• Advances in concrete construction
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• v.10 no.4
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• pp.345-355
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• 2020

In this paper, vibration characteristics of double-walled carbon nanotubes (CNTs) is studied based upon nonlocal elastic shell theory. The significance of small scale is being perceived by developing nonlocal Love shell model. The wave propagation approach has been utilized to frame the governing equations as eigen value system. The influence of nonlocal parameter subjected to diverse end supports has been overtly analyzed. An appropriate selection of material properties and nonlocal parameter has been considered. The influence of changing mechanical parameter Poisson's ratio has been investigated in detail. The dominance of boundary conditions via nonlocal parameter is shown graphically. The results generated furnish the evidence regarding applicability of nonlocal shell model and also verified by earlier published literature.

• Computers and Concrete
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• v.25 no.3
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• pp.205-214
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• 2020

Cytoskeleton components in living cell bear large compressive force and are responsible in maintaining the cell shape. Actually these filaments are surrounded by viscoelastic media within the cell. This surrounding, viscoelastic media affects the buckling behavior of these filaments when external force is applied on these filaments by exerting continuous pressure in opposite directions to the incipient buckling of the filaments. In this article a mechanical model is applied to account the effects of this media on the buckling behavior of intermediate filaments network of cytoskeleton. The model immeasurably associates; filament's bending rigidity, adjacent system elasticity, and cytosol viscosity with buckling wavelength, buckling growth rate and buckling amplitude of the filaments.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• Journal of Korean Society of Disaster and Security
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• v.13 no.1
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• pp.1-11
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• 2020

One of the important problem in statistical hydrology is to estimate the appropriated probability distribution for a given sample data. For the problem, a goodness-of-fit test is conducted based on the similarity between estimated probability distribution and assumed theoretical probability distribution. Probability plot correlation coefficient test (PPCC) is one of the goodness-of-fit test method. PPCC has high rejection power and its application is simple. In this study, test statistics of PPCC were derived for generalized extreme value distribution (GEV) models based on L-moments and these statistics were suggested by the multiple and nonlinear regression equations for its usability. To review the rejection power of the newly proposed method in this study, Monte Carlo simulation was performed with other goodness-of-fit tests including the existing PPCC test. The results showed that PPCC-A test which is proposed in this study demonstrated better rejection power than other methods, including the existing PPCC test. It is expected that the new method will be helpful to estimate the appropriate probability distribution model.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.797-810
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• 2017

The Korean peninsula is exposed to typhoons every year. Typhoons cause huge socioeconomic damage because tropical cyclones tend to occur with strong winds and heavy precipitation. In order to understand the complex dependence structure between strong winds and heavy precipitation, the copula links a set of univariate distributions to a multivariate distribution and has been actively studied in the field of hydrology. In this study, we carried out analysis using data of wind speed and precipitation collected from the weather stations in Busan and Jeju. Log-Normal, Gamma, and Weibull distributions were considered to explain marginal distributions of the copula. Kolmogorov-Smirnov, Cramer-von-Mises, and Anderson-Darling test statistics were employed for testing the goodness-of-fit of marginal distribution. Observed pseudo data were calculated through inverse transformation method for establishing the copula. Elliptical, archimedean, and extreme copula were considered to explain the dependence structure between strong winds and heavy precipitation. In selecting the best copula, we employed the Cramer-von-Mises test and cross-validation. In Busan, precipitation according to average wind speed followed t copula and precipitation just as maximum wind speed adopted Clayton copula. In Jeju, precipitation according to maximum wind speed complied Normal copula and average wind speed as stated in precipitation followed Frank copula and maximum wind speed according to precipitation observed Husler-Reiss copula.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.4
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• pp.9-19
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• 2016

The objective of this study was to evaluate the applicability of SWAT (Soil and Water Assessment Tool) model for runoff estimation in the Nam river dam watershed. Input data for the SWAT model were established using spatial data (land use, soil, digital elevation map) and weather data. The SWAT model was calibrated and validated using observed runoff data from 2003 to 2014 for three stations (Sancheong, Shinan, Changchon) within the study watershed. The $R^2$ (Determination Coefficient), RMSE (Root Mean Square Error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (Relative Mean Absolute Error) were used to evaluate the model performance. Parameters for runoff calibration were selected based on user's manual and references and trial and error method was applied for parameter calibration. Calibration results showed that annual mean runoff were within ${\pm}5%$ error compared to observed. $R^2$ were ranged 0.64 ~ 0.75, RMSE were 2.51 ~ 4.97 mm/day, NSE were 0.48 ~ 0.65, and RMAE were 0.34 ~ 0.63 mm/day for daily runoff, respectively. The runoff comparison for three stations showed that annual runoff was higher in Changchon especially summer and winter seasons. The flow exceedance graph showed that Sancheong and Shinan stations were similar while Changchon was higher in entire fraction.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.58-58
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• 2011

