• Journal of Environmental Science International
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• v.20 no.1
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• pp.25-34
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• 2011

The new empirical static model was constructed on the basis of dimension analysis to predict the pressure drop according to the operating conditions. The empirical static model consists of the initial pressure drop term (${\Delta}P_{initial}$) and the dust mass number term($N_{dust}=\frac{{\omega}_0{\nu}_f}{P_{pulse}t}$), and two parameters (dust deposit resistance and exponent of dust mass number) have been estimated from experimental data. The optimum injection distance was identified in the 64 experimental data at the fixed filtration velocity and pulse pressure. The dust deposit resistance ($K_d$), one of the empirical static model parameters got the minimum value at d=0.11m, at which the total pressure drop was minimized. The exponent of dust mass number was interpreted as the elasticity of pressure drop to the dust mass number. The elasticity of the unimodal behavior had also a maximum value at d=0.11m, at which the pressure drop increased most rapidly with the dust mass number. Additionally, the correlation coefficient for the new empirical static model was 0.914.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.439-446
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• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4159-4169
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• 2022

Hydrogen mitigation using Passive Autocatalytic Recombiners (PARs) has been widely accepted methodology inside reactor containment of accident struck Nuclear Power Plants. They reduce hydrogen concentration inside reactor containment by recombining it with oxygen from containment air on catalyst surfaces at ambient temperatures. Exothermic heat of reaction drives the product steam upwards, establishing natural convection around PAR, thus invoking homogenisation inside containment. CFD models resolving individual catalyst plate channels of PAR provide good insight about temperature and hydrogen recombination. But very thin catalyst plates compared to large dimensions of the enclosures involved result in intensive calculations. Hence, empirical correlations specific to PARs being modelled are often used in integral containment studies. In this work, an experimentally validated CFD model of PAR has been employed for developing an empirical correlation for Indian PAR. For this purpose, detailed parametric study involving different gas mixture variables at PAR inlet has been performed. For each case, respective values of gas mixture variables at recombiner outlet have been tabulated. The obtained data matrix has then been processed using regression analysis to obtain a set of correlations between inlet and outlet variables. The empirical correlation thus developed, can be easily plugged into commercially available CFD software.

• Korean Management Science Review
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• v.34 no.1
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• pp.71-84
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• 2017

The purpose of this study is to extract the characteristic cost through the time series analysis of each cost from 2003 to 2014, and to grasp the performance and relevance of the enterprise. Therefore, in this section, we analyzed the time-series analysis of selling, administrative, and non-operating expenses as described above. First, depreciation cost, advertising cost, transportation cost, research cost, current research cost, and ordinary development cost were extracted as the variables of interest to be verified in the empirical analysis. However, in the analysis of non-operating expenses, we could not extract the specific cost, but we could grasp the time-series flow of cost data before and after two epochs such as financial crisis and introduction of IFRS obligation. The results of this study show that sales management costs have a positive (+) effect on firm value. Empirical analysis confirms that management is trying to increase or decrease the cost This can be confirmed by the empirical results of this paper. At present, general enterprise accounting is done through ERP system. However, since the ERP system does not have an analysis system for each sales and management cost, the current system has difficulty in knowing the budget item for each cost each time the expenditure resolution for each cost item is made, It is a reality that the expenditure plan must be managed separately and it is inconvenient to keep it. However, if this practical difficulty is solved by the cost analysis system such as sales management cost, the present accounting information system will be further developed. Furthermore, the management will increase the profit item It is thought that coordination actions can also be prevented in advance.

• Journal of Distribution Science
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• v.14 no.9
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• pp.121-132
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• 2016

Purpose - This study has the following objectives. First, it seeks to build an integrated model that can analyze the path through which headquarters, subsidiary competence, and knowledge transfer performance influence subsidiary performance. Second, it analyzes the influence of the specificity of technology as a moderating effect factor on knowledge transfer process. Third, it will conduct an empirical analysis on distributive manufacturing MNEs and suggests an implication for companies that actually need technological localization. The difference of this study are as follows. First, unlike the existing studies, this study can expand a theoretical discussion as it uses subsidiary performance as the dependent variable. Second, it sets the specificity of technology as a moderating effect factor, not an antecedent, and can draw a theoretical implication. Research design, data, and methodology - This study built a path analysis model to identify the influence of the disseminative capacity and absorptive capacity of distributive manufacturing MNEs on subsidiary performance. Based on the previous studies, it set 19 items as 5 latent variables, and established 6 hypotheses by including the moderating effect of the specificity of technology between them. The final 203 companies were selected as analysis samples through a survey questionnaire. For empirical analysis, the study used PLS (Partial Least Square) that is based on structural equation model. Results - The empirical analysis result demonstrated that both headquarters' disseminative capability and subsidiaries absorptive capacity had a positive influence on knowledge transfer performance. Knowledge transfer performance also had a positive influence on subsidiary performance. In the analysis of moderating effect, the specificity of technology acted as a significant moderating variable only between knowledge transfer performance and subsidiary performance; it did not show a statistically significant moderating effect among disseminative capability, absorptive capacity, and knowledge transfer performance. Conclusions - The empirical analysis results of this study demonstrate the importance of disseminative capability and absorptive capacity in knowledge transfer to subsidiaries from the distributive manufacturing MNEs in Korea. The analysis on the moderating effect indicates that the specificity of technology in Korean companies influences on the process of making achievement by using the transferred knowledge.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.21-29
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• 2018

