• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.5B
• /
• pp.439-446
• /
• 2006

In the present study, we applied wavelet transform to decompose the hydro-meteorological data such as precipitation and temperature into the components with different return periods with a primary objective for extraction of nonlinear dynamical component. For the transform, we used the Daubechies wavelet of order 9 ('db9') as a basis function. Also, we applied the correlation dimension analysis to determine whether or not the detail and approximation components at the respective decomposition stage with the increasing of scale in the wavelet transform reveal the nonlinear dynamical characteristics. In other words, we proposed the combined use of the wavelet transform and the correlation dimension analysis as methodology to extract the nonlinear dynamical component from the hydro-meteorological data. The derived result has shown the method proposed in the present study is suitable for the segregation and extraction of the nonlinear dynamical component which is, in general, difficult to reveal by using the raw data.

• Steel and Composite Structures
• /
• v.44 no.5
• /
• pp.707-720
• /
• 2022

In the present work, bending and free vibration analyses of multilayered functionally graded (FG) graphene platelet (GPL) and fiber-reinforced hybrid composite beams are carried out using the parabolic function based shear deformation theory. Parabolic variation of transverse shear stress across the thickness of beam and transverse shear stress-free conditions at top and bottom surfaces of the beam are considered, and the proposed formulation incorporates a transverse displacement field. The present theory works only with four unknowns and is computationally efficient. Hamilton's principle has been employed for deriving the governing equations. Analytical solutions are obtained for both the bending and free vibration problems in the present work considering different variations of GPLs and fibers distribution, namely, FG-X, FG-U, FG-Λ, and FG-O for beams having simply-supported boundary condition. First, the matrix is assumed to be strengthened using GPLs, and then the fibers are embedded. Multiscale modeling for material properties of functionally graded graphene platelet/fiber hybrid composites (FG-GPL/FHRC) is performed using Halpin-Tsai micromechanical model. The study reveals that the distributions of GPLs and fibers have significant impacts on the stresses, deflections, and natural frequencies of the beam. The number of layers and shape factors widely affect the behavior of FG-GPL-FHRC beams. The multilayered FG-GPL-FHRC beams turn out to be a good approximation to the FG beams without exhibiting the stress-channeling effects.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.6
• /
• pp.33-39
• /
• 2023

Recently, reinforcement learning has been used to improve the communication performance of flying ad-hoc networks (FANETs) and to design mobility models. Mobility model is a key factor for predicting and controlling the movement of unmmaned aerial vehicle (UAVs). In this paper, we designed and analyzed the performance of Q-learning with fourier basis function approximation and Deep-Q Network (DQN) models for optimal path finding in a three-dimensional virtual environment where UAVs operate. The experimental results show that the DQN model is more suitable for optimal path finding than the Q-learning model in a three-dimensional virtual environment.

• The Journal of the Acoustical Society of Korea
• /
• v.27 no.3
• /
• pp.119-128
• /
• 2008

This paper analyzes the relationship between two representative statistical methods for bandwidth extension (BWE): Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) ones, and compares their performances. The HMM method is a memory-based system which was developed to take advantage of the inter-frame dependency of speech signals. Therefore, it could be expected to estimate better the transitional information of the original spectra from frame to frame. To verify it, a dynamic measure that is an approximation of the 1st-order derivative of spectral function over time was introduced in addition to a static measure. The comparison result shows that the two methods are similar in the static measure, while, in the dynamic measure, the HMM method outperforms explicitly the GMM one. Moreover, this difference increases in proportion to the number of states of HMM model. This indicates that the HMM method would be more appropriate at least for the 'blind BWE' problem. On the other hand, nevertheless, the GMM method could be treated as a preferable alternative of the HMM one in some applications where the static performance and algorithm complexity are critical.

• Journal of Distribution Science
• /
• v.12 no.2
• /
• pp.49-57
• /
• 2014

Purpose - Theorists and researchers in the area of customer satisfaction generally agree that product satisfaction intervenes between expectancy disconfirmation and various post-purchase cognitive states including attitude and behavioral intention. Studies in a variety of settings have supported the effect of expectation and its disconfirmation on satisfaction, but only a small number of studies address the cognitive consequences of satisfaction decisions and none report data on choice processes such as brand selection. This study examines the influence of satisfaction and its determinants on behavioral intention and product preference in eight coffee shops across the country. Generally it was found in both overall and summed attribute analyses that satisfaction was a function of expectation and disconfirmation, that intention was a function of satisfaction, and that preference was influenced by satisfaction and disconfirmation, the latter having the greater effect. Research design, data, and methodology - The main objective of this study was to assess the dimensions of consumer selection and satisfaction in choosing a coffee shop. In order to achieve this objective, a study of coffee shops across the country was conducted. This study comprised in-depth questionnaires distributed to coffee shop customers. A survey was conducted from September 1, 2011 to September 30, 2011, involving franchise coffee shop, independently owned coffee shop, and roastery coffee shop customers. Results - Hypothesis 1-1, which states that coffee shop choice attributes differ based on the type of coffee shop, is accepted. It has a significance level of 0.05, according to choosing properties of coffee shop by convenience of transportation, varieties of beans, residence of the owner (manager), information, and relationships. Hypothesis 1-2, which states that satisfaction with the choice factor differs depending on the type of coffee shops, is accepted. The P-values for cleanliness and varieties of beans were 0.04 and 0.00, respectively, and have a significance level of 0.05, according to the satisfaction with the chosen coffee shop. Hypothesis 2-1, which states that the importance of the choice attributes in coffee shop selections differs based on the demographic characteristics of the customers, is accepted. According to the t-test result, convenience of parking and residence of the owner (manager) are significant. Hypothesis 2-2, which states that satisfaction with the choice factor will differ depending of the type of coffee shop, is accepted. According to the t-test result, convenience of parking and residence of the owner (manager) are significant. Conclusions - This study has shown that intention to revisit a certain shop is most likely correlated to satisfaction in all cases. In order to offer subsequent developments for coffee shops, this study also identifies relations between customer satisfaction and selection by finding significant factors. In order to maximize customers' satisfaction, coffee shops should analyze and satisfy customers' needs and wants in terms of coffee service. While the findings do not generalize beyond the mall sampling procedure used here, we have hopefully identified a close approximation of the process of satisfaction decisions used by consumers generally.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.6
• /
• pp.1229-1244
• /
• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.4
• /
• pp.270-278
• /
• 2020

