• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.57-60
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• 1998

Darwin Machine은 자기 자신의 구조를 전자적인 속도로 진화해 나가는 하드웨어로서 복잡한 구조와 성질으 진화 기법을 사용하여 만들어 나가는 진화공학(Evolutionary Engineering)의 한 예이다. 하드웨어가 전자적인 속도로 진화하기 위해서는 각각으리 하드웨어 구성요소들이 병렬적으로 작동해햐 하는데 셀룰라 오토마타는 이러한 문제를 해결하는 적합한 구조이며, 하드췌어에 쉽게 이식할 수 있는 장점이 있다. 신경망의 학습 능력과 진한 연산을 이용하면 효율적인 진화를 유도할 수 있다. 본 논문에서는 이러한 하드웨어 구현을 위한 셀룰라 오토마타에 기반한 신경망을 보이고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.179-182
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• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.10-14
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• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.31-36
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• 2010

Many clustering algorithms have been used in diverse fields. When we need to group given data set into clusters, many clustering algorithms based on similarity or distance measures are considered. Most clustering works have been based on hierarchical and non-hierarchical clustering algorithms. Generally, for the clustering works, researchers have used clustering algorithms case by case from these algorithms. Also they have to determine proper clustering methods subjectively by their prior knowledge. In this paper, to solve the subjective problem of clustering we make empirical comparisons of popular clustering algorithms which are hierarchical and non hierarchical techniques using Silhouette measure. We use silhouette information to evaluate the clustering results such as the number of clusters and cluster variance. We verify our comparison study by experimental results using data sets from UCI machine learning repository. Therefore we are able to use efficient and objective clustering algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1092-1098
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• 2004

Today the demand of the optical communication components has been increased. Zirconia Ferrule has become the one of the most important elements because it determines transmission efficiency and quality of information in the optical communication system. Grinding is the major process in the ferrule manufacturing process which require high processing precision. In this reseach, specially designed spindle, chucking system, loading & unloading system and cooling system, as a supporting experimental equipment for development of an Intelligent Coaxial Grinding System (ICGS) for Zirconia Ferrule processing, is developed. We are also analized the adaptability of ICGS in practical use, through the way of evaluation for the performance of the each systems above.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.485-491
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• 1998

이산사건 시스템은 시간의 이산순간에 상태변화가 발생하는 시스템으로서 공정제어, Robotics, 교통시스템, Flexible Manufacturing System, 통신등 많은 분야의 시스템이 이산사건 시스템들이지만 아직도 포괄적이고 융통성 있는 제어이론이 연구되지 않았다. 본 연구는 특히 Real-Time Temporal Logic Framework(RTTL)에서 비결정적으로 발생되어지는 확정적인 사건들로써 유발되어지는 비결정적 이산사건 시스템의 모델링 방법을 제시하였다. 이 방법을 두 개의 machine들로 구성된 Flexible Manufacturing System(FMS)에 적용하여 설명하였다. 이 방법은 복잡한 이산사건 시스템의 모델링을 모듈화하여 간편하게 표현 할 수 있는 우수한 특성을 가지고 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.597-608
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• 1998

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide early warnings in rolling mill. Because dynamics of rolling mill is non-linear. This paper proposes a new method for diagnosis of rolling mill using wavelet to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern datas. And we design a nero-fuzzy model that diagnose a rolling mill using this data. Validity of the new method is asserted by numerical simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.331-334
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• 2006

Original Support Vsctor Machines (SVMs) by Vapnik were used for binary classification problems. Some researchers have tried to extend original SVM to multiclass classification. However, their studies have only focused on classifying samples into nominal categories. This study proposes a novel multiclass SVM model in order to handle ordinal multiple classes. Our suggested model may use less classifiers but predict more accurately because it utilizes additional hidden information, the order of the classes. To validate our model, we apply it to the real-world bond rating case. In this study, we compare the results of the model to those of statistical and typical machine learning techniques, and another multi class SVM algorithm. The result shows that proposed model may improve classification performance in comparison to other typical multiclass classification algorithms.