• Title/Summary/Keyword: 패턴 확장

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λ Matrix for Evaluating an Incomplete Bloc Design (불완비블록계획법을 평가하기 위한 λ행렬)

  • Jang, Dae-Heung
    • The Korean Journal of Applied Statistics
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    • v.24 no.4
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    • pp.647-656
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    • 2011
  • Incidence matrix is a useful tool for presenting incomplete block designs; however, it is inadequate to use only an incidence matrix in examining whether a certain incomplete block design becomes a balanced incomplete block design or not. We can use a structural matrix as a useful tool to show whether a certain incomplete block design becomes a balanced incomplete block design or not. We propose an augmented incidence matrix and ${\lambda}$ matrix as another tools for evaluating incomplete block designs. Through the augmented incidenc matrix and ${\lambda}$ matrix, we can ascertain whether a certain incomplete block design becomes a balance incomplete block design or not.

Application Examples Applying Extended Data Expression Technique to Classification Problems (패턴 분류 문제에 확장된 데이터 표현 기법을 적용한 응용 사례)

  • Lee, Jong Chan
    • Journal of the Korea Convergence Society
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    • v.9 no.12
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    • pp.9-15
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    • 2018
  • The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

WORKGLOW: A P2P-based Web Service Orchestration Supporting Complex Workflow Patterns (복잡한 워크플로우 패턴들을 지원하는 P2P 기반 웹 서비스 오케스트레이션)

  • Tran, Doan Thanh;Hoang, Nam Hai;Choi, Eun-Mi
    • Journal of the Korea Society for Simulation
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    • v.16 no.4
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    • pp.77-86
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    • 2007
  • Web services are considered as the critical component in the business plans of corporations as they offers the potential for creating highly dynamic and versatile distributed applications that span across business boundaries. Web Service Orchestration studies composition of already-existing web services to create new value-added services. The composite web services could be executed in a centralized or peer-to-peer(P2P) orchestration model. Compared with centralized-orchestration model, the P2P-based orchestration model provides better scalability, reliability, and performance for the overall services. However, recent P2P-orchestration solutions have limitation in supporting complex workflow patterns. Therefore, they could not effectively handle sophisticated business workflow, which contains complex workflow patterns. In this paper, we propose the WORKGLOW system, which can deal with complex workflow patterns while it is able to perform composite services in P2P orchestration manner. Comparing with centralized orchestration systems, the WORKGLOW brings up more business logic advantages, better performance, and higher flexibility with only a little overhead.

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Efficient Dynamic Weighted Frequent Pattern Mining by using a Prefix-Tree (Prefix-트리를 이용한 동적 가중치 빈발 패턴 탐색 기법)

  • Jeong, Byeong-Soo;Farhan, Ahmed
    • The KIPS Transactions:PartD
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    • v.17D no.4
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    • pp.253-258
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    • 2010
  • Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

A Study of Pattern Classification System Design Using Wavelet Neural Network and EEG Signal Classification (웨이블릿 신경망을 이용한 패턴 분류 시스템 설계 및 EEG 신호 분류에 대한 연구)

  • Im, Seong-Gil;Park, Chan-Ho;Lee, Hyeon-Su
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.39 no.3
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    • pp.32-43
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    • 2002
  • In this paper, we propose a pattern classification system for digital signal which is based on neural networks. The proposed system consists of two models of neural network. The first part is a wavelet neural network whose role is a feature extraction. For this part, we compare existing models of wavelet networks and propose a new model for feature extraction. The other part is a wavelet network for pattern classification. We modify the structure of previous wavelet network for pattern classification and propose a learning method. The inputs of the pattern classification wavelet network is connection weights, dilation and translation parameters in hidden nodes of feature extraction network. And the output is a class of the signal which is input of feature extraction network. The proposed system is, applied to classification of EEG signal based on frequency.

