• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.26-37
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• 2001

In order to improve the several problems of the general backpropagation, we propose a method using a fuzzy logic system for automatic tuning of the activation function gain in the backpropagation. First, we researched that the changing of the gain of sigmoid function is equivalent to changing the learning rate, the weights, and the biases. The inputs of the fuzzy logic system were the sensitivity of error respect to the last layer and the mean sensitivity of error respect to the hidden layer, and the output was the gain of the sigmoid function. In order to verify the effectiveness of the proposed method, we performed simulations on the parity problem, function approximation, and pattern recognition. The results show that the proposed method has considerably improved the performance compared to the general backpropagation.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.526-532
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• 2022

With the development of IoT technology, image processing is being utilized in various fields such as image analysis, image recognition, medical industry, and factory automation. Noise is generated in image data from causes such as defect in transmission line. Image noise must be removed because it damages the performance of the image processing application program. Salt and Pepper noise is a representative type of image noise, and various studies have been conducted to remove Salt and Pepper noise. Widely known methods include A-TMF, AFMF, and SDWF. However, as the noise density increases, the performance deteriorates. Thus, this paper proposes an algorithm that performs filtering using a fuzzy logic weight mask only in case of noise after noise determination. In order to prove the noise removal performance of the proposed algorithm, an experiment was performed on images with 10% to 90% noise added and the PSNR was compared.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.431-437
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• 2004

This paper propose a facial recognition method based on an ubiquitous computing that is one of next generation intelligence technology fields. The facial images are acquired by a mobile device so-called cellular phone camera. We consider a mobile security using facial feature extraction and recognition process. Facial recognition is performed by the PCA and fuzzy LDA algorithm. Applying the discrete wavelet based on multi-resolution analysis, we compress the image data for mobile system environment. Euclidean metric is applied to measure the similarity among acquired features and then obtain the recognition rate. Finally we use the mobile equipment to show the efficiency of method. From various experiments, we find that our proposed method shows better results, even though the resolution of mobile camera is lower than conventional camera.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.55-61
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• 2009

This paper proposes a position estimation of a mobile robot based on USN(Ubiquitous Sensor Network) and encoder, and development of tele-operation system using Internet. USN used in experiments is based on ZigBee protocol and has location estimation engine which uses RSSI signal to estimate distance between nodes. By distortion the estimated distance using RSSI is not correct, compensation method is needed. We obtained fuzzy model to calculate more accurate distance between nodes and use encoder which is built in robot to estimate accurate position of robot. Based on proposed position estimation method, tele-operation system was developed. We show by experiment that proposed method is more appropriate for estimation of position and remote navigation of mobile robot through Internet.

• Journal of IKEEE
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• v.18 no.4
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• pp.636-644
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• 2014

This paper is to technically implement the sensing platform for Home-Wellness Robot. First, self-localization technique is based on a smart home and object in a home environment, and IOT(Internet of Thing) between Home Wellness Robots. RF tag is set in a smart home and the absolute coordinate information is acquired by a object included RF reader. Then bluetooth communication between object and home wellness robot provides the absolute coordinate information to home wellness robot. After that, the relative coordinate of home wellness robot is found and self-localization through a stereo camera in a home wellness robot. Second, this paper proposed fuzzy control methode based on a vision sensor for approach object of home wellness robot. Based on a stereo camera equipped with face of home wellness robot, depth information to the object is extracted. Then figure out the angle difference between the object and home wellness robot by calculating a warped angle based on the center of the image. The obtained information is written Look-Up table and makes the attitude control for approaching object. Through the experimental with home wellness robot and the smart home environment, confirm performance about the proposed self-localization and posture control method respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.87-95
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• 1996

A new design scheme for the fuzzy pattern matching classifier (FPMC) is proposed. in conventional design of FPMC, there are no exact information about the membership function of which shape and number critically affect the performance of classifier. So far, a trial and error or heuristic method is used to find membership functions for the input patterns. But each of them have limits in its application to the various types of pattern recognition problem. In this paper, a new method to find the appropriate shape and number of membership functions for the input patterns which minimize classification error is proposed using genetic algorithms(GAs). Genetic algorithms belong to a class of stochastic algorithms based on biological models of evolution. They have been applied to many function optimization problems and shown to find optimal or near optimal solutions. In this paper, GAs are used to find the appropriate shape and number of membership functions based on fitness function which is inversely proportional to classification error. The strings in GAs determine the membership functions and recognition results using these membership functions affect reproduction of next generation in GAs. The proposed design scheme is applied to the several patterns such as tire tread patterns and handwritten alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of Navigation and Port Research
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• v.38 no.3
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• pp.261-268
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• 2014

The route of Korea and China situates stable shipping market by the agreement on maritime transport between two countries. Epecially, the Car-ferry shipping market between Korea and China is growing up the world's largest markets in this situation. But, the rapid growth of markets have the possibility of imbalance between supply and demand. In addition that heavy competition can be arisen. The aim of this study is to analyze the ways to reinforce competitiveness of Car-ferry shipping companies(CFSCs) between Korea and China routes through suggesting importance weights of service factors. Firstly, evaluating service factors of CFSCs between Korea and China routes are selected by reviewing literatures and Delphi method. Secondly, importance weights of service factors are calculated using Fuzzy method. As a result, Shipper and CFSCs between Korea and China routes select 'agility of loading and unloading', 'agility of customs', and 'punctuality of transportation' as the most important factors. On the other hand, the two groups are shown the perception gaps on the factors such as 'reasonable shipping cost', 'voyage speed', 'multimodal transportation', and 'professionality of manpower'. The implication of this study is that Government of Korea and China Have to cooperate agreement for mutual drive towed trailer and customs to speedy transportation.