• Proceedings of the KSME Conference
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• 2001.06c
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• pp.408-413
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• 2001

The structural optimization is carried out in the continuous design space or discrete design space. Methods for discrete variables such as genetic algorithms are extremely expensive in computational cost. In this research, an iterative optimization algorithm using orthogonal arrays is developed for design in discrete space. An orthogonal array is selected on a discrete design space and levels are selected from candidate values. Matrix experiments with the orthogonal array are conducted. New results of matrix experiments are obtained with penalty functions for constraints. A new design is determined from analysis of means(ANOM). An orthogonal array is defined around the new values and matrix experiments are conducted. The final optimum design is found from iterative process. The suggested algorithm has been applied to various problems such as truss and frame type structures. The results are compared with those from a genetic algorithm and discussed.

• Journal of Communications and Networks
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• v.5 no.4
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• pp.328-343
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• 2003

Orthogonal pulse-shape modulation using Hermite pulses for ultra-wideband communications is reviewed. Closedform expressions of cross-correlations among Hermite pulses and their corresponding transmit and receive waveforms are provided. These show that the pulses lose orthogonality at the receiver in the presence of differentiating antennas. Using these expressions, an algebraic model is established based on the projections of distorted receive waveforms onto the orthonormal basis given by the set of normalized orthogonal Hermite pulses. Using this new matrix model, a number of pulse-shape modulation schemes are analyzed and a novel orthogonal design is proposed. In the proposed orthogonal design, transmit waveforms are constructed as combinations of elementary Hermites with weighting coefficients derived by employing the Gram-Schmidt (QR) factorization of the differentiating distortion model’s matrix. The design ensures orthogonality of the vectors at the output of the receiver bank of correlators, without requiring compensation for the distortion introduced by the antennas. In addition, a new set of elementary Hermite Pulses is proposed which further enhances the performance of the new design while enabling a simplified hardware implementation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.786-792
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• 2006

The design of experiment(DOE) with orthogonal arrays is adopted when the engineering design is needed in a discrete design space. In this research, a design process with orthogonal array is peformed to obtain the optimum design which satisfy the frequency target of the steering system. The optimum design is determined from the analysis of means(ANOM) and sensitivity information about design variables is evaluated by the analysis of variance(ANOVA). Interactions between design variables are investigated to achieve additivity which should be valid in using orthogonal array. It is shown that when strong interactions exist, the DOE process with orthogonal array considering interaction is introduced to find out optimum design.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.7
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• pp.885-895
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• 2004

Generally, automatic design is carried out with computer simulation and the simulation models are established by investigating the correlations between the simulation and real experiments. Therefore, the experiment results are utilized as complimentary data although they are considered to be precise. Orthogonal arrays have been adopted for discrete design. A method is proposed to directly exploit the experiment results in the design process with orthogonal arrays. Experiments are allocated to some rows of an orthogonal array and computer simulations are allocated to the others. A rule for the allocation is found to keep the orthogonality. Error analysis of the design results is performed. Mathematical examples are made to verify the validity of the proposed method. Error models are defined with the examples and the design solutions from the examples are discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1621-1626
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• 2001

The structural optimization have been carried out in the continuous design space or in the discrete design space. Methods fur discrete variables such as genetic algorithms , are extremely expensive in computational cost. In this research, an iterative optimization algorithm using orthogonal arrays is developed for design in discrete space. An orthogonal array is selected on a discrete des inn space and levels are selected from candidate values. Matrix experiments with the orthogonal array are conducted. New results of matrix experiments are obtained with penalty functions leer constraints. A new design is determined from analysis of means(ANOM). An orthogonal array is defined around the new values and matrix experiments are conducted. The final optimum design is found from iterative process. The suggested algorithm has been applied to various problems such as truss and frame type structures. The results are compared with those from a genetic algorithm and discussed.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.138-146
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• 2020

The paper deals with comparative study of characteristics of surrogate models and sensitivity evaluation using design of experiments in order to enhance and analysis the structure design of an automatic salt collector under various design load conditions. Orthogonal array design based on numerical analysis was used for the design of experiments. The thickness sizing variables of main structure member were considered the design factors, and the output responses were selected from the strength performances as well as the weight. The quantitative effects on responses for each design factor were evaluated from the orthogonal array experiment. Optimum design case was also identified to improve the strength performances with weight minimization. Using the orthogonal array experiment. various surrogate models such as response surface model, Kriging model, and Chebyshev orthogonal polynomial were generated. The orthogonal array experiment results were validated by the surrogate modeling results. The most suitable surrogate model was the response surface model for the exploration of design space of the automatic salt collector.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.85-95
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• 2021

Structure design sensitivity was evaluated using the orthogonal array experimental method for passive-type deck support frame (DSF) developed for float-over installation of the offshore plant. Moreover, approximation characteristics were also reviewed based on various meta-models. The minimum weight design of the DSF is significantly important for securing both maneuvering performance and buoyancy of a ship equipped with the DSF and guaranteeing structural design safety. The performance strength of the passive type DSF was evaluated through structure analysis based on the finite element method. The thickness of main structure members was applied to 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 experimental method and analysis of variance. The optimum design case was also identified from the orthogonal array experiment results. Various meta-models, such as Chebyshev orthogonal polynomial, Kriging, response surface method, and radial basis function-based neural network, were generated from the orthogonal array experiment results. The results of the orthogonal array experiment were validated using the meta-modeling results. It was found that the radial basis function-based neural network among the meta-models could approximate the design space of the passive type DSF with the highest accuracy.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.116-127
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• 2014

This paper aims to review methods for computing orthogonal projection of points onto curves and surfaces, which are given in implicit or parametric form or as point clouds. Special emphasis is place on orthogonal projection onto conics along with reviews on orthogonal projection of points onto curves and surfaces in implicit and parametric form. Except for conics, computation methods are classified into two groups based on the core approaches: iterative and subdivision based. An extension of orthogonal projection of points to orthogonal projection of curves onto surfaces is briefly explored. Next, the discussion continues toward orthogonal projection of points onto point clouds, which spawns a different branch of algorithms in the context of orthogonal projection. The paper concludes with comments on guidance for an appropriate choice of methods for various applications.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.858-863
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• 2004

In the optimized design of an actual structure, the design variable should be selected among any certain values or corresponds to a discrete design variable that needs to handle the size of a pre-formatted part. Various algorithms have been developed for discrete design. As recently reported, the sequential algorithm with orthogonal arrays(SOA), which is a local minimum search algorithm in discrete space, has excellent local minimum search ability. It reduces the number of function evaluation using orthogonal arrays. However it only finds a local minimum and the final solution depends on the initial value. In this research, the genetic algorithm, which defines an initial population with the potential solution in a global space, is adopted in SOA. The new algorithm, sequential algorithm with orthogonal arrays and genetic algorithm(SOAGA), can find a global solution with the properties of genetic algorithm and the solution is found rapidly with the characteristics of SOA.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.837-842
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• 2008

Students taking linear model courses have difficulty in determining which parametric functions are estimable when the design matrix of a linear model is rank deficient. In this note a special form of estimable functions is presented with a linear combination of some orthogonal estimable functions. Here, the orthogonality means the least squares estimators of the estimable functions are uncorrelated and have the same variance. The number of the orthogonal estimable functions composing the special form is equal to the rank of the design matrix. The orthogonal estimable functions can be easily obtained through the singular value decomposition of the design matrix.