• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2369-2377
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• 1998

Fault simulators are used to evaluate the quality of a test pattern generated. So far, most fault simulators did not handle bus conflicts properly. We analyzed . all possible bus conflicts when test patterns are applied to a circuit with bus structure and categorized bus conflicts into various types. Also. we proposed an efficient method to identify various types of bus conflicts. The fault simulator which employs the proposed method can evaluate the quality of test patterns generated and also can avoid destruction of bus drivers due to bus conflicts hy warning the use of test patterns which cause bus conflicts. The proposed method can also be incorperated into a test pattern generator so that it can generate conflict-free test patterns.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.1 s.307
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• pp.39-44
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• 2006

Pattern noise in the Delta-Sigma modulator is a well Known phenomenon that intrigued many circuit designers. These noise appear as the modulator output falls into a cyclic mode of operation. This paper addresses the dependence of these tone signal upon the system topologies. Among the four well known single-stage DSM topologies, namely Cascade of Integrators with Feedback Form(CIFB), Cascade of Integrators with Feedforward Form(CIFF), Cascade of Resonators with Feedback Form(CRFB), and Cascade of Resonators with Feedforward Form(CRFF), resonator type DSMs turn out to be more susceptible to the pattern noise than the integrator type. Noise transfer functions of the investigated topologies are also presented.

• The KIPS Transactions:PartA
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• v.12A no.2 s.92
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• pp.87-94
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• 2005

Pattern matching is one of critical parts of Network Intrusion Prevention Systems (NIPS) and computationally intensive. To handle a large number of attack signature fattens increasing everyday, a network intrusion prevention system requires a multi pattern matching method that can meet the line speed of packet transfer. In this paper, we analyze Snort, a widely used open source network intrusion prevention/detection system, and its pattern matching characteristics. A multi pattern matching method for NIPS should efficiently handle a large number of patterns with a wide range of pattern lengths and case insensitive patterns matches. It should also be able to process multiple input characters in parallel. We propose a multi pattern matching hardware accelerator based on Shift-OR pattern matching algorithm. We evaluate the performance of the pattern matching accelerator under various assumptions. The performance evaluation shows that the pattern matching accelerator can be more than 80 times faster than the fastest software multi-pattern matching method used in Snort.

• Education of Primary School Mathematics
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• v.26 no.1
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• pp.45-63
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• 2023

The activity of finding rules is useful for enhancing the algebraic thinking of elementary school students. This study analyzed the pattern activities of a finding rules unit in 10 different government-authorized mathematics curricular materials for fourth graders aligned to the 2015 revised national mathematics curriculum. The analytic elements included three main activities: (a) activities of analyzing the structure of patterns, (b) activities of finding a specific term by finding a rule, and (c) activities of representing the rule. The three activities were mainly presented regarding growing numeric patterns, growing geometric patterns, and computational patterns. The activities of analyzing the structure of patterns were presented when dealing mainly with growing geometric patterns and focused on finding the number of models constituting the pattern. The activities of finding a specific term by finding a rule were evenly presented across the three patterns and the specific term tended to be close to the terms presented in the given task. The activities of representing the rule usually encouraged students to talk about or write down the rule using their own words. Based on the results of these analyses, this study provides specific implications on how to develop subsequent mathematics curricular materials regarding pattern activities to enhance elementary school students' algebraic thinking.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.8 no.3
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• pp.40-47
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• 1997

안테나의 특성은 셀룰러 시스템의 서비스 품질에 직접적으로 영향을 미치는 요소가 되며, 특히 섹터화로 인한 용량의 증가를 기할 수 있는 CDMA 시스템의 경우에는 매우 중요한 의미를 갖는다. 본 논문의 지향성 안테나를 이용하여 전력 제어된 CDMA 셀을 섹터화할 경우 얻을 수 있는 용량의 이득을 논의한다. 용량의 측면에서 이상적인 안테나 패턴과 비교할 때 실제 안테나 패턴의 역방향 링크 용량 효율을 해석적으로 구한다. 셀룰러 CDMA 시스템의 용량은 동일한 주파수 대역을 사용할 수 있는 최대 통신 채널의 수이므로 용량 효율은 결국 주파수 스펙트럼 이용 효율을 의미한다. 측정된 반사판 섹터 안테나 패턴을 대상으로 분석한 결과를 제시하고 패턴 특성과 섹터 셀의 배치 구조에 따라 용량 효율이 86~97%로 변화함을 보인다.

• Review of KIISC
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• v.5 no.3
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• pp.5-14
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• 1995

본 고에서는 정교한 칼라 복사기와 디지탈 영상처리 기법의 발달과 함께 보안성에 대한 요구가 증대되고 있는 크레딧카드, 은행카드, 운전면허, Membership 카드 등에 적용될 수 있는 패턴인식기법에 대해 알아본다. 개인 ID의 확인을 위해 사용될 수 있는 패턴들은 지문, 얼굴모양, 홍채패턴, 서체, 손가락 구조, 음성패턴, 타이핑 리듬 등이다. 이들 특성들로부터 개인의 ID를 확인할 수 있는 방법들을 기존의 제안들을 중심으로 살펴본다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2188-2195
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• 1999

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neuralnet, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Means) arithmetics, to decide whether a requested call not to be trained in learning phase to be connected or not. The system generates the estimated traffic pattern for the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmetics. The input to the NN is the vector consisted of traffic parameters which are the means and variances of the number of cells arriving in decision as to whether to accept or reject a new call depends on whether the NN is used for decision threshold(+0.5). This method is a new technique for call admission control using the membership values as traffic parameter which declared to CAC at the call set up stage, and this is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simulations, it is founded the performance of the suggested method outperforms compared to the conventional NN method.