• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.123-126
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• 2000

An elevator system which is a essential equipment for a vertical movement of object, as a property of building, have been drove by various expenditure and purpose. Since developing electrical control technology, control systems are highly developed. An elevator equipment is expended to wide, but a data accuracy acquisition technique and safety predict technique for securing system safety is still basic level. So, objective verification for elevator confidence condition is required absolutely accuracy measurement technique. Therefore, this study is accomplished in order to conquer a method of depending on sense of a manager with a simple numeric measurement data, and construct a logical, analytical foresight system for more efficient elevator management system.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.420-428
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• 2012

In this paper, a novel intelligent digital redesign (IDR) technique is proposed for the nonlinear interconnected systems which can be represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique is to convert a pre-designed analog controller into an equivalent digital one. To develop this method, the discretized models of the analog and digital closed-loop system with the decentralized controller are presented, respectively. Using these discretized models, the digital decentralized control gain is obtained to minimize the norm between the state variables of the analog and digital closed-loop systems and stabilize the digital closed-loop system. Its sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.417-423
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• 2001

This paper proposes a technique for fault detection and classification for both LIF(Low Impedance Fault)s and HIF(High Impedance Fault)s using Adaptive Network-based Fuzzy Inference System(ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square(RMS) values of 3-phase currents and zero sequence current. The performance of the proposed technique is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results show that the ANFIS can detect and classily faults including (LIFs and HIFs) accurately within half a cycle.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.109-116
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• 2005

In this paper, we investigate the visual and quantitative analysis at the same time with an electronic tongue(e-tongue) system using an array of ISE(ion-selective electrode). We apply the FCM(fuzzy c-means) algorithm combined with PCA(principal component analysis), which can be reduced multi-dimensional data to third-dimensional data, to classify data patterns detected by E-Tongue system. The proposed technique can be designed to solve the cluster centers and membership grade of patterns combined with the output results obtained by PCA method. According to the proposed technique, the membership grade of unknown pattern, which does not shown previously can be determined and analyzed visually. Conclusionally, the relationship between the standard patterns and unknown pattern can be easily analyzed. Throughout the experimental trials, the proposed technique has been confirmed using developed E-Tongue system.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.202-209
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• 2004

Quality control is essential to the final product in BGA-type PCB fabrication. So, many automatic vision systems have been developed to achieve speedy, low cost and high quality inspection. A multiple threshold selection algorithm is a very important technique for machine vision based inspection. In this paper, an inspected image is modeled by using fuzzy sets and then the parameters of specified membership functions are estimated to be in maximum fuzzy entropy with the probability of the fuzzy sets, using the exhausted search method. Fuzzy c-partitions with the estimated parameters are automatically generated, and then multiple thresholds are selected as the crossover points of the fuzzy sets that form the estimated fuzzy partitions. Several experiments related to flip chip BGA images show that the proposed algorithm outperforms previous ones using both entropy and variance, and also can be successfully applied to AVI systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.489-491
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• 1993

Many optimization problem or multiple attribute, multiple alternative decision making problem may have fuzzy evaluation factors. In this case, fuzzy number operation technique is needed to evaluate and compare object functions which become fuzzy sets. Generally, fuzzy number operations can be defined by extension principle of fuzzy set theory, but it is tedious to do fuzzy number operations by using extension principle when the membership functions are defined by complex functions. Many fast methods which approximate the membership functions such as triangle, trapezoidal, or L-R type functions are proposed. In this paper, a fast fuzzy number operation method is proposed which do not simplify the membership functions of fuzzy numbers.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2283-2285
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• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.531-536
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• 2014

With the very large quantity of information available on the Internet, techniques for dealing with the abundance of documents have become increasingly necessary but the problem of processing information in the documents is still technically challenging and remains under study. Automatic document summary techniques have been considered as one of critical solutions for processing documents to retain the important points and to remove duplicated contents of the original documents. In this paper, we propose a document summarization technique that uses a fuzzy theory. Proposed summary technique solves the ambiguous problem of various features determining the importance of the sentence and the experiment result shows that the technique generates better results than other previous techniques.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.61-71
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• 2003

In the study of control technique for a turbojet engine model, the Takagi-Sugeno fuzzy logic controller has been designed based on the model identification by the well designed PI controlled system through T-S neuro-fuzzy inference system. To enhance this designed controller, those procedures are proposed that certainty factors are adopted to each rule of objective groups which are classified by the fuzzy C-Means algorithm and the satisfaction degrees are matched to meet the objectives. This proposed technique shows its feasibility by upgrading performances of the previously well-designed T-S fuzzy controller.