Design of Transmission Lines with Arbitrary Reflection Responses Using Synthesis Method for Spatially Adaptive Source Distribution (공간적응형 소스 분포 합성법을 사용한 임의의 반사응답을 갖는 전송선로 설계)

  • Park, Ui-Jun
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.39 no.5
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    • pp.243-250
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    • 2002
  • In the synthesis of the current source distribution function of an array antenna with the arbitrary radiation pattern, the Woodward-Lawson sampling method has been mainly used for the synthesis of an even function lobe pattern. In this paper, the method is extended to the synthesis of the odd function pattern and then the optimum synthesis method for the nonlinear source distribution function is proposed. The proposed method is applied to the design of nonuniform transmission lines with arbitrary reflection responses. The both dispersive impedance profiles of single and coupled nonuniform lines with arbitrary reflection responses are directly synthesized by the sampled values of a reflected spectral pattern which is optimally shaped by a perturbation of its complex null positions, hence removing the conventional step-by-step segmentation process and global optimization routines. The control problem in the case that all of port impedances are identical is also solved. The generality of the proposed method is verified by a filter design with the controlled arbitrary passband

A Weighted Fuzzy Min-Max Neural Network for Pattern Classification (패턴 분류 문제에서 가중치를 고려한 퍼지 최대-최소 신경망)

  • Kim Ho-Joon;Park Hyun-Jung
    • Journal of KIISE:Software and Applications
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    • v.33 no.8
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    • pp.692-702
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    • 2006
  • In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

Dynamic Hand Gesture Recognition Using CNN Model and FMM Neural Networks (CNN 모델과 FMM 신경망을 이용한 동적 수신호 인식 기법)

  • Kim, Ho-Joon
    • Journal of Intelligence and Information Systems
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    • v.16 no.2
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    • pp.95-108
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    • 2010
  • In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

Design and Implementation of the Intrusion Detection Pattern Algorithm Based on Data Mining (데이터 마이닝 기반 침입탐지 패턴 알고리즘의 설계 및 구현)

  • Lee, Sang-Hoon;Soh, Jin
    • The KIPS Transactions:PartC
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    • v.10C no.6
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    • pp.717-726
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    • 2003
  • In this paper, we analyze the associated rule based deductive algorithm which creates the rules automatically for intrusion detection from the vast packet data. Based on the result, we also suggest the deductive algorithm which creates the rules of intrusion pattern fast in order to apply the intrusion detection systems. The deductive algorithm proposed is designed suitable to the concept of clustering which classifies and deletes the large data. This algorithm has direct relation with the method of pattern generation and analyzing module of the intrusion detection system. This can also extend the appication range and increase the detection speed of exiting intrusion detection system as the rule database is constructed for the pattern management of the intrusion detection system. The proposed pattern generation technique of the deductive algorithm is used to the algorithm is used to the algorithm which can be changed by the supporting rate of the data created from the intrusion detection system. Fanally, we analyze the possibility of the speed improvement of the rule generation with the algorithm simulation.

An Efficient Method to Find Accurate Spot-matching Patterns in Protein 2-DE Image Analysis (단백질 2-DE 이미지 분석에서 정확한 스팟 매칭 패턴 검색을 위한 효과적인 방법)

  • Jin, Yan-Hua;Lee, Won-Suk
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.5
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    • pp.551-555
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    • 2010
  • In protein 2-DE image analysis, the accuracy of spot-matching operation which identifies the spot of the same protein in each 2-DE gel image is intensively influenced by the errors caused by the various experimental conditions. This paper proposes an efficient method to find more accurate spot-matching patterns based on multiple reference gel images in spot-matching pattern analysis in protein 2-DE image analysis. Additionally, in order to improve the reduce the execution time which is increased exponentially along with the increasing number of gel images, a "partition then extension" framework is used to find spot-matching pattern of long length and of higher accuracy. In the experiments on real 2-DE images of human liver tissue are used to confirm the accuracy and the efficiency of the proposed algorithm.