• Composites Research
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• v.26 no.6
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• pp.363-372
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• 2013

Numerical simulation for Resin Transfer Molding manufacturing process is attempted by using the eXtended Finite Element Method (XFEM) combined with the level set method. XFEM allows to obtaining a good numerical precision of the pressure near the resin flow front, where its gradient is discontinuous. The enriched shape functions of XFEM are derived by using the level set values so as to correctly describe the interpolation with the resin flow front. In addition, the level set method is used to transport the resin flow front at each time step during the mold filling. The level set values are calculated by an implicit characteristic Galerkin FEM. The multi-frontal solver of IPSAP is adopted to solve the system. This work is validated by comparing the obtained results with analytic solutions. Moreover, a localization method of XFEM and level set method is proposed to increase the computing efficiency. The computation domain is reduced to the small region near the resin flow front. Therefore, the total computing time is strongly reduced by it. The efficiency test is made with a simple channel flow model. Several application examples are analyzed to demonstrate ability of this method.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.89-95
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• 2004

Intensity-modulated radiation therapy (IMRT) often uses small beam segments. The heterogeneity effect is well known for relatively large field sizes used in the conventional radiation treatments. However, this effect is not known in small fields such as the beamlets used in IMRT. There are many factors that can cause errors in the small field i.e. electronic disequilibrium and multiple electron scattering. This study prepared geometrically regular heterogeneous phantoms, and compared the measurements with the calculations using the Convolution/Superposition algorithm and Monte Carlo method for small beams. This study used the BEAM00/EGS4 code to simulate the head of a Varian 2300C/D. The commissioning of a 6MV photon beam were performed from two points of view, the beam profiles and depth doses. The calculated voxel size was 1${\times}$1${\times}$2$\textrm{cm}^2$ with field sizes of 1${\times}$1$\textrm{cm}^2$, 2${\times}$2$\textrm{cm}^2$, and 5${\times}$5$\textrm{cm}^2$. The XiOTM TPS (Treatment Planning System) was used for the calculation using the Convolution/Superposition algorithm. The 6MV photon beam was irradiated to homogeneous (water equivalent) and heterogeneous phantoms (water equivalent ＋ air cavity, water equivalent ＋ bone equivalent). The beam profiles were well matched within :t1 mm and the depth doses were within ${\pm}$2%. In conclusion, the dose calculations of the Convolution/Superposition and Monte Carlo simulations showed good agreement with the film measurements in the small field.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.281-281
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• 2016

기후변화로 인한 기상학적 자연재해로부터 대비하고 안정적인 용수공급을 위해 유역의 다양한 수문 요소들에 대한 분석 필요성이 증가하고 있다. 계절적 강수량의 편차가 큰 우리나라는 유역 통합 물관리가 중요하며, 효율적 수자원 관리와 물안보 확보를 위해 유역내 물순환을 이해하는 것이 중요하다. 유역의 유출을 결정하는 요소들에는 강우, 증발산량, 토양 수분 및 지하수 등이 있으며, 시간적으로는 홍수와 같이 단기에 발생하는 유출과 장기적으로 발생하는 유출이 있다. 장기 유출은 단기 유출에 비해 토양내 수분량이 무시할 수 없을 정도로 영향을 미치게 되므로, 1년 이상의 장기 유출 해석을 위해서는 강우가 발생하지 않는 기간 동안의 토양 수분량 변화와 증발산 영향을 고려할 필요가 있다. K-water에서 자체 개발된 분포형 장단기유출 모델인 K-DRUM은 유역을 격자(grid)단위로 구분하고 각 셀들에 대한 매개변수는 흐름방향도, 표고분포도, 토지이용도, 토지피복도 등을 GIS처리하여 일괄 입력할 수 있도록 함으로써 매개변수 산정과정에서 문제가 되는 경험적인 요인을 제거하였다. 흐름의 구분은 얕은면 흐름, 지표하 흐름, 지하수 흐름으로 구분하여 운동파법과 선형저류법을 적용하였다. 또한 초기 토양함수 자동보정기법으로 실제의 기저유출량을 재현하여 전체적인 유출모의 정확도를 높였으며, FAO-56 Penman-Monteith법을 적용한 증발산량 산정모듈과 Sugawara et al.(1984)이 제안한 개념적 융설 및 적설모듈을 추가하였다. K-DRUM모형을 이용한 유출분석은 용담댐 시험유역을 대상으로 2013년도 1년간의 유출모의를 수행하였다. 입력자료는 용담댐 유역의 지형, 토양 및 토지특성 정보와 시단위 강우 및 기상정보(온도, 바람, 일사 등)를 활용하였다. 분석 결과, 총 관측유출량은 7,151 ㎥/s이고 총 계산유출량 $8,257m^3/s$이며, 관측유출량 대비 계산유출량은 약 115% 정도로 나타났다. 연간 총 강우량은 1303.5 mm로 유역면적 약 $930km^2$을 적용하여 유역 총 강우량을 산정하면 $14,030m^3/s$로서 관측유출량은 유역 총 강우량 대비 51%이고 계산유출량은 59% 정도로 나타났다. 즉 유역 유출율은 약 51% 수준으로 보통의 유역과 유사한 수준이다. 관측된 토양수분량과 K-DRUM 모형의 계산된 토양수분량을 비교하기 위하여 관측 토양수분량의 비율을 이용하여 비교하였다. 모의결과 토양수분은 강우에 의해 변화하며, 관측결과와 유사한 형태로 나타남을 알 수 있었다.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.26-33
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• 2005

