• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.608-615
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• 2006

This paper proposes a fuzzy model tree consisting of local linear models using fuzzy cluster for data modeling. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. As a final step, data prediction is performed with a linear model having the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional model tree and artificial neural networks.

• Fibers and Polymers
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• v.7 no.2
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• pp.203-212
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• 2006

The purpose of this study was to examine the effects of the structural properties of plain knit fabrics on the subjective perception of textures, sensibilities, and preference among consumers. This study, then, aimed to provide useful information with respect to planning and designing knitted fabrics by predicting the subjective characteristics analyzed according to their structural properties. For this purpose, we employed statistical analysis tools, such as factor and regression analysis and an adaptive-network-based fuzzy inference system(ANFIS), thereby combining the merits of fuzzy and neural networks and presupposing a non-linear relationship. Through factor analysis, we also categorized the subjective textures into 'roughness', 'softness', 'bulkiness' and 'stretch-ability' with R2=70.32%: and categorized the sensibilities into 'Stable/Neat', 'Natural/Comfortable' and 'Feminine/Elegant' with R2=68.12%. We analyzed subjective textures, sensibilities, and preference with ANFIS, assuming non-linear relationships; consequently, we were able to generate three or four fuzzy rules using wool/rayon fiber content and loop length as input data. The textures of roughness and softness exhibited a linear relationship, but other subjective characteristics demonstrated a non-linear input-output relationship. Compared with linear regression analysis, the ANFIS exhibited had higher predictive power with respect to predicting subjective characteristics.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.562-568
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• 2014

There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.341-346
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• 2013

In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.355-365
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• 1998

The purposes of this study are to propose a network for the characterizing of the input data and to show how to design predictive neural net재가 expert system which doesn't need previous knowledge base. We derived this network from the radial basis function networks(RBFN), and named it as a statistical EBFN. The proposed network can replace the statistical methods for analyzing dynamic relations between target disease and other parameters in medical studies. We compared statistical RBFN with the probabilistic neural network(PNN) and fuzzy logic(FL). And we testified our method in the diabetes prediction and compared our method with the well-known multilayer perceptron(MLP) neural network one, and showed good performance of our network. At last, we developed the diabetes prediction expert system based on the proposed statistical RBFN without previous knowledge base. Not only the applicability of the characterizing of parameters related to diabetes and construction of the diabetes prediction expert system but also wide applicabilities has the proposed statistical RBFN to other similar problems.

• Science of Emotion and Sensibility
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• v.12 no.1
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• pp.77-86
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• 2009

A comfort evaluation of car seat clothing has been proposed for high comforts interior seat clothing. Car seat covers have received wide spread attention due to their man-machine interface working. And then, it will be necessary for measurements on delicate basic mechanical-properties, which closely relate with human touch feeling of its materials. In this research, we have utilized $KES-FB^{(R)}$(Kawabata Evaluation System) series, $^ST300{(R)}$ analogue softness tester and friction tester for measurement a physical properties. In order to consider both kansei and physical properties on interior seat covers, we firstly have established subjective words of judgement for the seat covers. Secondly, related them to the objective measurement of physical properties. Each kansei-language has clearly defined as 'Softness', 'Elasticity', 'Volume' and 'Stickiness' for the adjectives of leather car seat covers. These technical terms have correlated to physical properties in other words, h (mm), bending moment ($gf^*$cm/cm), To-Tm (mm) and ${\mu}$. At this time, fuzzy logic has utilized to predict the value of kansei language through physical values. On the basis of this result, finally it is possible to predict quality index of car seat covers using neural networks technique. In short, we develop a quality evaluation system of car seat clothing combining four physical quantities with kansei engineering.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.41-53
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• 2000

In this paper, the Maekjin and the Yangdorak Diagnosis algorithm by using a neuro-fuzzy method is proposed and it is implemented on the DSP-based system. Maekjin is measured by 3-channels of the Maekjin board through Maekjin probe which is attached on Chon, Kwan and Chuk of patient's wrist. First, we experiment Chon, Kwan and Chuk, 3-parts simultaneously and second perform one part of Chon, Kwan and Chuk respectively, The experimental results show that the Maekjin signal is measured precisely with any Maekjin probe. In Yangdorak diagnosis, the pulse generated by electric stimulator stimulates a portion of body and the response signal is measured through electrodes which is attached on representative points of 12 kyungmaks. The experimental methods are (1) 1 channel-measure, (2) 2 channels-measure, (3) 6 channels-measure and (4) 24 channels-measure. A fuzzy diagnosis is performed and neural networks is learned using fuzzy values as inputs, and we show that neuro-fuzzy diagnosis method is performed well.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.379-384
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• 2002

This paper represents the mobile robot system remote controlled by PDA(personal digital assistance). So far, owing to the development of internet technologies, lots of remote control methods through internet have been proposed. To control a mobile robot through internet and guide it under unknown environment, We propose a control method activated by PDA. In a proposed system, PDA acts as a user interface to communicate with notebook as a controller of the mobile robot system using TCP/IP protocol, and the notebook controls the mobile robot system. The information about the direction and velocity of the mobile robot feedbacks to the PDA and the PDA send new control method produced from the fuzzy inference engine.