• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.2
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• pp.87-93
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• 1989

Squared-error Pattern Clustering algorithm used in unsupervised pattern recognition and image processing application demands substantial processing time for operation of feature vector matrix. So, this paper propose the fast squared-error Pattern Clustering Processor using the Residue Number System which have been the nature of parallel processing and pipeline. The proposed Squared-error Pattern Clustering Processor illustrate satisfiable error rate for Cluster number which can be divide meaningful region and about 200 times faster than 80287 coprocessor from experiments result of image segmentation. In this result, it is useful to real-time processing application for large data.

• Journal of Internet Computing and Services
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• v.3 no.3
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• pp.1-9
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• 2002

The various researches are investigated for transforming byte code into objective machine code which can be implemented in the specific processor using classical compiling methods to improve the execution speed of the JAVA language. The code generation techniques using pattern matching can generate more high-quality code than code expansion techniques. We provide, in this research, the standardized pattern describing methods and pattern matching techniques that can be used to generate the register-based inter-language which is for the effective native code generation from byte code. And we designed and realized the inter-code transformer with which we can generate the high-quality register-based inter-code using standardized pattern described formerly.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.1-6
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• 1999

We propose a new algorithm for pattern classification by extracting feature vectors based on Gibbs distributions which are well suited for representing the characteristic of an images. The extracted feature vectors are comprised of 2-D moments which are invariant under translation rotation, and scale of the image less sensitive to noise. This implementation contains two puts: feature extraction and pattern classification First of all, we extract feature vector which consists of an improved 2-D moments on the basis of estimated Gibbs distribution Next, in the classification phase the minimization of the discrimination cost function for a specific pattern determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on SUN ULTRA 10 Workstation Experiment results reveal that the proposed scheme had high classification rate over 98%.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.205-208
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• 2000

In this paper, we design Tracking Filter that is principal component of Frequency Hopping System. This filter can acquire hopping pattern in short time and track it at high speed. This is high Q, narrowband, RF filter whose center frequency is controlled digitally between 30MHz ∼ 88MHz.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.511-512
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• 2012

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1996.11a
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• pp.365-368
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• 1996

카오스 함수인 베이커 함수를 이용하여 불규칙한 상태에 의해 카오스적인 패턴을 발생시키는 패턴 발생기를 구현하였다. 카오스적인 패턴 발생기는 예측할 수 있는 패턴들을 발생시킨다는 점에서 기존의 패턴 발생기와 흡사하지만, 패턴요소의 출력 순서는 결정론적 카오스 함수에 따른다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.95-104
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• 1993

IPA(interpattern association) model is a method of feature extraction using a neural network. Even in the case that the reference patterns are simuklar to one another, this model can recover the reference patterns effectively. However, when the pattern whose feature pixels are lost is used as input, this model can not guarantee perfect recovery of the reference pattern. It is proposed a improved interpattern association(IPA) model for the feature extraction using neural network. The improved IPA model that combines the first interconnection weight matrix of the IPA model with the second additional weight matrix is proposed here to overcome the recovery problem of the original IPA model. The results of computer simulation and optical experiment are advanced.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.319-323
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• 2005

As the software industry develops at a rate speed, anyone can copy or plagiarize without difficulty contents that are becoming digitalized. To make it worse, the development of various contents that be illegally copied and plagiarized are resulting in the increasing infringement on and the plagiarism of the intellectual property. This dissertation tries to put forth the method and the theory to effectively detect any plagiarism of the source code of programs realized in various languages. This dissertation analyzes the advantage and disadvantage of the plagiarism test software, and especially, presents a method to detect possible plagiarism by using the Pattern Matching to overcome its disadvantage. And it also intends to introduce more developed automatic detection system by overcoming the problems with the method of Pattern Matching.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.7
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• pp.55-62
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• 1994

Interpattern association (IPA) model which the interconnection weight matrix(IWM) is constructed by the association between patterns is effective in similar pattern recognitions. But, if the number of reference patterns is increased, the ability of recognition is decreased. Using a hierarchical recognition algorithm which adopts the tree search strategy, we classified reference patterns into sub-groups by similarity. In IPA model, if input includes random noise we make it converge to reference pattern by means of input includes random noise we make it converge to reference pattern by means of increasing the number of pixels of prohibited state in IWM. In relation to reference patterns the pixel of prohibited state made partially prohibited state of no connected state using which is not included common and feature regions by each reference patterns.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.446-452
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• 2002

For most of pattern recognition applications, it is required to correctly recognize patterns even if they have translation variations. In this paper, to achieve the goal of translation invariant pattern recognition, we propose a new generalized translation invariant second-order neural network using a constraint on the weights. The weight constraint is implemented using generalized translation invariant features which are accumulated sums of pixel combinations. Simulation results will be given to demonstrate that the proposed second-order neural network has the generalized translation invariant property.