Inversion of multi-mode surface-wave phase velocity for shallow engineering site investigation has received much attention in recent years. A sensitivity analysis and inversion of both synthetic and field data demonstrates the greater effectiveness of this method over employing the fundamental mode alone. Perturbation of thickness and shear-wave velocity parameters in multi-modal Rayleigh wave phase velocities revealed that the sensitivities of higher modes: (a) concentrate in different frequency bands, and (b) are greater than the fundamental mode for deeper parameters. These observations suggest that multi-mode phase velocity inversion can provide better parameter discrimination and imaging of deep structure, especially with a velocity reversal, than can inversion of fundamental mode data alone. An inversion of the theoretical phase velocities in a model with a low velocity layer at 20 m depth can only image the soft layer when the first higher mode is incorporated. This is especially important when the lowest measurable frequency is only 6 Hz. Field tests were conducted at sites surveyed by borehole and PS logging. At the first site, an array microtremor survey, often used for deep geological surveying in Japan, was used to survey the soil down to 35 m depth. At the second site, linear multichannel spreads with a sledgehammer source were recorded, for an investigation down to 12 m depth. The f-k power spectrum method was applied for dispersion analysis, and velocities up to the second higher mode were observed in each test. The multi-mode inversion results agree well with PS logs, but models estimated from the fundamental mode alone show f large underestimation of the depth to shallow soft layers below artificial fill.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.809-817
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• 2008

This paper deals with the development of ViscoElastic Continuum Damage Finite Element Program (VECD-FEP++) and its verification with the results from both field and laboratory accelerated pavement tests. Damage characteristics of asphalt concrete mixture have been defined by Schapery's work potential theory, and uniaxial constant crosshead rate tests were carried out to be used for damage model implementation. VECD-FEP++ predictions were compared with strain responses (longitudinal and transverse strains) under moving wheel loads running at different constant speeds. To this end, an asphalt pavement section (A5) of Korea Expressway Corporation Test Road (KECTR) instrumented with strain gauges were loaded with a dump truck. Also, a series of accelerated pavement fatigue tests have been conducted at pavement sections surfaced with four asphalt concrete mixtures (Dense-graded, SBS, Terpolymer, CR-TB). Planar strain responses were in good agreement with field measurements at base layers, whereas strains at both surface and intermediate layers were found different from simulation results due to the complexity of tire-road contact pressures. Finally, fatigue characteristics of four asphalt mixtures were reasonably described with VECD-FEP++.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.1 s.4
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• pp.135-141
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• 2002

Both non-linear damping values of the deep and shallow crustal materials and seismic source parameters are found from the observed near-field seismic ground motions at the South-eastern Korean Peninsula. The non-linear numerical algorithm applied in this study is Levenberg-Marquadet method. All the 25 sets of horizontal ground motions (east-west and north-south components at each seismic station) from 3 events (micro to macro scale) were used for the analysis of damping values and source parameters. The non-linear damping values of the deep and shallow crustal materials were found to be more similar to those of the region of the Western United States. The seismic source parameters found from this study also showed that the resultant stress drop values are relatively low compared to those of the Western United Sates. Consequently, comparisons of the various seismic parameters from this study and those of the United States Seismo-tectonic data suggest that the seismo-tectonic characteristics of the South eastern Korean Peninsula is more similar to those of the Western U.S.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Tuberculosis and Respiratory Diseases
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• v.58 no.3
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• pp.230-242
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• 2005

Background : Spirometry should be compared with the normal predictive values obtained from the same population using the same procedures, because different ethnicity and different procedures are known to influence the spirometry results. This study was performed to obtain the normal predictive values of the Forced Vital Capacity(FVC), Forced Expiratory Volume in 1 Second($FEV_1$), Forced Expiratory Volume in 6 Seconds($FEV_6$), and $FEV_1/FVC$ for a representative Korean population. Methods : Based on the 2000 Population Census of the National Statistical Office of Korea, stratified random sampling was carried out to obtain representative samples of the Korean population. This study was performed as a part of the National Health and Nutrition Survey of Korea in 2001. The lung function was measured using the standardized methods and protocols recommended by the American Thoracic Society. Among those 4,816 subjects who had performed spirometry performed, there was a total of 1,212 nonsmokers (206 males and 1,006 females) with no significant history of respiratory diseases and symptoms, with clear chest X-rays, and with no significant exposure to respiratory hazards subjects. Their residence and age distribution was representative of the whole nation. Mixed effect models were examined based on the Akaike's information criteria in statistical analysis, and those variables common to both genders were analyzed by regression analysis to obtain the final equations. Results : The variables affecting the normal predicted values of the FVC and $FEV_6$ for males and females were $age^2$, height, and weight. The variables affecting the normal predicted values of the $FEV_1$ for males and females were $age^2$, and height. The variables affecting the normal predicted values of the $FEV_1/FVC$ for male and female were age and height. Conclusion : The predicted values of the FVC and $FEV_1$ was higher in this study than in other Korean or foreign studies, even though the difference was < 10%. When compared with those predicted values for Caucasian populations, the study results were actually comparable or higher, which might be due to the stricter criteria of the normal population and the systemic quality controls applied to the whole study procedures together with the rapid physical growth of the younger generations in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.