• Korean Journal of Medicinal Crop Science
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• v.12 no.5
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• pp.401-405
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• 2004

Karyotypes were established in the eight Korean native species of the genus Iris. Chromosome numbers were 2n=50 in I. koreana and 2n=42 in I. uniflora var. carinata and their karyotype formulas were K = 2n = 50 = 14m + 28sm + 8st and K = 2n = 42 = 16m + 26sm, respectively. I. dichotoma and I. pseudoacorus were diploids of 2n=34. However, they showed different karyotype formulas: K = 2n = 34 = 26m + 6sm + 2st in I. dichotoma and K = 2n = 34 = 8m + 24sm + 2st in I. pseudoacorus. I. setosa, and I. pallasii var. chinensis carried the same chromosome numbers of 2n=40, but they showed different patterns of karyotype formula: K = 2n = 40 = 22m + 14sm + 4st in I. setosa and K = 2n = 40 = 26m + 12sm + 2st in I. pallasii var. chinensis. I. sanguinea was a diploid of 2n=28 and the karyotype formula was K = 2n = 28 = 14m + 14sm. I. ensata var. spontanea was a diploid of 2n=24 and the karyotype formula was K = 2n = 24 = 10m + 14sm. Each species showed characteristic chromosome composition with a pair of satellite chromosome except I. koreana with three pairs of satellite chromosomes. The chromosomes of I. dichotoma and I. uniflora were comparatively short, while the chromosomes of I. ensata were remarkably bigger than those of other species. These cytological data will give a useful information for the identification and breeding program of the Iris plants.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.46-51
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• 2004

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.233-238
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• 2016

In this paper, we propose the algorithms of processing the uncertainty data using data fusion for the next generation intrusion detection. In the next generation intrusion detection, a lot of data are collected by many of network sensors to discover knowledge from generating information in cyber space. It is necessary the data fusion process to extract knowledge from collected sensors data. In this paper, we have proposed method to represent the uncertainty data, by classifying where is a confidence interval in interval of uncertainty data through feature analysis of different data using inference method with Dempster-Shafer Evidence Theory. In this paper, we have implemented a detection experiment that is classified by the confidence interval using IRIS plant Data Set for anomaly detection of uncertainty data. As a result, we found that it is possible to classify data by confidence interval.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1011-1014
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• 2005

In this paper, we propose a method that extracts an invariant biometric key in order to apply this biometric key to the crypto-biometric system. This system is a new authentication architecture which can improve the security of current cryptographic system and solve the problem of stored template protection in conventional biometric system, also. To use biometric information as a cryptographic key in crypto-biometric system, same key should be generated from the same person. However, it is difficult to obtain such an invariant biometric key because biometric data is sensitive to surrounding environments. The proposed method solves this problem by clustering Iris Codes obtained by using independent component analysis (ICA).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.245-248
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• 2003

In this paper, we propose a learning method of the performance improvement of the LVQ network using the radios of the hypersphere with the n-dimensional input vectors. The proposed method determines the reference vectors using the radius of the hypersphere include n+1 set of input vectors in the same class. In order to verify the effectiveness of the proposed method, we performed experiments on the Fisher's IRIS data. The experimental results showed that the proposed method improves considerably on the performance of the conventional LVQ network.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.460-465
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• 2006

This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.5
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• pp.280-288
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• 2003

In this paper, we propose a design method of the LVQ network using the SOM. The proposed method determines subclasses and initial reference vectors of the LVQ network using the SOM. The efficacy of the proposed method is verified by means of simulations on iris data of Fisher and character recognition. The results show that the proposed method improves considerably on the performance of the conventional LVQ network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.322-325
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• 2002

We proposed a method to improve KCN's problems. Proposed method adjusts neighborhood and teaming rate by fuzzy logic system. The input of fuzzy logic system used a distance and a change rate of distance. The output was used by site of neighborhood and learning rate. The rule base of fuzzy logic system was taken by using KCN simulation results. We used Anderson's Iris data to illustrate this method, and simulation results showed effect of performance.