In many companies handling flammable liquids, explosion-proof electrical equipment have been installed according to the Korean Industrial Standards (KS C IEC 60079-10-1). In these standards, hazardous area for explosive gas atmospheres has to be classified by the evaluation of the evaporation rate of flammable liquid leakage. The evaporation rate is an important factor to determine the zones classification and hazardous area distance. However, there is no systematic method or rule for the estimation of evaporation rate in these standards and the first principle equations of a evaporation rate are very difficult. Thus, it is really hard for industrial workplaces to employ these equations. Thus, this problem can trigger inaccurate results for evaluating evaporation range. In this study, empirical models for estimating an evaporation rate of flammable liquid have been developed to tackle this problem. Throughout the sensitivity analysis of the first principle equations, it can be found that main factors for the evaporation rate are wind speed and temperature and empirical models have to be nonlinear. Polynomial regression is employed to build empirical models. Methanol, benzene, para-xylene and toluene are selected as case studies to verify the accuracy of empirical models.

• Advances in concrete construction
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• v.5 no.5
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• pp.493-512
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• 2017

Blast loads may considerably affect the response of structures. In previous years, before computer analysis programs, the parameters of blast effects were calculated with empirical methods, consequently some researchers had proposed equations to find out the phenomenon. In recent year's computer analysis programs have developed already, so detailed solutions can be made numerically. This paper describes the blasting response of the structures using numerical and empirical methods. For the purpose, a reinforced concrete retaining wall is modelled using ANSYS Workbench software, and the model is imported to ANSYS AUTODYN software to perform explicit analyses. In AUTDYN software, a sum of TNT explosive is defined 5,5 m away from the wall and solution is done. Numerical results are compared with those of obtained from empirical equations. Similar study is also considered for equal explosive which is the 4 m away from the wall. The results are represented by graphics and contour diagrams of such as displacements and pressures. The results showed that distance of explosive away from the wall is highly affected the structural response of it.

• Journal of Korean Association for Spatial Structures
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• v.19 no.2
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• pp.101-108
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• 2019

In this paper, the characteristic of intrinsic mode function(IMF) and its orthogonalization of ensemble empirical mode decomposition(EEMD), which is often used in the analysis of the non-linear or non-stationary signal, has been studied. In the decomposition process, the orthogonal IMF of EEMD was obtained by applying the Gram-Schmidt(G-S) orthogonalization method, and was compared with the IMF of orthogonal EMD(OEMD). Two signals for comparison analysis are adopted as the analytical test function and El Centro seismic wave. These target signals were compared by calculating the index of orthogonality(IO) and the spectral energy of the IMF. As a result of the analysis, an IMF with a high IO was obtained by GSO method, and the orthogonal EEMD using white noise was decomposed into orthogonal IMF with energy closer to the original signal than conventional OEMD.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.89-96
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• 2015

In this paper, time-frequency analysis algorithms, empirical mode decomposition and local mean decomposition, are reviewed and their applications to nonlinear and nonstationary real-world data are discussed. In addition, their generic extensions to complex domain are addressed for the analysis of multichannel data. Simulations of these algorithms on synthetic data illustrate the fundamental structure of the algorithms and how they are designed for the analysis of nonlinear and nonstationary data. Applications of the complex version of the algorithms to the synthetic data also demonstrate the benefit of the algorithms for the accurate frequency decomposition of multichannel data.

• Ocean Systems Engineering
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• v.2 no.2
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• pp.137-145
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• 2012

Recent developments in underwater image recognition methods have received large attention by the ocean engineering researchers. In this paper, an improved bi-dimensional empirical mode decomposition (BEMD) approach is employed to decompose the given underwater image into intrinsic mode functions (IMFs) and residual. We developed a joint algorithm based on BEMD and Canny operator to extract multi-pixel edge features at multiple scales in IMFs sub-images. So the multiple pixel edge extraction is an advantage of our approach; the other contribution of this method is the realization of the bi-dimensional sifting process, which is realized utilizing regional-based operators to detect local extreme points and constructing radial basis function for curve surface interpolation. The performance of the multi-pixel edge extraction algorithm for processing underwater image is demonstrated in the contrast experiment with both the proposed method and the phase congruency edge detection.