• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.869-874
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• 2005

This paper proposes the Pareto artificial life algorithm for a multi-objective function optimization problem. The artificial life algorithm for a single objective function optimization problem is improved through incorporating the new method to estimate the fitness value fur a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm is applied to the optimum design of a Journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application are reported to present the possible solutions to a decision maker or a designer.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.449-454
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• 2000

A new fuzzy logic control design algorithm suitable for multi-objective control problems is proposed based on the orthogonal array which is widely used for design of experiments in statistics and industrial engineering. The essence of the algorithm is to introduce Nth-certainty factor defined from the F-value of the ANOVA(analysis of variance) table, in order to effectively exclude the less confident rules. The proposed algorithm with multi-objective decision table(MODT) is found to be capable of the detection of inconsistency and the rule classification, reduction and modification. It is also shown that the algorithm can be successfully applied to the fuzzy controller design of an active magnetic bearing system.

• Structural Engineering and Mechanics
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• v.77 no.6
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• pp.779-795
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• 2021

In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of Crow Search Algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow's memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood; it is considered as a "local superior design" (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.3
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• pp.327-339
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• 1986

In this paper, a hardware compiler for symbolic description language(SDL) is proposed for logic design automation. Lexical analysis is performed for SDL which describes the behavioral characteristics of a digital system at the register transfer level by the proposed algorithm I. The algorithm I is proposed to get the expressions for the control unit and for the data transfer unit. In order to obtain the network description language(NDL) expressions equivalent to gate-level logic circuits, another algorithm, the the algorithm II, is proposed. Syntax analysis for the data formed by the algorithm I is also Performed using circuit elements such as D Flip-Flop, 2-input AND, OR, and NOT gates. This SDL hardware compiler is implemented in the programming language C(VAX-11/750(UNIX)), and its efficiency is shown by experiments with logic design examples.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.299-306
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• 1998

Design sensitivity analysis of a mechanical system is an essential tool for design optimization and trade-off studies. This paper presents an efficient algorithm for the design sensitivity analysis of railway vehicle systems, using the direct differentiation method. The cartesian coordinate is employed as the generalized coordinate. The governing equations of the design sensitivity analysis are formulated as the differential equations. Design sensitivity analysis of railway vehicle systems is performed to show the validity and efficiency of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.198-202
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• 2004

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a test function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

• Korean Journal of Computational Design and Engineering
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• v.4 no.3
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• pp.269-283
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• 1999

Surface/surface intersection is a common and important problem in geometric modeling and CAD/CAM. Several methods have been used to approach this problem. All possible intersection curves can be obtained by using the subdivision algorithm, while it requires a great deal of memory and is somewhat inefficient. The tracing algorithm is much faster than the subdivision algorithm, and can find points on the intersection curve sequentially. But, the tracing algorithm has some problems in the intersection curves on surface boundaries. In this paper, an Improved tracing algorithm that includes some ideas such as a new trace-terminating condition for the intersection curves on surface boundaries, detecting closed intersections and extension for composite surfaces is suggested. This algorithm consists of three step: generating state points for curve tracing, tracing intersection curves and sorting pieces of the intersection curves. The results of this algorithm and comparisons to the 'DESIGNBASE' and 'ACIS' system are presented.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.24-36
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• 2016

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.589-594
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the losed loop problem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and efficiently. This algorithm is a hybrid type of using both declarative description (constraint represention) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1624-1634
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• 1995

Conventional full search block matching algorithm for motion estimation is computationally intensive and the resulting hardware cost is very high. In this paper, we present an efficient block matching algorithm using a 16:1 subsampling technique, and describe its hardware design. The algorithm reduces the number of pixels in calculating the mean absolute difference at each search location, instead of reducing the search locations.The algorithm is an extension of the block mating algorithm with 4:1 subsampling proposed by Liu and Zaccarin such that the amount of computation is reduced by a fact of 4(16 compared to the full search block matching algorithm) while producing similar performance.The algorithm can efficiently be designed into a hardware for real-time applications.