• 대기
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• 제17권1호
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• pp.55-68
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• 2007

Future climate changes over East Asia are projected by anthropogenic forcing of greenhouse gases and aerosols using ECHO-G/S (ECHAM4/HOPE-G). Climate simulation in the 21st century is conducted with three standard SRES scenarios (A1B, B1, and A2) and the model performance is assessed by the 20th Century (20C3M) experiment. From the present climate simulation (20C3M), the model reproduced reliable climate state in the most fields, however, cold bias in temperature and dry bias of summer in precipitation occurred. The intercomparison among models using Taylor diagram indicates that ECHO-G/S exhibits smaller mean bias and higher pattern correlation than other nine AOGCMs. Based on SRES scenarios, East Asia will experience warmer and wetter climate in the coming 21st century. Changes of geographical patterns from the present to the future are considerably similar through all the scenarios except for the magnitude difference. The temperature in winter and precipitation in summer show remarkable increase. In spite of the large uncertainty in simulating precipitation by regional scale, we found that the summer (winter) precipitation at eastern coast (north of $40^{\circ}N$) of East Asia has significantly increased. In the 21st century, the warming over the continents of East Asia showed much more increase than that over the ocean. Hence, more enhanced (weakened) land-sea thermal contrast over East Asia in summer (winter) will cause strong (weak) monsoon. In summer, the low pressure located in East Asia becomes deeper and the moisture from the south or southeast is transported more into the land. These result in increasing precipitation amount over East Asia, especially at the coastal region. In winter, the increase (decrease) of precipitation is accompanied by strengthening (weakening) of baroclinicity over the land (sea) of East Asia.

• 대한기계학회논문집B
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• 제32권11호
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• pp.897-905
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• 2008

Selective catalytic reduction(SCR) is known as one of promising methods for reducing $NO_x$ emissions in diesel exhaust gases. $NO_x$ emissions react with ammonia in the catalyst surface of SCR system at working temperature of catalyst. In this study, to raise the reacting temperature when the exhaust gas temperature is too low, a heater is located at the bottom of SCR reactor. At an ambient temperature, ammonia is radially injected perpendicular to the exhaust gas flow at inlet pipe and uniformly mixed in the mixing area after being impinged against the wall. To predict the turbulent model inside the mixing area of SCR system, the standard ${\kappa}\;-\;{\varepsilon}$ model is applied. This work investigates numerically the effects of induced heat on the gaseous flow. The results show that the Taylor-$G{\ddot{o}}rtler$ type vortex is generated after the gaseous flow impinges the wall in which these vortices influence the temperature distribution. The addition of heat disturbs the flow structure in bottom area and then stretching flow occurs. Vorticity strand is also formed when heat is continuously increased. Constriction process takes place, however, when a further heat input over a critical temperature is increased and finally forms shed vortex which is disconnected from the vorticity strand. The strong vortex restricts the heat transport in the gaseous flow.

• 한국전문물리치료학회지
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• 제14권2호
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• pp.1-10
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• 2007

The purpose of this experiment was to investigate the applicability of audio-visual modeling for improving the efficiency of rehabilitative programs by analyzing the effects of observing these various models on the capacity of stroke patients to perform upper limb activities. Twenty-one stroke patients participated in the experiment and were randomly assigned to either task modeling, sport modeling, or control group. During 2 weeks of intervention, subjects in all groups participated in the physical practice of experimental tasks. These tasks comprised of a Nine Hole Peg Test, the Jebsen-Taylor Hand Function tests, and locomotion. These tasks were performed 5 days a week, 30 min per day. In addition to the physical practice, the task modeling group observed a model performing experimental tasks and locomotive activities for 20 min, while the sport modeling group observed a model performing various sport activities for 20 min. Subjects' ability to perform the experimental tasks was measured 3 times, before, immediately after, and 1 week after the intervention. Analyses of the capacity to perform upper extremity activities displayed significant improvement from the pre-test to immediate and delayed post-tests in all groups. However, the amount of improvement was the highest in the task modeling group. The task modeling group was superior to the control group in the post-test of all experimental tasks, whereas the sport modeling group did not display significant differences from the control group. These results suggest that audio-visual modeling can be used as an effective cognitive intervention for facilitating the rehabilitation of stroke patients, and its rehabilitative effect can be maximized when the program is comprised of performance scenes directly related to the target task.

• Ocean Systems Engineering
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• 제7권4호
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• pp.435-460
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• 2017

The main objective of this study is to investigate the turning and zig-zag maneuvering performance of the well-known naval surface combatant DTMB (David Taylor Model Basin) 5415 hull with URANS (Unsteady Reynolds-averaged Navier-Stokes) method. Numerical simulations of static drift tests have been performed by a commercial RANS solver based on a finite volume method (FVM) in an unsteady manner. The fluid flow is considered as 3-D, incompressible and fully turbulent. Hydrodynamic analyses have been carried out for a fixed Froude number 0.28. During the analyses, the free surface effects have been taken into account using VOF (Volume of Fluid) method and the hull is considered as fixed. First, the code has been validated with the available experimental data in literature. After validation, static drift, static rudder and drift and rudder tests have been simulated. The forces and moments acting on the hull have been computed with URANS approach. Numerical results have been applied to determine the hydrodynamic maneuvering coefficients, such as, velocity terms and rudder terms. The acceleration, angular velocity and cross-coupled terms have been taken from the available experimental data. A computer program has been developed to apply a fast maneuvering simulation technique. Abkowitz's non-linear mathematical model has been used to calculate the forces and moment acting on the hull during the maneuvering motion. Euler method on the other hand has been applied to solve the simultaneous differential equations. Turning and zig-zag maneuvering simulations have been carried out and the maneuvering characteristics have been determined and the numerical simulation results have been compared with the available data in literature. In addition, viscous effects have been investigated using Eulerian approach for several static drift cases.

• 한국작물학회지
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• 제43권2호
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• pp.113-119
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• 1998

Spatial patterns of soil temperature on sloping lands are related to the amount of solar irradiance at the surface. Since soil temperature is a critical determinant of many biological processes occurring in the soil, an accurate prediction of soil temperature distribution could be beneficial to agricultural and environmental management. However, at least two problems are identified in soil temperature prediction over natural sloped surfaces. One is the complexity of converting solar irradiances to corresponding soil temperatures, and the other, if the first problem could be solved, is the difficulty in handling large volumes of geo-spatial data. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. In this paper, a simple model for conversion of solar irradiance to soil temperature is developed within a GIS environment. The irradiance-temperature conversion model is based on a geophysical variable consisting of daily short- and long-wave radiation components calculated for any slope. The short-wave component is scaled to accommodate a simplified surface energy balance expression. Linear regression equations are derived for 10 and 50 cm soil temperatures by using this variable as a single determinant and based on a long term observation data set from a horizontal location. Extendability of these equations to sloped surfaces is tested by comparing the calculated data with the monthly mean soil temperature data observed in Iowa and at 12 locations near the Tennessee - Kentucky border with various slope and aspect factors. Calculated soil temperature variations agreed well with the observed data. Finally, this method is applied to a simulation study of daily mean soil temperatures over sloped corn fields on a 30 m by 30 m resolution. The outputs reveal potential effects of topography including shading by neighboring terrain as well as the slope and aspect of the land itself on the soil temperature.

• Geomechanics and Engineering
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• 제31권2호
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Computers and Concrete
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• 제32권6호
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Steel and Composite Structures
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• 제51권4호
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• pp.391-406
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• 2024

The present research investigates the combination resonance behaviors of porous FG shallow shells reinforced with oblique stiffeners and subjected to a two-term excitation. The oblique stiffeners considered in this research reinforce the shell internally and externally. To model the stiffeners, Lekhnitskii's smeared stiffeners technique is utilized. According to the first-order shear deformation theory (FSDT) and stress functions, a nonlinear model of the oblique stiffened shallow shell is established. With regard to the FSDT and von-Kármán nonlinear geometric assumptions, the stress-strain relationships for the present shell system are developed. Also, in order to discretize the nonlinear governing equations, the Galerkin method is implemented. To obtain the required relations for investigating the combination resonance theoretically, the method of multiple scales is applied. For verifying the results of the present research, generated results are compared with previous research. Additionally, a comparison with the P-T method is conducted to increase the validity of the generated results, as this method has illustrated advantages over other numerical methods in terms of accuracy and reliability. In this method, the piecewise constant argument is used jointly with the Taylor series expansion, which is why it is named the P-T method. The effects of stiffeners with different angles, and the effects of material parameters on the combination resonance behaviors of the present system are addressed. With the findings of this research, researchers and engineers in this field may use them as benchmarks for their design and research of porous FG shallow shells.

• Advances in materials Research
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• 제13권1호
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.

• Journal of the Optical Society of Korea
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• 제18권4호
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.