In this paper, sound transmission of Micro-Perforated Plates (MPPs) installed in an impedance tube with a circular cross-section is described using an analytic method. Vibration of the plates is expressed in terms of an infinite series of modal functions, where modal function in the radial direction is given by the Bessel function. Under the plane wave assumption, a low frequency approximation is derived, and a formula for the sound transmission coefficient of multi-layered MPPs is presented using the transfer matrix method. The Sound Transmission Losses (STLs) of single and double MPPs are computed using the proposed method and compared with those done by the Finite Element Method (FEM), which shows an excellent agreement. As the perforation increases, the STL is degraded, since the STL becomes dominated by the perforation ratio rather than by vibration of the plate. The STL shows dips at natural frequencies as well as at the mass-spring-mass resonance frequency. The proposed model for the STL prediction in this study can be applied to an arbitrary number of MPPs, where each MPP may or may not have a perforation.

• Journal of the Korea Convergence Society
• /
• v.12 no.3
• /
• pp.187-196
• /
• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.13 no.2
• /
• pp.73-84
• /
• 1993

In this research, configuration design optimization of plane truss structure has been tested by using decomposition technique. In the first level, the problem of transferring the nonlinear programming problem to linear programming problem has been effectively solved and the number of the structural analysis necessary for doing the sensitivity analysis can be decreased by developing stress constraint into member stress approximation according to the design space approach which has been proved to be efficient to the sensitivity analysis. And the weight function has been adopted as cost function in order to minimize structures. For the design constraint, allowable stress, buckling stress, displacement constraint under multi-condition and upper and lower constraints of the design variable are considered. In the second level, the nodal point coordinates of the truss structure are used as coordinating variable and the objective function has been taken as the weight function. By treating the nodal point coordinates as design variable, unconstrained optimal design problems are easy to solve. The decomposition method which optimize the section areas in the first level and optimize configuration variables in the second level was applied to the plane truss structures. The numerical comparisons with results which are obtained from numerical test for several truss structures with various shapes and any design criteria show that convergence rate is very fast regardless of constraint types and configuration of truss structures. And the optimal configuration of the truss structures obtained in this study is almost the identical one from other results. The total weight couldbe decreased by 5.4% - 15.4% when optimal configuration was accomplished, though there is some difference.

• Investigative Magnetic Resonance Imaging
• /
• v.7 no.1
• /
• pp.39-46
• /
• 2003

Purpose : To develop a theoretical model for magnetic relaxation behavior of the superparamagnetic nano-particle agent, which demonstrates multi-functionality such as liver- and lymp node-specificity. Based on the developed model, the computer simulation was performed to clarify the relationship between relaxation time and the applied magnetic field strength. Materials and Methods : The ultrasmall superparamagnetic iron oxide (USPIO) was encapsulated with biocompatiable polymer, to develop a relaxation model based on outsphere mechanism, which was resulting from diffusion and/or electron spin fluctuation. In addition, Brillouin function was introduced to describe the full magnetization by considering the fact that the low-field approximation, which was adapted in paramagnetic case, is no longer valid. The developed model describes therefore the T1 and T2 relaxation behavior of superparamagnetic iron oxide both in low-field and in high-field. Based on our model, the computer simulation was performed to test the relaxation behavior of superparamagnetic contrast agent over various magnetic fields using MathCad (MathCad, U.S.A.), a symbolic computation software. Results : For T1 and T2 magnetic relaxation characteristics of ultrasmall superparamagnetic iron oxide, the theoretical model showed that at low field (<1.0 Mhz), $\tau_{S1}(\tau_{S2}$, in case of T2), which is a correlation time in spectral density function, plays a major role. This suggests that realignment of nano-magnetic particles is most important at low magnetic field. On the other hand, at high field, $\tau$, which is another correlation time in spectral density function, plays a major role. Since $\tau$ is closely related to particle size, this suggests that the difference in R1 and R2 over particle sizes, at high field, is resulting not from the realignment of particles but from the particle size itself. Within normal body temperature region, the temperature dependence of T1 and T2 relaxation time showed that there is no change in T1 and T2 relaxation times at high field. Especially, T1 showed less temperature dependence compared to T2. Conclusion : We developed a theoretical model of r magnetic relaxation behavior of ultrasmall superparamagnetic iron oxide (USPIO), which was reported to show clinical multi-functionality by utilizing physical properties of nano-magnetic particle. In addition, based on the developed model, the computer simulation was performed to investigate the relationship between relaxation time of USPIO and the applied magnetic field strength.