• Computational Structural Engineering
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• v.10 no.2
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• pp.255-263
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• 1997

The optimum design of most structural systems used in practice requires considering design variables as discrete quantities. The present paper shows that the linear approximation method is very effective as a tool for the discrete optimum designs of unsymmetric composite laminates. The formulated design problem is subjected to a multiple in-plane loading condition due to shear and axial forces, bending and twisting moments, which is controlled by maximum strain criterion for each of the plys of a composite laminate. As an initial approach, the process of continuous variable optimization by FDM is required only once in operating discrete optimization. The nonlinear discrete optimization problem that has the discrete and continuous variables is transformed into the mixed integer programming problem by SLDP. In numerical examples, the discrete optimum solutions for the unsymmetric composite laminates consisted of six plys according to rotated stacking sequence were found, and then compared the results with the nonlinear branch and bound method to verify the efficiency of present method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.351-358
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• 2005

Many methods have been developed and are in use for structural size optimization problems, In which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary In this paper, a discrete search strategy using the HS algorithm is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through a standard truss example. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current method.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.33 no.12
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• pp.53-62
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• 2017

Many gradient-based mathematical methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. The main objective of this paper is to propose an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm that is derived using penalty function. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this paper, a discrete search strategy using the HS algorithm with a static penalty function is presented in detail and its applicability using several standard truss examples is discussed. The numerical results reveal that the HS algorithm with the static penalty function proposed in this study is a powerful search and design optimization technique for structures with discrete-sized members.

• The Mathematical Education
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• v.59 no.1
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• pp.47-62
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• 2020

Understanding the concept of probability distribution becomes more important. We considered probabilities defined in the sample space, the definition of discrete random variables, the probability of defined discrete probability distribution, and the relationship between them as knowledge of discrete probability distribution, and investigated the understanding degree of the mathematics preservice teachers. The results are as follows. Firstly, about 70% of preservice teachers who participated in this study expressed discrete probability distribution graphs in ordered pairs or continuous distribution. Secondly, with regard to the two factors for obtaining discrete probability distributions: probability for each element in the sample space and the concept of random variables that convert each element in the sample space into a real value, only 13% of the preservice teachers understood and addressed both factors. Thirdly, 39% of the preservice teachers correctly responded to whether different probability distributions can be defined for one sample space. Fourthly, when the probability of each fundamental event was determined to obtain the probability distribution of the discrete random variables defined in the undefined sample space, approximately 70% habitually calculated by the uniform probability. Finally, about 20% of preservice teachers understood the meaning and relationship of binomial distribution, discrete random variables, and sample space. In relation, clear definitions and full explanations of concept need to be provided from textbooks and a program to improve the understanding of preservice teachers need to be developed.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.11a
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• pp.167-173
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• 1999

This paper suggests a method to design the deep groove ball bearing for high-load capacity by using a genetic algorithm. The design problem of ball bearings is a typical discrete/continuous optimization problem because the deep groove ball bearing has discrete variables, such as ball size and number of balls. Thus, a genetic algorithm is employed to find the optimum values from a set of discrete design variables. The ranking process is proposed to effectively deal with the constraints in genetic algorithm. Results obtained fer several 63 series deep groove ball bearings demonstrated the effectiveness of the proposed design methodology by showing that the average basic dynamic capacities of optimally designed bearings increase about 9~34% compared with the standard ones.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.63-69
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• 1999

The research trend of the simulation optimization has been focused on exploring continuous decision variables. Yet, the research in discrete decision variable area has not been fully studied. A new research trend for optimizing discrete decision variables ha just appeared recently. This study, therefore, deals with a discrete simulation method to get the system evaluation criteria required for designing a complex probabilistic discrete event system and to search the effective and reliable alternatives to satisfy the objective values of the given system through a on-line, single run with the short time period. Finding the alternative, we construct an algorithm which changes values of decision variables and a design alternative by using the stopping algorithm which ends the simulation in a steady state of system. To avoid the loss of data while analyzing the acquired design alternative in the steady state, we provide background for estimation of an auto-regressive model and mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data through simulation with the short time period. In numerical experiment we applied the proposed algorithm to (s, S) inventory system problem with varying Δt value. In case of the (s, S) inventory system, we obtained good design alternative when Δt value is larger than 100.

• Korean System Dynamics Review
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• v.5 no.1
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• pp.127-142
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• 2004

Feedback loop dominance is a key concept to understand structural driving forces of system behavior. In this paper, we propose two kinds of shifts in dominant feedback loops: continuous shifts (CS) and discrete shifts (DS). With the help of questionnaires, we verified three hypotheses regarding cognitive biases in perceiving the shifts in dominant feedback loops: 1) failure in perceiving continuous shifts, 2) tendency of decision making based on discrete shifts, and 3) different perception on the dominant feedback loops between level variables and rate variables. We discussed the implication of these cognitive biases on time delay and timing strategy in decision-making processes.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.163-169
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• 1998

In this paper, we consider the problem of designing H$\infty$ state feedback controller for the generalized time systems with delayed states and control inputs in continuous and discrete time cases, respectively. The generalized time delay system problems are solved on the basis of LMI(linear matrix inequality) technique considering time delays. The sufficient condition for the existence of controller and H$\infty$ state feedback controller design methods are presented. Also, using some changes of variables and Schur complements, the obtained sufficient condition can be rewritten as a LMI form in terms of transformed variables. The propose controller design method can be extended into the problem of robust H$\infty$ state feedback controller design method easily.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.9
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• pp.1399-1407
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• 2004

Structural optimization has been carried out in continuous design space or in discrete design space. Generally, available designs are discrete in design practice. However, the methods for discrete variables are extremely expensive in computational cost. An iterative optimization algorithm is proposed for design in a discrete space, which is called a sequential algorithm using orthogonal arrays (SOA). We demonstrate verifying the fact that a local optimum solution can be obtained from the process with this algorithm. The local optimum solution is defined in a discrete design space. Then the search space, which is a set of candidate values of each design variables formed by the neighborhood of a current design point, is defined. It is verified that a local optimum solution can be found by sequentially moving the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained by using the SOA algorithm

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1005-1010
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• 2004

The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm