• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.425-429
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• 1996

Our unicycle has simple mechanical structure. But unicycle's dynamic system is a very sensitive unstable nonlinear system. In this paper, a fuzzy inference control mechanism was established throughout an inquiry into human riding a unicycle, and we developed a direct fuzzy controller to control our unicycle robot. This proposed fuzzy controller is consisted with fuzzy logic controllers for attitude stability and wheel's velocity. Computer simulation results show that our fuzzy controller has very powerful performance to unstable nonlinear unicycle robot system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.970-976
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• 2007

In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.

• Journal of the Ergonomics Society of Korea
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• v.17 no.1
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• pp.37-46
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• 1998

A human sensibility ergonomics design supporting system was applied to the product development for the customer's satisfaction based on ergonomics technology. The system is composed of three major subsystems such as customer's sensibility analysis, inference mechanism, and presentation technology. The main approaches of the system are to analyze customer's sensibilities and to translate them into product design elements. The purpose of this paper is to develop a design supporting system in which the relationship between customer's sensibility and product design elements is reasoned by a MADM(Multi-Attribute Decision Making) fuzzy model. In this model, three variables such as multiple correlation coefficients, partial correlation coefficients, and category scores were used in reasoning process. The weighted value of the words were also considered in fuzzy decision process. As a case study, the design supporting system with the MADM fuzzy model was applied to the personnel computer design.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.199-207
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• 2023

Trend Impact Analysis is a prominent hybrid method has been used in future studies with a modified surprise- free forecast. It considers experts' perceptions about how future events may change the surprise-free forecast. It is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using adaptive neuro-fuzzy inference system (ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.119-136
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• 1996

The evaluation of risk level or possibility of traffic accidents is a fundamental task in reducing the dangers associated with current transportation system. However, due to the lack of data and basic researches for identifying such factors, evaluations so far have been undertaken by only the experts who can use their judgements well in this regard. Here comes the motivation this thesis to evaluate such risk level more or less in an automatic manner. The purpose of this thesis is to test the fuzzy-logic theory in evaluating the risk level of traffic accidents. In modeling the process of expert's logical inference of risk level determination, only the geometric features have been considered for the simplicity of the modeling. They are the visibility of road surface, horizontal alignment, vertical grade, diverging point, and the location of pedestrain crossing. At the same time, among some inference methods, fuzzy composition inference method has been employed as a back-bone inference mechanism. In calibration, the proposed model used four sites' data. After that, using calibrated model, six sites' risk levels have been identified. The results of the six sites' outcomes were quite similar to those of real world other than some errors caused by the enforcement of the model's output. But it seems that this kind of errors can be overcome in the future if some other factors such as driver characteristics, traffic environment, and traffic control conditions have been considered. Futhermore, the application of site's specific time series data would produce better results.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.560-567
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• 2007

The objective of this research is to optimize the designing parameters of the parallel manipulator with large orientation workspace at the boundary position of the constant orientation workspace (COW). The method uses a simple genetic algorithm(SGA) while considering three different kinematic performance indices: COW and the global conditioning index(GCI) to evaluate the mechanism's dexterity for translational motion of an end-effector, and orientation workspace of two angle of Euler angles to obtain the large rotation angle of an end-effector at the boundary position of COW. Total fifteen cases divided according to the combination of the sphere radius of COW and rotation angle of orientation workspace are studied, and to decide the best model in the total optimized cases, the fuzzy inference system is used for each case's results. An optimized model is selected as a best model, which shows better kinematic performances compared to the basis of the pre-existing model.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.159-177
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• 1995

The objective of this paper is to propose a knowledge-based fuzzy post adjustment so that unstructured problems can be solved more realistically by expert systems. Major part of this mechanism forcuses on fuzzily assessing the influence of various external factors and accordingly improving the solutions of unstructured problem being concerned. For this purpose, three kinds of knowledge are used : user knowledge, expert knowledge, and machine knowledge. User knowledge is required for evaluating the external factors as well as operating the expert systems. Machine knowledge is automatically derived from historical instances of a target problem domain by using machine learning techniques, and used as a major knowledge source for inference. Expert knowledge is incorporate dinto fuzzy membership functions for external factors which seem to significantly affect the target problems. We applied this mechanism to a prototyoe expert system whose major objective is to provide expert guidance for stock market timing such as sell, buty, or wait. Experiments showed that our proposed mechanism can improve the solution quality of expert systems operating in turbulent decision-making environments.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.344-347
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• 2005

Helper T(Th) cells regulate immune response by producing various kinds of cytokines in response to antigen stimulation. The regulatory functions of Th cells are promoted by their differentiation into two distinct subsets, Th1 and Th2 cells. Th1 cells are involved in inducing cellular immune response by activating cytotoxic T cells. Th2 cells trigger B cells to produce antibodies, protective proteins used by the immune system to identify and neutralize foreign substances. Because cellular and humoral immune responses have quite different roles in protecting the host from foreign substances, Th cell differentiation is a crucial event in the immune response. The destiny of a naive Th cell is mainly controlled by cytokines such as IL-4, IL-12, and IFN-${\gamma}$. To understand the mechanism of Th cell differentiation, many mathematical models have been proposed. One of the most difficult problems in mathematical modeling is to find appropriate kinetic parameters needed to complete a model. However, it is relatively easy to get qualitative or linguistic knowledge of a model dynamics. To incorporate such knowledge into a model, we propose a novel approach, fuzzy continuous Petri nets extending traditional continuous Petri net by adding new types of places and transitions called fuzzy places and fuzzy transitions. This extension makes it possible to perform fuzzy inference with fuzzy places and fuzzy transitions acting as kinetic parameters and fuzzy inference systems between input and output places, respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.471-476
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• 1995

This paper focues on development and implementation of a perfomance management algorithm for IEEE802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents the structure of a network performance manager that possesses the knowledge about perfomance management in a set of fuzzy rules and deriving its action through fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.931-940
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• 2001

In this paper, I propose Fuzzy Policing Mechanism(FPM) over ATM networks for the control of traffic which is unpredictable and bursty source. The FPM is consist of counter, subtracter and Fuzzy Logic Controller(FLC). The FLC is divided to fuzzifier, inference engine and defuzzifer The output of FLC inputs to the subtractor and it controls the counter. The counter works as a switch in transmission of cells. In simulation, I compared the FPM with the Leaky Bucket algorithm(LBM) in cell loss probability and performance characteristics. As a result, FPM gives lower cell loss probability than that of LBM and has good response behavior The FPM efficiently controls the transmission of packets which are variable traffic source and, it also has good selectivity.