• Journal of Korean Society for Atmospheric Environment
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• v.5 no.1
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• pp.1-10
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• 1989

The sensitivity analysis is a method to quantify to what extent the output of a model changes with the values of input parameters. This will lead to increase model accuracy through measurement validation. Three line source air quality models, HIWAY 2, PAL, and CALINE 3 were selected for this study. The input parameters analysed included wind speed, wind direction, stability, emission rate, mixing height, receptor distance, initial dispersion coefficient, surface roughness, and averaging time. It turned out that PAL model generally showed higher concentration than other two models, and that between CALINE 3 and HIWAY 2, CALINE 3 showed higher concentration than HIWAY 2 model near the line sources, but beyond a certain downwind distances HIWAY 2 model showed higher concentration. The modesl were very sensitive to wind speed especially in the range of 0 $\sim$ 1 m/s and to wind direction near the parallel wind to streets. In case of emission rate, the output concentration was directly proportional to these input parameters. And the sensitivity of the input parameters such as stability, mixing height, initial dispersion coefficient, surface roughness, and averaging time were not very significant.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.368-370
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• 1998

The TPS accepts as input, vehicle parameters, control parameters, station parameters and right-of-way profile. Outputs of TPS program include velocity, elapsed time and power profiles. This paper represents how to develop the TPS program. The TPS program simulates the operation of a single train under the input conditions.

• Tunnel and Underground Space
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• v.25 no.3
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• pp.255-263
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• 2015

Explosive blasting is a very useful tool for mining and civil engineering applications. It, however, may cause severe environmental hazards on adjacent structures due to blasting impact. Blast engineers try to make optimum blast design to provide efficient performance and to minimize the environmental impact as well. It requires a pre-assessment of the impacts resulting from the blasting operation in design stage. One of the common procedures is to evaluate the proposed blast pattern through a series of test blasting in the field. Another approach is to evaluate the possible environmental effects using the numerical methods. There are a number of input parameters to be prepared for the numerical analysis. Some of them are well understood, while some are not. This paper presents some results of sensitivity analysis of the basic input parameters in numerical modelling of blasting problems so as to provide sound understanding of the parameters and some guidelines for input preparation.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.467-472
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• 2020

Methods for deriving design input-parameters to ensure the stability of a structure on a common ground are generally well known. Folded metamorphic rocks, such as the study area, are highly foliated and have small faults parallel to the foliation, resulting in special research methods and tests to derive design input parameters, Etc. are required. The metamorphic rock ground with foliation development of several mm intervals has a direct shear test on the foliation surface, the strike/dip mapping of the foliation, the boring investigation to determine the continuity of the foliation, and the rock mass rating of the metamorphic rock. etc. are required. In the case of a large number of small foliation-parallel faults developed along a specific foliation plane, it is essential to analyze the lineament, surface geologic mapping for fault tracing, and direct shear test. Folded ground requires additional geological-structural-domain analysis, discontinuity analysis of stereonet, electrical resistivity exploration along the fold axis, and so on.

• Korean Journal of Metals and Materials
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• v.56 no.11
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• pp.813-821
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• 2018

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.89-95
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• 1997

Welding technique is widely applied to general industry such as pressure vessel for chemical plant, pipe system, heavy industry, and automobile. There are some points which must be considered when robot system is used in welding automation process for productivity improvement. Welding quality is governed by heat input, and this quantity can be different according to shape, property, and thick of material . For desired heat input , weld input parameters such as welding voltage, current, and welding velocity must be determined with those consideration. Until now these parameters have been determined mainly by experience of operator. In this study, the size of welding zone was predicted by fuzzy rules were constructed from the relation between welding variables and weld pool size. Inverse model method which welding control input for welder is determined with optimum voltage and current by fuzzy controller is validatied by computer simulation.

• Journal of Welding and Joining
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• v.21 no.7
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• pp.42-48
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• 2003

Welding distortion is a current issue in many industrial parts, especially for heavy industry such as shipbuilding, plant industry. The welding process has many processing parameters influencing welding angular distortion such as heat input power, welding speed, gas flow rate, plate thickness and the welded material properties, etc. In this work, the conventional local minimization concept was applied to find a set of optimum welding process parameters, consisted of welding speed, plate thickness and heat input, for a minimum angular distortion. An analytic solution for welding angular distortion, which is based on laminated plate theory, was also applied to investigate and optimize the welding process parameters. The optimized process parameters and the angular distortion for various parametric conditions could be easily found by using the local minimum concept.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.135-143
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• 2006

The objective of this study is to develop PSCF (potential source contribution function) program and determine the optimal values of control parameters to enhance the prediction of PSCF modeling. This study provides an important information and methodologies that can be used to get better results of locating influencing sources, especially unknown and fugitive sources. To determine proper values of control parameters in PSCF model, the diagnostic assessment on the results obtained by the various input conditions was carried out. PSCF model has created and improved from version 1.0 to version 7.0 since 200 I and the measured data (at least > 100) of receptor, and the values of control input parameters should be arranged and determined to obtain reliable results in PSCF modeling. The size of modeling domain must be determined to include enough trajectories to get reliable results. And the size of grid is recommended to be 2.5 $\sim$ 5 degrees for global scale, 0.2 $\sim$ 1 degrees for regional scale and 0.05 degree for local scale.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2821-2835
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• 2020

The phenomenon identification and ranking table (PIRT) is an important basis in the nuclear power plant (NPP) thermal-hydraulic analysis. This study focuses on the importance ranking of the input parameters when lacking the PIRT, and the target scenario is the small break loss of coolant accident (SBLOCA) in a pressurized water reactor (PWR) CPR1000. A total of 54 input parameters which might have influence on the figure of merit (FOM) were identified, and the sensitivity measure of each input on the FOM was calculated through an optimized moment-independent global sensitivity analysis method. The importance ranking orders of the parameters were transformed into the Savage scores, and the parameters were categorized based on the Savage scores. A parameter importance ranking table for the SBLOCA scenario of the CPR1000 reactor was obtained, and the influences of some important parameters at different break sizes and different accident stages were analyzed.

• Journal of Radiation Protection and Research
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• v.25 no.4
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• pp.233-239
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• 2000

The sensitivity analysis of input parameters was Performed fer an ingestion dose assessment model (U. S. NRC's Regulatory Guide 1.109 model) from routine releases of radionuclides. In this study, three kinds of typical Korean foodstuffs (rice, leaff vegetables, milk) and two kinds of radionuclides $(^{l37}Cs,\;^{131}I)$ were considered. The values of input parameters were sampled using a Latin hypercube sampling technique based on Monte Carlo approach. Sensitivity indices, which represent the influence or the importance of input parameters for predictive results, were quantitatively expressed by the partial rank correlation coefficients. As the results, the ratio of the interception fraction to the yield of agricultural plants and the human consumption rate were sensitive input parameters for the considered foodstuffs and radionuclides. Additionally, in case of milk, the transfer factor of radionuclides from animal intake to milk and the daily intake rate of feedstuffs were sensitive input parameters. The weathering removal half-life and the delay time from food production to human consumption were relatively sensitive for $^{137}Cs$ and $^{131}I$ depositions, respectively.