• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.239-243
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• 2001

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.171-175
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• 2000

Neural networks are in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.108-112
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• 2000

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1237-1239
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• 1996

3-D object recognition and restoration independent of the translation and rotation using an ultrasonic sensor array, neural networks and invariant moment are presented. Using invariant moment vectors on the acquired $16{\times}8$ pixel data, 3-D objects can be classified by SOFM(Self Organizing Feature Map) neural networks. Invariant moment vectors kept constant independent of the translation and rotation. The experiment result shows the suggested method can be applied to the environment recognition.

• KSBB Journal
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• v.20 no.4
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• pp.291-298
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• 2005

Two-dimensional (2D) spectrofluorometer is often used to monitor various fermentation processes. The change in fluorescence intensities resulting from various combinations of excitation and emission wavelengths is investigated by using a spectra subtraction technique. But it has a limited capacity to classify the entire fluorescence spectra gathered during fermentations and to extract some useful information from the data. This study shows that the self-organizing map (SOM) is a useful and interpretative method for classification of the entire gamut of fluorescence spectral data and selection of some combinations of excitation and emission wavelengths, which have useful fluorometric information. Some results such as normalized weights and variances indicate that the SOM network is capable of interpreting the fermentation processes of S. cerevisiae and recombinant E. coli monitored by a 2D spectrofluorometer.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.72-79
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• 2010

A EAARS(Electronic Attendance-Absence Recording System) is the important LSS(Learning Support System) for blending a on-line learning in the face-to-face classroom. However, the EAARS based on the smart card can not identify a real owner of the checked card. Therefore, we develop the CS(Client-Sever) system that manages the attendance-absence checking automatically, which is used the self-organizing neural network for the face recognition. A client system creates the ID file by extracting the face feature, a server system analyzes the ID file sent from client system, and performs a student identification by using the Recognized weight file saved in Database. As a result, The proposed CS EAARS shows the 92% efficiency in the CS environment that includes the various face image database of the real classroom.

• Korean Journal of Environmental Biology
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• v.25 no.2
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• pp.149-157
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• 2007

This study focused on patterning waterbirds occurrences at the western costal area of the Korean Peninsula in winter and relating the occurrence patterns with their environmental factors. Waterbird communities were monitored at 10 different study areas, and the composition of land cover as environmental factors was estimated at each study area. Overall dabbling ducks were the most abundant with 84% of total individuals, followed by shorebird and diving ducks. Species Anae platyrhynchos was the first dominant species, and Anas formosa was the second one. Self-organizing map (SOM), an unsupervised artificial neural network, was applied for patterning wintering waterbird communities, and identified 6 groups according to the differences of communities compositions. Each group reflected the differences of indicator species as well as their habitats.

• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1484-1486
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• 2001

In this study, The underwater object recognition using ultrasonic sensor fabricated with porous PZT-Polymer 3-3 type composites and invariant moment vector and SOFM(Self Organizing Feature Map) neural networks are presented. The recognition rates for the training data and the testing data were 98% and 94%, respectively.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.573-575
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• 2004

This study presents a new customer segmentation method based on features that can predict the customer's buying behavior. In this method, we consider all variables that can affect the customer's buying behavior including demographics, psychographics, technographics, transaction pattern-related variables, etc. We define several features which are the combination of variables with the interaction effect by using C5.0, use SOM (Self-Organizing Map) neural networks in odor to extract the feature's patterns and classify, and then make features' rules using C5.0 far the prediction of customer buying behavior