• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.172-178
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• 2008

If the fault occurs on the underground power cable systems, the fault current on the sheath has an influence on all sections of cable because it's returned through earth at the directly grounded point and operation point of SVL(Sheath Voltage Limiter) on each insulated joint box. Therefore, the earth resistance and the operation of SVL have an effect on the zero-sequence current, and then the impedance between relaying point and fault point is increased. That causes the overreach of distance relay. For these reasons, the distance relay algorithm for protecting an underground power cable systems hasn't been developed till now. In this paper, new distance relay algorithm is developed for protecting a underground power cable system using fuzzy inference system which is the one of ACI(Advanced Computational Intelligence) techniques. This algorithm is verified by EMTP simulation of real power cable system, and proves to effectively advance the errors

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.82-85
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• 2006

With the fast growing number of skyscrapers and large ultrahigh apartment complexes, the concerns on fire caused by electricity also grow. Among about 30,000 fires recorded annually, roughly one third of them are hewn to be caused by electricity. If one of such high and densely populated buildings or apartments catches a fire, the consequence can potentially be quite catastrophic. However, with the rapid development of the techniques in the fields of communications and computers, electric power distribution systems for such buildings and apartments have been largely digitalized in recent years. More detailed informations on the operating status are now available, which enables more sophisticated monitoring and early detection of potential fire caused by electricity. In this paper, we present an inference technique that can be used as one of the basic techniques in building intelligent distribution panel systems that can effectively monitor, prevent and detect the occurrence of fire caused by electricity. The technique can accommodate production rules in linguistic expressions on high abstraction levels. Fire finding strategies can be easily modified to provide more effective countermeasures. Simulation results show that inference capabilities and thus the capability of fire monitoring in power distribution panel systems can be significantly enhanced with our approach.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.53-55
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• 1993

This paper describes the development of a software that has the man expert knowledge, experience and inference. This software is helpful for selecting the motors and driving systems which are best fit for the applications. Developed software can automatically select the most reasonable motor driving systems, only if a semi-skilled engineer inputs the performance criteria for the applications and mechanical data. Expert system inference engine and knowledge-base are implemented by C programming language. Data-base was implemented from manufacturer's catalogues for DC motors and brushless DC motors. Efficiencies of the various motor driving systems are compared reference on the average efficiency depends on the operating profiles. Developed expert system was tested in various of applications to verify the reliability, quick and easy selecting of the motor driving systems.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.521-526
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• 2012

The performance of arrow from a manufacturing process depends on arrow's trajectory(archer's paradox) and intensity of an impact points. Especially, when conducting a shooting experiment over and over in the same experiment condition, the intensity of impact point is an objective standard to judge the performance of the arrow. However, the analysis method for the impact point is not enough, a previous research of the arrow's performance has been focused on a skill to optimize a manufacturing variables(feathers of an arrow, barb of an arrow, arrow's shaft, weight, external diameter, spine). In this paper, We propose measurement system of arrow's impact point with Mamdani fuzzy inference system and similarity of polygon for automation of impact point's measurement. Measuring the impact point data of the arrow moving with a high speed(approximately 275km/h) by using line laser and photo diode array, then the measured data are mapped to arrow's impact point with fuzzy inference and similarity of polygon.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.291-296
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• 2006

In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.79-94
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• 2018

The purpose of this study is to specify a probabilistic tracking mechanism for customer luxury purchase implemented by hidden Markov model, Bayesian inference, customer satisfaction and net promoter score. In this paper, we have designed a probabilistic model based on customer's actual data containing purchase or non-purchase states by tracking the SPC chain : customer satisfaction -> customer referral -> purchase/non-purchase. By applying hidden Markov model and Viterbi algorithm to marketing theory, we have developed the statistical model related to probability theories and have found the best purchase pattern scenario from customer's purchase records.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.144-150
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• 2002

In this paper a design of neuro-fuzzy-based predictive controller for nonlinear systems with time-delay is proposed. The proposed control system contains two neuro-fuzzy systems called ANFIS(Adaptive Neuro-Fuzzy Inference System). One is run as a series-parallel mode and the other is run as a parallel mode. An ANFIS running in series-parallel mode emulates the response of the nonlinear system with time-delay. Another ANFIS running in parallel mode generates the predicted output of the nonlinear system to compensate for the time-delays. Therefore, the proposed control system can be thought of as an extension of Smith-predictor scheme to the nonlinear systems with time-delay. A detailed design Procedure is presented and finally computer simulations are executed for the effectiveness of the proposed control scheme.

• Smart Structures and Systems
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• v.21 no.5
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.