, is introduced in terms of water-to-cement ratio (w/c) and sand volume fraction. Using the newly developed approaches, the effect of cracking of concrete on chloride diffusion is reflected by means of the similar process as that in the test. The results of calculation give close match with experimental observations. Furthermore, with consideration of the binding capacity of chloride ions to cement paste and the concentration dependence for diffusivity, the one-dimensional nonlinear diffusion equation is established, as well as its finite difference form in terms of the truss network model. A series of numerical analysises performed on the model find that the chloride diffusion is substantially influenced by the binding capacity and concentration dependence, which is same as that revealed in some experimental investigations. This indicates the necessity to take into account the binding capacity and chloride concentration dependence in the durability analysis and service life prediction of concrete structures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.97-106
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• 2011

In this study, viscoplastic material properties of solder alloy which is used in the electronics packages are inversely estimated. A specimen is fabricated to this end, and an experiment is conducted to examine deformation by Moir$\acute{e}$ interferometry. As a result of the experiment, bending displacement of the specimen and shear strain of the solder are obtained. A viscoplastic finite element analysis procedure is established, and the material parameters are determined to match closely with the experiments. The uncertainties which include inherent experimental error and insufficient data of experiments are addressed by using the method of computer model calibration. As a result, material parameters are identified in the form of confidence interval, and the displacements and strains using these parameters are predicted in the form the prediction interval.