• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.777-784
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• 2006

This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying 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 water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.

• The Korean Journal of Pain
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• v.32 no.2
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• pp.120-128
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• 2019

Background: We aimed to explore the American College of Rheumatology (ACR) 1990 and 2011 fibromyalgia (FM) classification criteria's items and the components of Fibromyalgia Impact Questionnaire (FIQ) to identify features best discriminating FM features. Finally, we developed a combined FM diagnostic (C-FM) model using the FM's key features. Methods: The means and frequency on tender points (TPs), ACR 2011 components and FIQ items were calculated in the FM and non-FM (osteoarthritis [OA] and non-OA) patients. Then, two-step multiple logistic regression analysis was performed to order these variables according to their maximal statistical contribution in predicting group membership. Partial correlations assessed their unique contribution, and two-group discriminant analysis provided a classification table. Using receiver operator characteristic analyses, we determined the sensitivity and specificity of the final model. Results: A total of 172 patients with FM, 75 with OA and 21 with periarthritis or regional pain syndromes were enrolled. Two steps multiple logistic regression analysis identified 8 key features of FM which accounted for 64.8% of variance associated with FM group membership: lateral epicondyle TP with variance percentages (36.9%), neck pain (14.5%), fatigue (4.7%), insomnia (3%), upper back pain (2.2%), shoulder pain (1.5%), gluteal TP (1.2%), and FIQ fatigue (0.9%). The C-FM model demonstrated a 91.4% correct classification rate, 91.9% for sensitivity and 91.7% for specificity. Conclusions: The C-FM model can accurately detect FM patients among other pain disorders. Re-inclusion of TPs along with saving of FM main symptoms in the C-FM model is a unique feature of this model.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.11-18
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• 2002

It is important to maintain the integrity of the personal information stored duplicately in DBs on the internet sites and to reduce the inconvenience of the user's. So it is useful to develop the integrated-DB supporting the independence of the S/W. In this paper, we investigate the defect of the membership management system on the internet site. Based on the result, we model the personal information management system to manage the membership efficiently, and implement the integrated personal information management system, called IPIMS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.568-572
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• 1998

This paper defines Fuzzy Logic Units(FLUs) which are piece wise finite elements in multidimension Euclidean space, and redefines triangular membership functions which are different from those defined in traditional literature. By analyzing FLUs, this paper gives a constructive method of fuzzy rules in fuzzy logic systems based on finite element method. The simulation results of single machine to infinite bus system show the effectiveness of the proposed method in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.488-492
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• 1993

This paper proposes a new approach to the optimization method of a blending process with neural network. The method is based on the error backpropagation learning algorithm for neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a system solver. A fuzzy membership function is used in parallel with the neural network to minimize the difference between measurement value and input value of neural network. As a result, we can guarantee the reliability and stability of blending process by the help of neural network and fuzzy membership function.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.50-58
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• 2002

The test method using simulator to objectively measure the steering feel from several drivers was proposed. It has also described the ideas to analyse the principal factors affecting the steering feel of the driver using the correlation analysis of the measured data and the questionnaire. Proportional Derivative(PD) controller has been used to measure the steering feel, and the control parameters have been selected to obtain the optimal steering feel. Membership frictions of Sugeno fuzzy model are constructed from the assist torque values calculated from PD controller at each steering state. Moreover to verify the performance, this fuzzy controller has been compared with the another fuzzy controller of which membership frictions are derived from the knowledge of drivers. As a result it can be concluded that the proposed fuzzy controller improves the steering feel at each steering state more than any other conventional methods.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.2
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• pp.123-130
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• 2000

Fuzzy control is proposed to regulate cutting force in turning operations under varying cutting conditions. The traditional linear controllers based on crisp mathematical model cannot effectively control cutting force becasue of the nonlinear dynamics of turning operations. The proposed fuzzy controller is based on operator experience and expert knowledge. The membership functions for the inputs and the output of the controller are designed. Cutting force is regulated by adjusting feedrate according to the variation of cutting conditions. The performance of the proposed controller is evaluated by experiments. The results of experiments show that the proposed fuzzy controller has a good cutting force regulation over a wide range of cutting conditions.

• The Transactions of the Korea Information Processing Society
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• v.2 no.2
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• pp.209-214
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• 1995

It is very hard to identify the fuzzy rules and tune the membership functions of the fuzzy reasoning in fuzzy systems modeling .We propose a method which canautomatically identify the fuzzy rules and tune the membership functions of fuzzy reasoning simultaneously using artifical neural network. In this model,fuzzy rules are identified by backpropagation algorithm. The feasibility of the method is simulated by a simple robot manipulator.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.107-112
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• 1996

In this paper it is proposed a new approach to the development of the automatic vision system to examine and repair the steam generator tubes at remote distance. In nuclear power plants workers are reluctant of works in steam generator because of the high radiation environment and limited working space. It is strongly recommended that the examination and maintenance works be done by an automatic system for the protection of the operator from the radiation exposure. Digital signal processors are used in implementing real time recognition and examination of steam generator tubes in the preposed vision system, Performance of proposed digital vision system is illustrated by experiment for similar steam generator model.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.273-275
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• 2006

In this paper, we introduce a new design of fuzzy neural networks using input-output data information of target system. The proposed fuzzy neural networks is constructed by input-output data information and used the center of data distance by HCM clustering to obtain the characteristics of data. A membership function is defined by HCM clustering and is applied input-output dat included each rule to conclusion polynomial functions. We use triangular membership functions and simplified fuzzy inference, linear fuzzy inference, and modified quadratic fuzzy inference in conclusion. In the networks learning, back propagation algorithm of network is used to update the parameters of the network. The proposed model is evaluated with benchmark data.