• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.5
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• pp.364-374
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• 2003

The performance of an acoustic transducer is determined by the effects of many design variables. and mostly the influences of these design variables are not linearly independent of each other To achieve the optimal performance of an acoustic transducer, we must consider the cross-coupled effects of the design variables. In this study with the FEM. we analyzed the variation of the resonance frequency and sound pressure of a flextensional transducer in relation to Its design variables. Through statistical multiple regression analysis of the results, we derived functional forms of the resonance frequency and sound pressure in terms of the design variables, and with which we determined the optimal structure of the transducer by means of a constrained optimization technique, SQP-PD. The proposed method can reflect all the cross-coupled effects of multiple design variables, and can be utilized to the design of general acoustic transducers.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.631-652
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• 2023

In the two-condition within-subject mediation design, pairs of variables such as mediator and outcome are observed under two treatment conditions. The main objective of the design is to investigate the indirect effects of the condition difference (sum) on the outcome difference (sum) through the mediator difference (sum) for comparison of two treatment conditions. The natural condition variables mean the original variables, while the rotated condition variables mean the difference and the sum of two natural variables. The outcome difference (sum) is expressed as a linear model regressed on two natural (rotated) mediators as a parallel two-mediator design in two condition approaches: the natural condition approach uses regressors as the natural condition variables, while the rotated condition approach uses regressors as the rotated condition variables. In each condition approach, the total indirect effect on the outcome difference (sum) can be expressed as the sum of two individual indirect effects: within- and cross-condition indirect effects. The total indirect effects on the outcome difference (sum) for both condition approaches are the same. The invariance of the total indirect effect makes it possible to analyze the nature of two pairs of individual indirect effects induced from the natural conditions and the rotated conditions. The two-condition within-subject design is extended to the addition of a between-subject moderator. Probing of the conditional indirect effects given the moderator values is implemented by plotting the bootstrap confidence intervals of indirect effects against the moderator values. The expected indirect effect with respect to the moderator is derived to provide the overall effect of moderator on the indirect effect. The model coefficients are estimated by the structural equation modeling approach and their statistical significance is tested using the bias-corrected bootstrap confidence intervals. All procedures are evaluated using function lavaan() of package {lavaan} in R.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.21 no.12
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• pp.1063-1070
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• 2008

The performance of an acoustic transducer is determined by the effects of many design variables, and mostly the influences of these design variables are not linearly independent of each other. To achieve the optimal performance of an acoustic transducer, we must consider the cross-coupled effects of the design variables. In this study, the variation of the performances of underwater acoustic transducer in relation to its structural variables was analyzed. In addition, the new optimal design scheme of an acoustic transducer that could reflect not only individual but also all the cross-coupled effects of multiple structural variables, and could determine the detailed geometry of the transducer with great efficiency and rapidity was developed. The validation of the new optimal design scheme was verified by applying the optimal structure design of a flextensional transducer which are the most common use for high power underwater acoustic transducer. With the finite element analysis(FEA), we analyzed the variation of the resonance frequency, sound pressure, and working depth of a flextensional transducer in relation to its design variables. Through statistical multiple regression analysis of the results, we derived functional forms of the resonance frequency, sound pressure, and working depth in terms of the design variables. By applying the constrained optimization technique, Sequential Quadratic Programming Method of Phenichny and Danilin(SQP-PD), to the derived function, we designed and verified the optimal structure of the Class IV flextensional transducer that could provide the highest sound pressure level and highest working depth at a given operation frequency of 1 kHz.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1449-1454
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• 2009

The performance variation of a multibody system is affected by a variation of various design variables of the system. And the effects of design variable variations on the performance variation must be considered in design of a multibody system. Accordingly, a variation analysis of a multibody system needs to be conducted in design of a multibody system. For a variation analysis of a performance, population mean and variance which are called statistical parameters of design variables are needed. However, an evaluation of statistical parameters of design variables is impossible in many practical cases. Therefore, an estimation of statistical parameters of the performance based on sample mean and variance which are called statistic of design variables is needed. In this paper, the variation analysis method for a multibody system based on design variable samples was proposed. And, using the proposed method, a variation analysis of the vehicle ride comfort based on sample statistic of design variables was conducted.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.380-385
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• 2001

The paper presents a new multidisciplinary design optimization architecture using optimal sensitivity and coordination of interdisciplinary design variables. Original design problem is decomposed into a number of sub-problems that represent individual engineering analysis. The coupled effects between sub-problems are computed by interdisciplinary design variables. System level coordination is determined by optimal parameter sensitivity calculated by finite difference method. The proposed. MDO strategy is applied to a simplified model of rotorcraft blade design associated with structures and aerodynamic disciplines.

• The Research Journal of the Costume Culture
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• v.12 no.3
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• pp.379-391
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• 2004

The purpose of this study was to examine the effects of uniform's color and motif on nurse's impression formation. The experimental design was 5×3×2(uniform color×motif ×perceiver's gender) factorial design with a between-subjects design. The experimental materials developed for the study were a set of stimuli and a response scale. The subjects were 738 undergraduate students of Daejon and Chungnam province. The SPSS package was used for data analysis which includes factor analysis, two-way ANOVA, Duncan's multiple range test, and Cronbach's α to measure the reliability. Results were as follows; The image or the stimulus was consisted of the 4 different dimensions(evaluation, sociability, ability, potency). All the independent variables showed some significant impression effects on selected dimensions. The motif and perceiver's gender also showed significant main effects as well as some interaction effects with the color variable on some selected impression dimension and the impression effects of the three variables in relationship to perceiving nurses' images. On a conclusion, these results supported the Gestalt theory.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.780-787
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• 2017

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

• Journal of Power System Engineering
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• v.15 no.4
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• pp.11-18
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• 2011

A Shock absorber is one of the most important components in vehicle suspension systems. In general, many repeated analyses are required for the design of a shock absorber to satisfy the suspension characteristics of a specific automobile, like fluid flow analysis and mechanical analysis. The purpose of this study is to develop a fast design tool for shock absorber designers. One of the efficient solutions for this can be an empirical design method considering phenomenological effects from the shock absorber design variables. In order to extract the shock absorber's experimental characteristics, we used Taguchi method. This method showed that which design variables have major effects for the shock absorber's damping characteristics. This empirical design method also showed the direction of the design changes to satisfy the designer's intension.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1496-1506
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• 2003

The SIMP (solid isotropic material with penalization) approach is perhaps the most popular density variable relaxation method in topology optimization. This method has been very successful in many applications, but the optimization solution convergence can be improved when new variables, not the direct density variables, are used as the design variables. In this work, we newly propose S-shape functions mapping the original density variables nonlinearly to new design variables. The main role of S-shape function is to push intermediate densities to either lower or upper bounds. In particular, this method works well with nonlinear mathematical programming methods. A method of feasible directions is chosen as a nonlinear mathematical programming method in order to show the effects of the S-shape scaling function on the solution convergence.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.