• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2002.09a
• /
• pp.285-291
• /
• 2002

This paper proposes the concept and the general framework of the risk-based seismic design. Because earthquakes and the behaviors of structures are very unpredictable, probabilistic seismic design methods have been proposed after deterministic design methods. Considering these changes, we can find that the important point of seismic design is not the structural behavior itself, but the consequence of structural behavior under possible earthquakes. Risk-based seismic design can tell these consequences under any earthquakes. In this paper, structural confidences are considered by using fragility curve, and risk is modeled by failure probability and consequence-property damage cost, casualty cost.

• Journal of the Korean Society of Safety
• /
• v.19 no.1
• /
• pp.102-107
• /
• 2004

Performance based design is a recent evolutionary step in the process of designing fire protection systems. In essence, it is a logical design process resulting in a solution that achieves a specified performance. Sometimes the prescriptive solutions presented in various codes and standards are too expensive or inflexible. Often the solutions do not and enables optimization of a solution for cost and function. In this study, performance based design was carried out to determine the extent of passive fire protection for oil terminal facilities. The results of performance based design were compared with those of prescriptive code based design. Performance based design is not always more economic than prescriptive code based design but provides more reliable and effective design that is fit for the purpose.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.1081-1087
• /
• 2015

This article aims to provide a design method of warning thresholds for active safety systems based on the performance-based approach considering driver behaviors. Both positive and negative consequences of warnings are considered, and the main idea is to choose a warning threshold where the positive consequence is maximized, whereas the negative consequence is minimized. The process of the performance-based approach involves: Defining the operating scenarios; setting the trajectory models, including human characteristics; estimating the alert and nominal trajectories; estimating the performance metrics; generating a performance-metric plot; and determining the alert thresholds. This paper chose a lane-departure warning system as an example to show the usefulness of the performance-based approach. Both human and sensor characteristics were considered in the system design, and this paper provided a quantitative method to include human factors in designing active safety systems.

• The Korean Journal of Applied Statistics
• /
• v.25 no.5
• /
• pp.847-857
• /
• 2012

This article is concerned with choice-based conjoint analysis versus rating-based and ranking-based conjoint analysis. Choice-based conjoint analysis has a definite advantage in that the respondent's task of choosing the most preferred profile from several competing profiles adequately mimics consumer marketplace behavior. It is crucial to design the choice sets appropriate for the choice-based conjoint. Thus, this article suggests a new method to design the choice sets that are well-balanced. It augments the balanced incomplete block design and then obtains the dual design of the result to accommodate various numbers of profiles. In consequence, the choice sets designed by the new method have the desirable characteristics that each profile is presented to the same number of respondents, and pairs of any two distinct profiles occur together in the same number of choice sets. The balancing of the design increases the efficiency of the conjoint analysis. In addition, the pair-comparison scheme can improve the quality of data through the identification of contradictory responses.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.2
• /
• pp.126-135
• /
• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.3
• /
• pp.621-632
• /
• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• Journal of Radiation Protection and Research
• /
• v.41 no.4
• /
• pp.368-372
• /
• 2016

Background: Methodologies for a series of radiological consequence assessments show a distinctive difference according to the design principles of the original nuclear suppliers and their technical standards to be imposed. This is due to the uncertainties of the accidental source term, radionuclide behavior in the environment, and subsequent radiological dose. Both types of PWR and PHWR are operated in Korea. However, technical standards for evaluating atmospheric dispersion have been enacted based on the U.S. NRC's positions regardless of the reactor types. For this reason, it might cause a controversy between the licensor and licensee of a nuclear power plant. Materials and Methods: It was modelled under the framework of the NRC Regulatory Guide 1.145 for light-water reactors, reflecting the features of heavy-water reactors as specified in the Canadian National Standard and the modelling features in MACCS2, such as atmospheric diffusion coefficient, ground deposition, surface roughness, radioactive plume depletion, and exposure from ground deposition. Results and Discussion: An integrated accident consequence assessment code, ACCESS (Accident Consequence Assessment Code for Evaluating Site Suitability), was developed by taking into account the unique regulatory positions for reactor types under the framework of the current Korean technical standards. Field tracer experiments and hand calculations have been carried out for validation and verification of the models. Conclusion: The modelling approaches of ACCESS and its features are introduced, and its applicative results for a hypothetical accidental scenario are comprehensively discussed. In an applicative study, the predicted results by the light-water reactor assessment model were higher than those by other models in terms of total doses.

• Steel and Composite Structures
• /
• v.14 no.4
• /
• pp.379-395
• /
• 2013

Aim of this paper is to apply to a steel truss bridge a methodology that takes into account the consequences of extreme loads on structures, focusing on the influence that the loss of primary elements has on the structural load bearing capacity. In this context, the topic of structural robustness, intended as the capacity of a structure to withstand damages without suffering disproportionate response to the triggering causes while maintaining an assigned level of performance, becomes relevant. In the first part of this study, a brief literature review of the topics of structural robustness, collapse resistance and progressive collapse takes place, focusing on steel structures. In the second part, a procedure for the evaluation of the structural response and robustness of skeletal structures under impact loads is presented and tested in simple structures. Following that, an application focuses on a case study bridge, the extensively studied I-35W Minneapolis steel truss bridge. The bridge, which had a structural design particularly sensitive to extreme loads, recently collapsed for a series of other reasons, in part still under investigation. The applied method aims, in addition to the robustness assessment, at increasing the collapse resistance of the structure by testing alternative designs.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.8
• /
• pp.561-570
• /
• 2004

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.2
• /
• pp.127-136
• /
• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.