• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.108-115
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• 1997

This paper presents a robust controller design for uncertain nonlinear systems with input saturation. In actual application, the robust controller may require a high input torque so that it faces input saturation due to power limitation of the system. The satruation problem may cause instability of the system. To improve this problem, a robust controller using a fuzzy logic control is proposed. The proposed controller keeps state errors bounded. To validate the proposed controller, an invert pendulum and its control system is set up. The experimental result shows bounded angular position errors under saturated input torques.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.603-608
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• 2009

In this paper, a similarity measure between interval-valued vague sets is proposed. In the interval-valued vague sets representation, the upper bound and the lower bound of a vague set are represented as intervals of interval-valued fuzzy set respectively. Proposed method combines the concept of geometric distance and the center-of-gravity point of interval-valued vague set to evaluate the degree of similarity between interval-valued vague sets. We also prove three properties of the proposed similarity measure. It provides a useful way to measure the degree of similarity between interval-valued vague sets.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.790-800
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• 2018

The simplified neutrosophic set (SNS) is a generalization of fuzzy set that is designed for some practical situations in which each element has truth membership function, indeterminacy membership function and falsity membership function. In this paper, we propose a new method to construct similarity measures of single valued neutrosophic sets (SVNSs) and interval valued neutrosophic sets (IVNSs), respectively. Then we prove that the proposed formulas satisfy the axiomatic definition of the similarity measure. At last, we apply them to pattern recognition under the single valued neutrosophic environment and multi-criteria decision-making problems under the interval valued neutrosophic environment. The results show that our methods are effective and reasonable.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.437-443
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• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.6
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• pp.824-834
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• 1993

In cellular mobile communication systems, if the size of a cell is decreasing for economic utilization of frequency resources, frequent handovers may be requested because the time a mobile stays in a cell is decreasing. In general the measured parameters to decide handover including RSSI, BER, and the distance between mobile station and base station, are usually incorrect and handover decision using single parameter insufficient. Therefore, the better handover algorithm should take over the problems of this uncertain measurements, and make the decision more robust and flexible by the consideration of all those decision parameters at the same time. We propose a novel handover algorithm based the multicriteria decision making, in which those parameters are participated in the decision process using aggregation function in fuzzy set theory. As a simulation results, the overall decision making is more reliable and flexible than the conventional method using only one parameter, RSSI in terms of call force ratio, and handover request ratio.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.91-95
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• 2007

Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

• The Journal of Information Systems
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• v.21 no.2
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• pp.181-196
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• 2012

본 논문은 고객의 수요정보에 대해 모호한 정보를 가진 공급자와 구매자 사이의 공급망 계약에 관한 것을 다루고 있는 것으로, 고객 수요에 대한 불확실성은 확률적 프로그래밍 모델에서 공식적으로 다루어져왔다. 확률적 프로그램의 한 가지 핵심적인 가정은 널리 알려져 있는바와 같이 수요에 대한 확률분포가 알려져 있다는 것이다. 그럼에도 불구하고 만약 수요에 대한 정보가 모호하거나 정확하지 않다면 수요에 대한 확률분포가 정확하지 않다는 점이다. 이런 상황에서 퍼지 이론은 수요정보를 나타내는데 유용하다고 할수 있다. 본 논문은 퍼지 랜덤수요변수들을 분산시스템의 공급망 계약에서 다루고 있다. 이 계약은 구매자의 주문량을 조정하는 옵션을 이용한다. 본 연구는 퍼지 랜덤 변수들을 GMIR(Graded Mean Integration Representation)을 이용하여, 알고리즘을 통해 구현함으로써 실증적 결과 값을 제시하고 미래 연구의 확장 가능성을 제시하고 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.115-121
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• 2002

This paper presents an evolutionary design of digital IIR filters using the genetic algorithm (GA) with modified genetic operators and real-valued encoding. Conventional digital IIR filter design methods involve algebraic transformations of the transfer function of an analog low-pass filter (LPF) that satisfies prescribed filter specifications. Other types of frequency-selective digital fillers as high-pass (HPF), band-pass (BPF), and band-stop (BSF) filters are obtained by appropriate transformations of a prototype low-pass filter. In the GA-based digital IIR filter design scheme, filter coefficients are represented as a set of real-valued genes in a chromosome. Each chromosome represents the structure and weights of an individual filter. GA directly finds the coefficients of the desired filter transfer function through genetic search fur given filter specifications of minimum filter order. Crossover and mutation operators are selected to ensure the stability of resulting IIR filters. Other types of filters can be found independently from the filter specifications, not from algebraic transformations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.119-124
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• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.