• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2000.10a
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• pp.253-260
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• 2000

In recent years, a number of successful nesting approaches have been developed by using the various heuristic algorithms, and due to their application potential several commercial CAD/CAM packages include a nesting module for solving the layout problem. Since a large portion of the complexity of the part nesting problem results from the overlapping computation, the geometric representation is one of the most important factors to reduce the complexity of the problem. The proposed part representation method can easily handle parts and raw materials with widely varying geometrical shape by using the redesigning modules. This considerably reduces the amount of processed data and consequently the run time of the computer. The aim of this research is to develop parametric macro for two-dimensional layout on the Auto-CAD system. Therefore, this research can be called "pre-nesting".

• Journal of Ship and Ocean Technology
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• v.4 no.3
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• pp.13-20
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• 2000

In recent years, a number of successful nesting approaches have been developed by using the various heuristic algorithms, and due to their application potential several commercial CAD/CAM packages include a nesting module for solving the layout problems. Since a large portion of the complexity of the part nesting problem results from the overlapping computation, the geometric representation is one of the most important factors to reduce the complexity of the problem. The proposed part representation method can easily handle parts and raw materials with widely varying geometrical shape by using the redesigning modules. This considerably reduces the amount of processed data and consequently the run time of the computer. The aim of this research is to develop parametric macro for two-dimensional layout on the Auto-CAD system. Therefore, this research can be called "pre-nesting".ing".uot;.

• Korean Institute of Interior Design Journal
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• v.26 no.6
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• pp.163-171
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• 2017

Brick is not only aesthetically beautiful and emotional material, but also eco-friendly and good building commodity for human health. Nonetheless, the use of brick has declined, due to the difficulty of building high-rise buildings and the limitation of the free form implementation. However, modern society is increasingly interested in environmentally friendly finishing materials for solving environmental problems. From this point of view, the brick architecture is being reexamined as a material to improve the living environment and to provide comfort without destroying nature. In addition, the development of digital technology enables the implementation of various types of masonry method and curved forms. Parametric design is one of the ways to realize the curved forms and various architectural expressions for brick architecture. In this background, the purpose of this study is to develop algorithms that can easily generate curved brick walls through parametric design, enable various pattern designs, and respond to real-time feedback. The details of the study are as follows. First of all, we examine organic architecture, the trend of brick architecture, and the concept of parametric design. Secondly, In order to generate curved surface with complex curvature, major planning factors affecting form generation are examined. Finally, we develop a parametric design method that consists of generating a curved surface for brick arrangement, implementing a parametric algorithm, and generating a curved form using bricks. Consequentially, we propose an algorithm that can maximize the use of ready-made bricks without using cut bricks to design curved walls and present efficient and economical design alternatives.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.10
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• pp.1561-1570
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• 1997

This study explores the treatment of the max-value cost function over a parameter interval in parametric optimization. To avoid the computational burden of the transformation treatment using an artificial variable, a direct treatment of the original max-value cost function is proposed. It is theoretically shown that the transformation treatment results in demanding an additional equality constraint of dual variables as a part of the Kuhn-Tucker necessary conditions. Also, it is demonstrated that the usability and feasibility conditions on the search direction of the transformation treatment retard convergence rate. To investigate numerical performances of both treatments, typical optimization algorithms in ADS are employed to solve a min-max steady-state response optimization. All the algorithm tested reveal that the suggested direct treatment is more efficient and stable than the transformation treatment. Also, the better performing of the direct treatment over the transformation treatment is clearly shown by constrasting the convergence paths in the design space of the sample problem. Six min-max transient response optimization problems are also solved by using both treatments, and the comparisons of the results confirm that the performances of the direct treatment is better than those of the tranformation treatment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.691-698
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• 1996

This paper propose a procedure to estimate the system lifetime distribution using simulation method in a parametric framework and also develop the criterion for terminating the simulation. We assume that a system is composed of many components whose lifetime and repair time distributions are general, and repair of each component is imperfect or not. General simulation algorithms can not be adopted for this case, due to the dependency of successive operating times and the discontinuity in base line intensity function of failure process. Then we propose algorithms for generating failure times subject to imperfect repair. We develop the event time tracking logic for identifying the system failure time, and also develop the criterion for terminating the simulation. Our procedure is composed of two phases. The first phase of the procedure is to generate the system failure times from the inputs. The second phase is to estimate the lifetime distribution of the system. The best model is selected by a fully automated procedure among well-known parametric families, and the required parameters are estimated. We give examples to show the accuracy of our procedure and the effect of repair effect of components to system MTTF(Mean Time To Failure).

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.155-157
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• 2005

We propose a new category of hybrid multi-layer neural networks with hetero nodes such as Fuzzy Set based Polynomial Neurons (FSPNs) and Polynomial Neurons (PNs). These networks are based on a genetically optimized multi-layer perceptron. We develop a comprehensive design methodology involving mechanisms of genetic optimization and genetic algorithms, in particular. The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNNs quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.