• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.57-63
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• 2010

The damping parameter of Levenberg-Marquardt algorithm switches between error backpropagation and Gauss-Newton learning and affects learning speed. Fixing the damping parameter induces some oscillation of error and decreases learning speed. Therefore, we propose the way of a variable damping parameter with referring to the alternation of error. The proposed method makes the damping parameter increase if error rate is large and makes it decrease if error rate is small. This method so plays the role of momentum that it can improve learning speed. We tested both iris recognition and wine recognition for this paper. We found out that this method improved learning speed in 67% cases on iris recognition and in 78% cases on wine recognition. It was also showed that the oscillation of error by the proposed way was less than those of other algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.429-440
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• 2014

The iris recognition is a biometrics technology to extract and code an unique iris feature from human eye image. Also, it includes the technology to compare with other's various iris stored in the system. On the other hand, eyelashes in iris image are a external factor to affect to recognition rate of iris. If eyelashes are not removed exactly from iris area, there are two false recognitions that recognize eyelashes to iris features or iris features to eyelashes. Eventually, these false recognitions bring out a lot of loss in iris informations. In this paper, in order to solve that problems, we removed eyelashes by gabor filter that using for analysis of frequency feature and improve preservation rate of iris informations. By novel method to extract various features on iris area using angle, frequency, and gaussian parameter on gabor filter that is one of the filters for analysing frequency feature for an image, we could remove accurately eyelashes with various lengths and shapes. As the result, proposed method represents that improve about 4% than previous methods using GMM or histogram analysis in iris preservation rate.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.30-39
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• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.

• The Journal of Korean Medicine
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• v.39 no.2
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• pp.56-63
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• 2018

Objectives: The purpose of this study was to investigate the usefulness of some iris parameters in predicting the 8 constitution Methods: From November 2012 to February 2018, we retrospectively reviewed the medical records of 171 patients who were visited to the Korean Oriental Clinic. We conducted a stepwise binary logistic regression analysis to find the association between Iris parameters and 8 constitutional Medicine. Results: Automic Nerve Wreath Ration (ANWR) was larger and toxic radii was longer in Earth and Wood constitutions than Metal and water constitution. ANWR was larger and pupil are ratio(PAR) was smaller in Wood constitutions than Earth constitutions. And others did not show significant results. Conclusions: This study suggests that the relationship between some of the iris parameters and 8 constitution was significant, but more accurate follow-up study is needed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.180-184
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• 1997

This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

• 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 the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.754-759
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• 2001

In this paper, we propose a TSK(Takagi-Sugeno-Kang)-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy c-Means) clustering, ANFIS(Adaptive Neuro-Fuzzy Inference System) and hybrid GA(Genetic Algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive GA) and RLSE(Recursive Least Square Estimate). Finally, we applied the proposed method to Iris data classificationl problems and obtained a better performance than previous works.

• The Journal of Engineering Geology
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• v.16 no.2 s.48
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• pp.171-178
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• 2006

The seismological observation of Korea began in 1905, and has been run with continuous earthquake network of observation, expanding to the advanced country, but still has some problems in accuracy and speed for report. There are many problems to announce the early warning system for earthquakes and tsunami in the East Sea because most events in the East Sea occur outside the seismic network. Therefore multi-waveform data conversion and composition from the surrounding countries such as Korea, Japan and Far East Russia are requested in order to improve more accurate determination of the earthquake parameters. We used FESNET(Far East Seismic Network) technology to analyze the May 29 and June 1 Earthquakes, and the March 20, 2005 Fukuoka Earthquake in this research, using the data sets of KMA, Japan(JMA/MIED) and IRIS stations. It was found out that use of FESNET resulted in more better outputs than that of a single network, either KMA or JMA stations.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.633-637
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• 2013

A wide range of applications for Foot Print Recognition System is discussed in this paper. The whole concept works under the principle that foot print is a parameter associated with biometrics that is very common as well as distinct. Its foremost application is at the government hospitals in the under developed and third world nations where there aren't the best of facilities. This system can be applied in the maternity ward of the hospitals for the identification or differentiation of the infants. Till date there has been no specialized system adopted for this purpose. The Foot Print Recognition System will overcome all the defects of any biometrics when applied here. Since the child will be very delicate for an iris scan and it will not be able to open its eyes wide or to correctly place its finger print on the sensor since the hands of a new born infant will be closed for a while. The Foot Print Recognition system can also be implemented in temples where there are cases of theft often reported. This can be used to grant access to the karpagraham of the deity by authorized users alone. These 2 applications of FPRS are discussed in this paper.