• Transactions of the Society of Information Storage Systems
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• v.3 no.4
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• pp.178-182
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• 2007

Topology optimization methods are classified into two methods such as the density method and the homogenization method. Those methods need to consider relationships between the material property and the density of each element in a design domain, the relaxation of the design space, etc. However, it is hard to apply on some cases due to the complexity to compose the design objective and its sensitivity analysis. In this paper, a modified topology optimization is proposed to assist designers who do not have mathematical or theoretical background of the topology optimization. In this study, optimal topology of structures can be achieved by the sequential design of experiment (DOE) and the sensitivity analysis. We conducted the DOE with an orthogonal array and the sensitivity analysis of design variables to determine sensitive variables used for connectivity between elements. The modified topology optimization method has advantages such as freedom from penalizing intermediate values and easy application with basic DOE concept.

• Bulletin of the Society of Naval Architects of Korea
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• v.25 no.3
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• pp.35-45
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• 1988

This paper presents a sophisticated Sequential Linear Approximation Method(SLAM) to solve nonlinear optimization problem and the performance of this method is compared with those of the Penalty Function Method(SUMT), Tangent Search Method(TSM) and Flexible Tolerance Method(FTM). To improve the convenience and flexibility in using the proposed SLAM, an user oriented design optimization language is developed and the application examples are shown for the optimization of propeller principal dimensions and the optimization of bulk carrier principal particulars.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.3
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• pp.258-266
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• 2008

The conventional approximate optimization method, which uses the statistical design of experiments(DOE) and response surface method(RSM), can derive an approximated optimum results through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The purpose of this study is to propose a new technique, which is called a sequential design of experiments(SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network(ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently. The suggested algorithm has been applied to various mathematical examples and a structural problem.

• Journal of Korean Society for Quality Management
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• v.32 no.3
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• pp.141-152
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• 2004

This study describes process and method to establish concurrent and/or sequential schedule planning for the system design process of the EIA632 standard. For this purpose, the study suggest Process and method that are context analysis of standard process, interface definition from activity decomposition, integration of related activities, and definition of concurrent and/or sequential work flow. The proposed process and method will contribute to minimize time loss that is emerged from activities iteration.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.896-904
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• 1987

In this paper, a design method for sequential circuit which is easy to have Built-in Self Test is kproposed using the functional advantages of multifunctional BILBO and LSSD. To achieve the hardware reduction, it is designed that a multifunctional BILBO has double operational functions of NLFSR and LFSR, when neccessary, and that test signal could be used as an input-output signal in the same line. By applying the proposed multifunctional BILBO to the sequential PLA, the test patterns and the additional circuit could be reduced in test operation and the propagation delay is vanished in normal operation, as we expected. Above them, the partitioned method for large scale sequential circuit is also suggested and it is observed that test patterns and additional circuit in them reduced by this method.

• Journal of Ocean Engineering and Technology
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• v.15 no.4
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• pp.108-114
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• 2001

Petri nets have been introduced as a powerful analyzing and design tool for the discrete systems such as sequential control systems. However, one of the important problems in its applications is that the model can be analyzed hardly when we deal with large scale systems because of increase of the number of Petri net components. To overcome this problem, some methods for dividing or reducing of Petri net have been suggested. In this paper, an approach for hierarchical expression of Petri net based on Sequential function Chart(SFC) and Bottom Up oriented Mehodology(BUM) is proposed. Especially, some definitions and rules are defined in order to divide and compose sub Petri nets. A measuring tank system will be described as a typical kind of discrete systems and modeled by some sub Petri nets based on the SFC and BUM by the proposed method in this paper.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.26-31
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• 2001

Petri nets(PN) have been introduced as a poweful analyzing and design tool for the discrete systems such as sequential control systems. However, one of the important problems in its applications is that the model can not be analyzed easily when we deal with large scale systems because of increase of the number of components of the systems. To overcome this problem, some methods for dividing or reducing of PN have been suggested. In this paper, an approach for hierarchical expression of PN based on Sequential Function Chart(SFC) and Bottom Up oriented Mehodology(BUM) is proposed. Especially, some definition and rules are defined in order to divide and compose sub PN. A measuring tank system will be described as a typical kind of discrete systems and modeled by some sub PN based on the SFC and BUM by the proposed method in this paper.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.6
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• pp.1041-1049
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• 2003

In this paper, a new local two-point approximation method which is based on the exponential intervening variable is proposed. This new algorithm, called the Two-Point Convex Approximation(TPCA), use the function and design sensitivity information from the current and previous design points of the sequential approximate optimization to generate a sequence of convex, separable subproblems. This paper describes the derivation of the parameters associated with the approximation and the numerical solution procedure. In order to show the numerical performance of the proposed method, a sequential approximate optimizer is developed and applied to solve several typical design problems. These optimization results are compared with those of other optimizers. Numerical results obtained from the test examples demonstrate the effectiveness of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.3040-3052
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• 2000

For effective construction of second-order response surface models, an efficient quad ratic approximation method is proposed in the context of trust region model management strategy. In the proposed method, although only the linear and quadratic terms are uniquely determined using 2n+1 design points, the two-factor interaction terms are mathematically updated by normalized quasi-Newton formula. In order to show the numerical performance of the proposed approximation method, a sequential approximate optimizer is developed and solves a typical unconstrained optimization problem having 2, 6, 10, 15, 30 and 50 design variables, a gear reducer system design problem and two dynamic response optimization problems with multiple objectives, five objectives for one and two objectives for the other. Finally, their optimization results are compared with those of the CCD or the 50% over-determined D-optimal design combined with the same trust region sequential approximate optimizer. These comparisons show that the proposed method gives more efficient than others.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.353-366
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• 1997

In this paper, we derive an approximate optimal Bayes group sequential design for a given loss function. We use the Monte-Carlo method to compare the ASN(average sample size) function and Bayes risk of approximate optimal Bayes group sequential design, Pocock design and O'Brien and Fleming design. Also introduced is the concept of Bayes efficiency and percentage loss of information due to grouping for the group sequential design and use it to measure the loss of information for different group sizes.