The Texas Commission on Environmental Quality(TCEQ) WAM(Water Availability Modeling) System consists of the generalized Water Rights Analysis Package(WRAP) river/reservoir system water management simulation model, 22 sets of WRAP hydrology and water rights input files for the 23 river basins of Texas, geographic information system tools, and other supporting databases. The WRAP/WAM modeling system, as routinely applied since the late 1990s, has not included consideration of water quality. Recently developed WRAP-SALT(Water Rights Analysis Package) is designed primarily for computing concentration frequency statistics and supply reliability indices at locations of interest in a river system for alternative water development and management scenarios. Though motivated primarily by natural salt pollution, WRAP-SALT water quality modeling features are applicable to essentially any conservative water quality constituent. The Brazos River studies discussed in this paper focus on total dissolved solids, though the available observed data also includes chloride and sulfate which can be modeled as individual constituents. The WRAP-SALT salinity input file contains loads or concentrations of salinity inflows during each month of the hydrologic period-of-analysis and reservoir storage at the beginning of the simulation. The WRAP-SALT model computes salt loads and concentrations for each control point of a river/reservoir system for inflows and outflows during the month and end-of-month reservoir storage for each month of the hydrologic period-of-analysis, for given loads entering the system. River reaches connect control points. The mass balance algorithms proceed from upstream to downstream, with outflow from one river reach contributing to inflow to the next downstream reach. In a given month, for each control point in sequence, the inflow loads are first computed. Loads and concentrations of outflows and reservoir storage at the control point are then determined. Complete mixing during the month is assumed at locations without reservoir storage.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.1-8
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• 2019

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

• Steel and Composite Structures
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• v.46 no.4
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• pp.471-483
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• 2023

This paper presents an analytical hyperbolic theory based on the refined shear deformation theory for mechanical stability analysis of the simply supported advanced composites plates (exponentially, sigmoidal and power-law graded) under triangular, trapezoidal and uniform uniaxial and biaxial loading. The developed model ensures the boundary condition of the zero transverse stresses at the top and bottom surfaces without using the correction factor as first order shear deformation theory. The mathematical formulation of displacement contains only four unknowns in which the transverse deflection is divided to shear and bending components. The current study includes the effect of the geometric imperfection of the material. The modeling of the micro-void presence in the structure is based on the both true and apparent density formulas in which the porosity will be dense in the mid-plane and zero in the upper and lower surfaces (free surface) according to a logarithmic function. The analytical solutions of the uniaxial and biaxial critical buckling load are determined by solving the differential equilibrium equations of the system with the help of the Navier's method. The correctness and the effectiveness of the proposed HyRPT is confirmed by comparing the results with those found in the open literature which shows the high performance of this model to predict the stability characteristics of the FG structures employed in various fields. Several parametric analyses are performed to extract the most influenced parameters on the mechanical stability of this type of advanced composites plates.

• Journal of Korea Water Resources Association
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• v.42 no.5
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• pp.365-374
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• 2009

Probability plotting positions are used for the graphical display of annual maximum rainfall or flood series and the estimation of exceedance probability of those values. In addition, plotting positions allow a visual examination of the fitness of probability distribution provided by frequency analysis for a given data. Therefore, the graphical approach using plotting position has been applied to many fields of hydrology and water resources planning. In this study, the plotting position formula for the Gumbel distribution is derived by using the order statistics and the probability weight moment of the Gumbel distribution for various sample sizes. And then, the parameters of plotting position formula for the Gumbel distribution are estimated by using genetic algorithm. The appropriate plotting position formulas for the Gumbel distribution are examined by the comparison of root mean square errors and biases between theoretical reduced Gumbel variates and those calculated from derived and existing plotting position formulas. As the results, Gringorten's plotting position formula has the smaller root mean square errors and biases than any other formulas.