• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.402-410
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• 2004

In this paper, an optimal design method of clonal selection based Fuzzy-Neural Networks (FNN) model for complex and nonlinear systems is presented. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. Also Advanced Clonal Selection (ACS) is proposed to find the parameters such as parameters of membership functions, learning rates and momentum coefficients. The proposed method is based on an Immune Algorithm (IA) using biological Immune System and The performance is improved by control of differentiation rate. Through that procedure, the antibodies are producted variously and the parameter of FNN are optimized by selecting method of antibody with the best affinity against antigens such as object function and limitation condition. To evaluate the performance of the proposed method, we use the time series data for gas furnace and traffic route choice process.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.77-83
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• 2005

In collision avoidance problem of a ship, collision risk model is usually set up using the interview results fron experts who sit on a simulator by varying parameters, in which DCPA and TCPA are commonly used. This method, however, has the weakness in that not only it is expensive but also it shows different results depending on the inerviewees and other navigational parameters. In this study, a fuzzy inference system is designed based on own ship's maneuverability verified fron simulation instead of interviewing navigators. The time and distance corresponding to the collision risk value on which avoidance maneuver should be started are set to the minimum marginal time at which own ship starts maneuvering and the minimum marginal distance suggested by marine traffic rules respectively. This system can be recorfigured as a nonlinearity-strengthened one by increasing the number of fuzzy membership functions.

• Korean Journal of Health Education and Promotion
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• v.26 no.1
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• pp.63-80
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• 2009

Objectives: This study identified how personal characteristics, healthy behavior and social capital might influence on physical activity of adults. Methods: This study used data from the health survey of a city of Korea. We surveyed 1,000 adults sampled by stratified sampling methods from 67,889 households. Outcome variable was the stage of physical activity which was broken into 5 categories. Sociodemographic factors, healthy behavior, self-rated health status and social capital were used as control variables. Sociodemographic factors included age, sex, educational status, economic status measured by deprivation score, residential period within survey city. Social capital was measured by Integrated Questionnaire for the Measurement of Social Capital (SC-IQ). This study used chi-square test and ordered logistic regression models to examine the associations between independent variables and physical activity. Variables were added to the regression model in three groups using a hierarchical approach. Results: Physical activity was significantly more likely to become active if they have higher educational status, healthier behavior. Among the six dimensions of SC-IQ, only "groups and networks" that is structural dimensions of social capital and "trust and solidarity" that is cognitive dimensions of social capital were significantly related to physical activity of adults. We found that a person having higher density of membership and having larger size of networks showed the high possibility of active physical activity. A person having high solidarity was significantly associated with physical activity, but general trust was inversely related to physical activity. Output dimensions of social capital did not show significant relationship to physical activity. Conclusion: We found that social capital is useful concept to explain health behaviors like physical activity. However we must consider social, cultural and political context of the study to evaluate the effect of social capital to health status and health determinants and to capture the exact meaning of relationship between them. We suggest further researches to refine the concept of social capital and to explain the relationship of social capital to diverse health determinants.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) 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 information granulation and genetic algorithms. The proposed IG_gSOFPNN 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. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.451-457
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• 2006

A peer-to-peer(P2P) network is inherently vulnerable to malicious attacks from participating peers because of its open, flat, and autonomous nature. This paper addresses the problem of effectively defending from active attacks of malicious peers at bootstrapping phase and at online phase, respectively. We propose a secure membership handling protocol to protect the assignment of ID related things to a newly joining peer with the aid of a trusted entity in the network. The trusted entities are only consulted when new peers are joining and are otherwise uninvolved in the actions of the P2P networks. For the attacks in online phase, we present a novel message structure applied to each message transmitted on the P2P overlay. It facilitates the detection of message alteration, replay attack and a message with wrong information. Taken together, the proposed techniques deter malicious peers from cheating and encourage good peers to obey the protocol of the network. The techniques assume a basic P2P overlay network model, which is generic enough to encompass a large class of well-known P2P networks, either unstructured or not.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Journal of Korean Academy of Nursing
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• v.16 no.2
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• pp.44-52
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• 1986

Maternal and child health(MCH) status is considered as an important indicator of the level of health and civilization of a community and a country. MCH services for the rural population in the remote ar deserves priority by the government, since more than half(52.9%) of the delivery was occured at home and almost half (45.5%) of the delivery was assited by family members or neighbors. The purpose of the study was to analyse the health fare behavior related to pregnancy and delivery, which can be contributed maternal health care policy mating for the rural people. Specifically, it was intended to analyze the variables which affect the health care behavior in selecting birth places and birth attendants. This study utilized the data which had been already collected for an experimental study on primary health program model in Korean rural communities, funded by the USAID. 184 sample households with women who had delivered a baby during March 1982 to February 1983 were selected. Discriminant Analysis was employed for statistical analysis by utilizing SPSS computer package program. Birth places and birth attendants were considered as dependent variables. Among 12 independent variables in 5 groups considered, 7 independent variables were found statistically significant to affect the selection of birth place. Significant variables by the order of importance are mother's age, order of baby, number of prenatal care, accessibility of emergency medical care, coverage of medical insurance, mother's membership in community organization and husband's educational level. The degree of correct classification of the grouped cases by employing a discriminant . analysis was significantly improved to 78.2％ in comparison to Cmax（56%) and Cpro(51%). Policy implications for each significant variable were discussed to improve the maternal and child health. in Korean ruralarea.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.52-56
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• 2004

The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.