• Journal of Environmental Impact Assessment
• /
• v.22 no.6
• /
• pp.713-724
• /
• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• Journal of computational fluids engineering
• /
• v.19 no.1
• /
• pp.64-72
• /
• 2014

This study aimed at assessing the analysis capability of thermal-hydraulic computer code, MARS for the behaviors of the core make-up tank (CMT). The sensitivity study on the nodalization to simulate the CMT was conducted, and the MARS calculations were compared with KAIST experimental data and RELAP5/MOD3.3 calculations. The 12-node model was fixed through a nodalization study to investigate the effect of the number of nodes in the CMT (2-, 4-, 8-, 12-, 16-node). The sensitivity studies on various parameters, such as water subcooling of the CMT, steam pressure, and natural circulation flow were done. MARS calculations were reasonable in the injection time and the effects of several parameters on the CMT behaviors even though the mesh-dependency should be properly treated for reactor applications.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.12 no.2
• /
• pp.217-227
• /
• 1992

The first and second-order reliability method(FORM and SORM) is presented using one dimensional finite difference and two dimensional finite element transport models. FORM and SORM can be used without any restrictive assumptions about the properties of the media, and the sensitivity information obtained as part of these analyses is used to identify the parameters which have major influence on the estimate of probability. The reliability analysis of transport in a one-dimensional domain is used to test the robustness of the reliability code and to evaluate the accuracy of the reliability method. A continuous source 2-D example with a concentration threshold limit state function is used to evaluate the influence of the parameters in the location of interest on the reliability solution.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.50 no.3
• /
• pp.144-148
• /
• 2001

For the purpose of developing capacitive humidity sensor having interdigit electrodes, interdigit electrode was modeled and simulated to obtain capacitance and sensitivity as a function of geometric parameters like the structural gap and thichness. For the development of ASIC, switched capacitor signal conditioning circuits for capacitive humidity sensor were designed and simulated by cadence using 0.25um CMOS process parameters. The signal conditioning circuits are composed of amplifier for voltage gain control, and clock generator for sensor driving and switch control The characteristics of the fabricated sensors are; 1) sensitivity is 9fF/%R.H., 2) temperature coefficient of offset(TCO) is 0.4%R.H./$^{\circ}C$, 3) nonlinearity is 1.2%FS, 4) hysteresis is 1.5%FS in humidity range of 3%R.H. ${\sim}$ 98%R.H.. The response time is 50 seconds in adsorption and 70 seconds in desorption. Fabricated process used in this capacitive humidity sensor having interdigit electrode are just as similar as conventional IC process technology. Therefore this can be easily mass produced with low cost, simple circuit and utilized in many applications for both industrial and environmental measurement and control system, such as monitoring system of environment, automobile, displayer, IC process room, and laboratory etc..

• Journal of Korea Water Resources Association
• /
• v.31 no.6
• /
• pp.695-708
• /
• 1998

Recently, it seems increasing trends that the design floods on the middle and small scale of urban regions and natural basins are evaluated with introducing to the concept of the critical storm duration. However the study of the critical storm duration is not sufficient and especially on the natural basins, it rarely performed. therefore in this study, estimated the critical storm duration and peak discharge according to the rainfall distribution type, the position of peak rainfall intensity, and the frequency on the natural basins were evaluated using Clark model and the influence of each factors on the design floods was analyzed with sensitivity analysis on the parameters of the model.

• Computers and Concrete
• /
• v.15 no.2
• /
• pp.167-181
• /
• 2015

"Mortar or concrete pneumatically projected at high velocity onto a surface" is called Shotcrete. Models that predict shotcrete design parameters (e.g. compressive strength, slump etc) from any mixing proportions of admixtures could save considerable experimentation time consumed during trial and error based procedures. Artificial Neural Network (ANN) has been widely used for similar purposes; however, such models have been rarely applied on shotcrete design. In this study 19 samples of shotcrete test panels with varying quantities of water, steel fibers and silica fume were used to determine their slump, cost and compressive strength at different ages. A number of 3-layer Back propagation Neural Network (BPNN) models of different network architectures were used to train the network using 15 samples, while 4 samples were randomly chosen to validate the model. The predicted compressive strength from linear regression lacked accuracy with $R^2$ value of 0.36. Whereas, outputs from 3-5-3 ANN architecture gave higher correlations of $R^2$ = 0.99, 0.95 and 0.98 for compressive strength, cost and slump parameters of the training data and corresponding $R^2$ values of 0.99, 0.99 and 0.90 for the validation dataset. Sensitivity analysis of output variables using ANN can unfold the nonlinear cause and effect relationship for otherwise obscure ANN model.

• Environmental Engineering Research
• /
• v.25 no.3
• /
• pp.418-425
• /
• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.8 no.2
• /
• pp.149-158
• /
• 2008

The parameters of each basin, required for the accurate analysis of flood runoff using Storage Function Model, are estimated. Prior to the estimation, sensitivity analysis and extraction of new regional topographic factors for Han River basin are conducted. Based on the result, the outflow constant of basin model is calculated through regression analysis in relation with pre-flood runoff depth. The storage constant of basin model is derived by the optimum storage constant equation, according to the flood event of each basin. The model using the mentioned parameters was compared with K-Water model of Korea Water Resources Corporation and the model of Han River Flood Control Office, and proved to correspond to the observed hydrograph more.

• Proceedings of the Korea Contents Association Conference
• /
• 2008.05a
• /
• pp.788-791
• /
• 2008

On this study, following works have been carried out : division of Han River Basin into 24 sub basins, use of rainfall data of 151 stations to make spatial distribution of rainfall, selection of control points such as Soyanggang Dam, Chungju Dam, Chungju Release Control Dam, Heongseong Dam, Hwachun Dam, Chuncheon Dam, Uiam Dam, Cheongpyung Dam and Paldang Dam, selection of SSARR (Streamflow Synthesis and Reservoir Regulation) model as a hydrologic model, preparation of input data of SSARR model, sensitivity analysis of parameter using hydrologic data of 2002. The sensitivity analysis showed that soil moisture index versus runoff percent (SMI-ROP), baseflow infiltration index versus baseflow percent (BII-BFP) and surface-subsurface separation (S-SS) parameters are higher sensitive parameters to the simulation result.

• Korean Journal of Remote Sensing
• /
• v.25 no.6
• /
• pp.487-499
• /
• 2009

Atmospheric correction of satellite measurements is a major step to estimate accurate surface reflectance of solar spectrum channels. In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model used to retrieve surface reflectance from MODIS (MODerate resolution Imaging Spectrometer) top of atmosphere (TOA) reflectance. It is fast and simple atmospheric correction method, so it uses for work site operation in various satellite. This study attempts a test of accuracy of SMAC through a sensitivity test to detected error sources and to improve accuracy of surface reflectance using SMAC. The results of SMAC as compared with MODIS surface reflectance (MOD09) was represented that low accuracy ($R^2\;=\;0.6196$, Root Means Square Error (RMSE) = 0.00031, bias = - 0.0859). Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Among the input parameters, Aerosol Optical Depth (AOD) is the most influence input parameter. In order to modify AOD term in SMAC code, Stepwise multiple regression was performed with testing and remove variable in three stages with independent variables of AOD at 550nm, solar zenith angle, viewing zenith angle. Surface reflectance estimation by using Newly proposed AOD term in the study showed that improve accuracy ($R^2\;=\;0.827$, RMSE = 0.00672, bias = - 0.000762).