• Spatial Information Research
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• v.7 no.2
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• pp.223-236
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• 1999

Recently, the need of wast incineratory has been recognized. However, the waste incinerator is considered the typical example of NYMB syndrome as a locally unwanted facilities. Therefore, the site selection of waste incineratory should be determined very carefully with consideration of various location factors. The purpose of this study is to provide a new decision-making process model for site selection that provides a rational and a systematic way. The fuzzy set theory and AHP theory, which have merits to overcome uncertainly and complexity of spatial data, are applied to select candidate sites for the waste incineratory. The method is able to produce a more flexible and objective solution for selecting suitability sites in comparison to rigid boolean logic. The result of this study shows that geographic information systems have clear implication for informing the spatial decision making process.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.142-151
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• 1993

An intelligent mulitiple monitoring system to monitor tool/machining states synthetically was proposed and developed. It consists of 2 fundamental subsystems : the multiple sensor detection unit and the intellignet integrated diagnosis unit. Three signals, that is, spindle motor current, Z-axis motor current, and machining sound were adopted to detect tool/machining states more reliably. Based on the multiple sensor information, the diagnosis unit judges either tool breakage or degree of tool wear state using fuzzy reasoning. Tool breakage is diagnosed by the level of spindle/z-axis motor current. Tool wear is diagnosed by both the result of fuzzy pattern recognition for motor currents and the result of pattern matching for machining sound. Fuzzy c-means algorithm was used for fuzzy pattern recognition. Experiments carried out for drill operation in the machining center have shown that the developed system monitors abnormal drill/states drilling very reliably.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.55-63
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• 2002

It has been known well that Traffic Conflict Technique(TCT) used to evaluate the safety of intersections in the case of shortage of traffic accidents data and surveying time. Because data for using in traffic conflict technique that is collected by trained surveyors, it is rely on the knowledge, experience and the characteristics of them. The data of surveying generate varying result. So, its variance must minimize and then it is considered of calculating in traffic conflict technique however obviously technique to minimize has not developed until now. So, this paper has a focus on the technical method to minimize the variance. For this, it applied the fuzzy reasoning theory to the existed traffic conflict technique that is the most comprehensive method in the country and then developed the new traffic conflict technique model. Fuzzy reasoning theory is a very appropriate method for minimizing the variance among surveyors because it can systematically calculate the uncertainty of surveyors by approximation reasoning structure. The result of analysis from pilot study, the new Procedure in this Paper minimized the variance by 53 Percentiles and it increased the value of conversion factor two times than the exited traffic conflict technique. The method proposed in this paper, it can be used for evaluating the safety of intersection, and before and after analysis of improving Project of black spots.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.17-35
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• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.41-45
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• 2010

Image processing techniques are fundamental in human vision-based image information processing. There have been widely studied areas such as image transformation, image enhancement, image restoration, and image compression. One of research subgoals in those areas is enhancing image information for the correct information retrieval. As a fundamental task for the image recognition and interpretation, image enhancement includes noise filtering techniques. Conventional filtering algorithms may have high noise removal rate but usually have difficulty in conserving boundary information. As a result, they often use additional image processing algorithms in compensation for the tradeoff of more CPU time and higher possibility of information loss. In this paper, we propose a Fuzzy Mask Filtering algorithm that has high noise removal rate but lesser problems in above-mentioned side-effects. Our algorithm firstly decides a threshold based on fuzzy logic with information from masks. Then it decides the output pixel value by that threshold. In a designed experiment that has random impulse noise and salt pepper noise, the proposed algorithm was more effective in noise removal without information loss.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.58-63
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• 2014

Most of light duty AGVs(AGCs) using tape of magnetic for the guide path have digital guidance magnetic sensor. Digital guidance magnetic sensor using magnet-tape is on/off type and has positioning error of magnet-tape as 10~50 mm. AGC using this sensor doesn't induce accurate position of magnet-line which is magnet-tape because of magnetic field which motor in AGC creates, outer magnetic field, earth's magnetic field, etc. AGC when driving wobbles due to this error and this error can cause path deviation. In this paper, we propose fuzzy inference system for improvement of bipolar analog magnetic guidance sensor performance. Fuzzy is suitable in term of fault tolerance, uncertainty tolerance, real-time operation, and Nonlinearity as compared with other algorithms. In previous research, we produced bipolar magnetic guidance sensor and we set the threshold in order to calculate digital values of magnet position. Fuzzy inference system is designed using outputs of Analog hall sensors. Magnet position calculated by digital method is improved by outputs of this system. In result, proposed method was verified by improving performance of magnetic guidance sensor.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of the Korean Geotechnical Society
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• v.23 no.11
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• pp.109-117
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• 2007

Uncertainty is pervasive in rock slope stability analysis due to various reasons and subsequently it may cause serious rock slope failures. Therefore, the importance of uncertainty has been recognized and subsequently the probability theory has been used to quantify the uncertainty since 1980's. However, some uncertainties, due to incomplete information, cannot be handled satisfactorily in the probability theory and the fuzzy set theory is more appropriate for those uncertainties. In this study the random variable is considered as fuzzy number and the fuzzy set theory is employed in rock slope stability analysis. However, the previous fuzzy analysis employed the approximate method, which is first order second moment method and point estimate method. Since previous studies used only the representative values from membership function to evaluate the stability of rock slope, the approximated analysis results have been obtained in previous studies. Therefore, the Monte Carlo simulation technique is utilized to evaluate the probability of failure for rock slope in the current study. This overcomes the shortcomings of previous studies, which are employed vertex method. With Monte Carlo simulation technique, more complete analysis results can be secured in the proposed method. The proposed method has been applied to the practical example. According to the analysis results, the probabilities of failure obtained from the fuzzy Monte Carlo simulation coincide with the probabilities of failure from the probabilistic analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.231-236
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• 2011

The binarization algorithm frequently applies to one part of the preprocessing phase for a variety of image processing techniques such as image recognition and image analysis, etc. So it is important that binarization algorithm is determined by the selection of threshold value for binarization in image processing. The previous algorithms could get the proper threshold value in the case that shows all the difference of brightness between background and object, but if not, they could not get the proper threshold value. In this paper, we propose the efficient fuzzy binarization method which first, segments the brightness range of gray_scale images to 2 intervals to perform car license plate binarization and applies fuzzy member function to each intervals. The experiment for performance evaluation of the proposed binarization algorithm showed that the proposed algorithm generates the more effective threshold value than the previous algorithms in car license plate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.585-590
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• 2012

Coronary Intervention treatment has become the core that is the test of cardiac catheterization to conduct treatment with Coronary Arteriography. Operators must be careful in Coronary Intervention treatment because the stent is inserted into the point of narrowing of blood vessel. So, the operator must correctly recognize the path of blood vessel to deal with the problems which are damages and ruptures of blood vessel, and there would be some errors of finding the path of blood vessel by bad qualify of the image. Therefore in this paper, median filtering is conducted by preprocessing to evaluate the performance of the effect of noise of the image that affects quality of the image and Fuzzy Edge Extraction Techniques is tested by using Soble Edge Extraction Techniques to compare the performance with The Fuzzy Edge Extraction Techniques. In result, the performance, removing the noise and extracting the signal of Fuzzy Edge Extraction Techniques using median filtering, demonstrates the superiority.