• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.274-279
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• 1998

For the accurate generation of DEMs, the determination of match quality in adaptive least square matching algorithm is significantly important. Traditionally, only the degree of convergence of a solution matrix in least squares estimation has been considered for the determination of match quality. It is, however, not enough to determine the true match quality. This paper reports two approaches of match quality determination based on adaptive least square correlation : the conventional if-then logic approaches with scene geometry and correlation as additional quality measures; and, the fuzzy logic approaches. Through these, accurate decision of match quality will minimize the number of blunder and maximize the number of exact match. The proposed methods have been tested on JERS and SPOT images and the results show good performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.259-265
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• 2004

In this study, a hybrid genetic algorithm merged with simulated annealing is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated one, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA. In particular, we observe a relatively high accuracy and fast convergence of the method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.63-73
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• 1997

For the practical applications of the EIT technology, it is essential to reconstruct sttic images iwth a higher spatial resolution in a reasonalble amount of processing time. Using the conventional EIT static image reconstruction algorithms, however, the processing time increases exponential with poor convergence characteristics as we try to get a higher spatial resolution. In order to overcome this problem, we developed a recursive mesh grouping method based on the Fuzzy-GA like algorithm. Computational simulation using the well-known improve dewton-raphson method with the proposed recursive mesh grouping algorithm shows a promising result that we can significantly reduce the processing time in the reconstruction of EIT static images of a higher spatial resolution.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.375-378
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• 2011

This study is intended to realize an intelligent controller using fuzzy control algorithms in order to recycle energy by recycling the waste water heat discharged by waste water heat collection boilers. Using waste water inflow temperature changes and waste water inflow amount changes as parameters, we present characteristic curves of the number of compressors being operated at fixed speeds and the temperature of hot water being discharged. We propose an intelligent controller that determines the optimum number of compressors being operated at fixed speeds in real time by measuring changes in the temperature and amount of waste water inflows in order to minimize the number of compressors being operated at fixed speeds relative to the waste water load flowing into the waste water heat collection boiler.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.84-87
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• 2003

This paper proposes the enhanced REF network, which arbitrates learning rate and momentum dynamically by using the fuzzy system, to arbitrate the connected weight effectively between the middle layer of REF network and the output layer of REF network. ART2 is applied to as the learning structure between the input layer and the middle layer and the proposed auto-turning method of arbitrating the learning rate as the method of arbitrating the connected weight between the middle layer and the output layer. The enhancement of proposed method in terms of learning speed and convergence is verified as a result of comparing it with the conventional delta-bar-delta algorithm and the REF network on the basis of the ART2 to evaluate the efficiency of learning of the proposed method.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.253-258
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• 2012

In this thesis, I categorize expression of context data required for context data inference according to five Ws and one H(5W1H) in Ubiquitous computing environment and infer superordinate context by combining context data of 4W1H with inferred context of why. This thesis suggests that we categorize specific context and service according to 6W2H added Whom(specific data or service) and How much (accuracy), and determine proper services for specific contexts by introducing the concept of rough set for expression and inference of categorized contexts and inaccurate knowledge. Since there is an limitation of the set of 0 and 1 when concerned with accuracy of services, I introduce the concept of fuzzy set. To provide users with the most appropriate service by ridding of unnecessary properties through the process of reduction, I also use the concept of rough set.

• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.408-414
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• 2013

The famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems may become unstable in non-stationary noise environment. To solve this problem, Sun's algorithm and Akhtar's algorithm are developed based on modifying the reference signal in update of FxLMS algorithm, but these two algorithms have dissatisfactory stability in dealing with sustaining impulsive noise. In proposed algorithm, probability estimation and zero-crossing rate (ZCR) control are used to improve the stability and performance, at the same time, an optimal parameter selection based on fuzzy system is utilized. Computer simulation results prove the proposed algorithm has faster convergence and better stability in non-stationary noise environment.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.1-8
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• 2009

This paper proposes a new document clustering system using fuzzy logic-based genetic algorithm (GA) and semantic vector expansion technology. It has been known in many GA papers that the success depends on two factors, the diversity of the population and the capability to convergence. We use the fuzzy logic-based operators to adaptively adjust the influence between these two factors. In traditional document clustering, the most popular and straightforward approach to represent the document is vector space model (VSM). However, this approach not only leads to a high dimensional feature space, but also ignores the semantic relationships between some important words, which would affect the accuracy of clustering. In this paper we use latent semantic analysis (LSA)to expand the documents to corresponding semantic vectors conceptually, rather than the individual terms. Meanwhile, the sizes of the vectors can be reduced drastically. We test our clustering algorithm on 20 news groups and Reuter collection data sets. The results show that our method outperforms the conventional GA in various document representation environments.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.443-451
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• 2017

This paper's objectives are to investigate the criteria for ergonomic evaluation of indoor bike coordinated with virtual images, and to compare HMD-based VR bike with 2D-based one. 12 experts performed delphi method with an aim to determine ergonomic evaluation criteria that were classified into 4 categories(Usability, Emotion, User Values, Reality). 2D-based bike and HMD-based one were compared according to part of final criteria through fuzzy-logic question performed by 20 subjects. Though there were no confidential difference in usability, HMD-based VR bicycle resulted in greater scores than 2D-based one in elements related with emotion, user value and reality statistically. It is expected that this research results will be used as references to evaluate ergonomic design of other indoor exercise equipments combined with VR or AR.