• Title/Summary/Keyword: Pattern Vector

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A Study on a neural-Net Based Call admission Control Using Fuzzy Pattern Estimator for ATM Networks (ATM망에서 퍼지 패턴 추정기를 이용한 신경망 호 수락제어에 관한 연구)

  • 이진이;이종찬;이종석
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.10a
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    • pp.173-179
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    • 1998
  • This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neural net, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Menas) arithmatics, to decide whether a requested call that is not trained in learning phase to be connected or not. The system generates the estimated traffic pattern of 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 arithmatics. The input to the NN is the vector consisted of traffic parameters which is the means and variances of the number of cells arriving inthe interval. After training(using error back propagation algorithm), when the NN is used for decision making, the decision as to whether to accept or reject a new call depends on whether the output is greater or less then decision threshold(+0.5). This method is a new technique for call admi sion control using the membership values as traffic parameter which declared to CAC at the call set up stage, and 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 simmulation. it is founded the performance of the suggested method outforms compared to the conventional NN method.

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Study on a Novel Switching Pattern Current Control Scheme Applied to Three-Phase Voltage-Source Converters

  • Zhao, Hongyan;Li, Yan;Zheng, Trillion Q.
    • Journal of Power Electronics
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    • v.17 no.6
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    • pp.1563-1576
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    • 2017
  • This paper presents a novel switching pattern current control (SP-CC) scheme, which is applied in three-phase voltage-source converters (VSCs). This scheme can select the optimal output switching pattern (SP) by referring the basic principle of space vector modulation (SVM). Moreover, SP-CC is a method without a carrier wave. Thus, the implementation process is concise and easy. When compared with the conventional hysteresis current control (C-HCC) and the space vector-based hysteresis current control (SV-HCC), the SP-CC has the performances of faster dynamic response of C-HCC and less switching number (SN) of SV-HCC. In addition, it has less harmonic contents in the three-phase current, along with a lower switching loss and a higher efficiency. Moreover, the hysteresis bandwidth and Clarke conversion are not required in the SP-CC. The effectiveness of the presented SP-CC is verified by simulation and experimental test results. In addition, the advantages of the SP-CC, when compared with the C-HCC and SV-HCC, are verified as well.

The Study on Dynamic Images Processing for Finger Languages (지화 인식을 위한 동영상 처리에 관한 연구)

  • Kang, Min-Ji;Choi, Eun-Sook;Sohn, Young-Sun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.2
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    • pp.184-189
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    • 2004
  • In this paper, we realized a system that receives the dynamic images of finger languages, which is the method of intention transmission of the hearing disabled person, using the white and black CCD camera, and that recognizes the images and converts them to the editable text document. We use the afterimage to draw a sharp line between indistinct images and clear images from a series of inputted images, and get the character alphabet from the away of continuous images and output the accomplished character to the word editor by applying the automata theory. After the system removes the varied wrist part from the data of clean image, it gets the controid point of hand by the maximum circular movement method and recognizes the hand that is necessary to analyze the finger languages by applying the circular pattern vector algorithm. The system abstracts the characteristic vectors of the hand using the distance spectrum from the center of the hand and it compares the characteristic vector of inputted pattern from the standard pattern by applying the fuzzy inference and recognizes the movement of finger languages.

A Fast Block Matching Motion Estimation Algorithm by using an Enhanced Cross-Flat Hexagon Search Pattern (개선된 크로스-납작한 육각 탐색 패턴을 이용한 고속 블록 정합 움직임 예측 알고리즘)

  • Nam, Hyeon-Woo
    • Journal of the Korea Society of Computer and Information
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    • v.13 no.7
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    • pp.99-108
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    • 2008
  • For video compression, we have to consider two performance factors that are the search speed and coded video's quality. In this paper, we propose an enhanced fast block matching algorithm using the spatial correlation of the video sequence and the center-biased characteristic of motion vectors(MV). The proposed algorithm first finds a predicted motion vector from the adjacent macro blocks of the current frame and determines an exact motion vector using the cross pattern and a flat hexagon search pattern. From the performance evaluations, we can see that our algorithm outperforms both the hexagon-based search(HEXBS) and the cross-hexagon search(CHS) algorithms in terms of the search speed and coded video's quality. Using our algorithm, we can improve the search speed by up to 31%, and also increase the PSNR(Peak Signal Noise Ratio) by at most 0.5 dB, thereby improving the video quality.

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Image retrieval using block color characteristics and spatial pattern correlation (블록 컬러 특징과 패턴의 공간적 상관성을 이용한 영상 검색)

  • Chae, Seok-Min;Kim, Tae-Su;Kim, Seung-Jin;Lee, Kun-Il
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.9-11
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    • 2005
  • We propose a new content-based image retrieval using a block color co-occurrence matrix (BCCM) and pattern correlogram. In the proposed method, the color feature vectors are extracted by using BCCM that represents the probability of the co-occurrence of two mean colors within blocks. Also the pattern feature vectors are extracted by using pattern correlogram which is combined with spatial correlation of pattern. In the proposed pattern correlogram method. after block-divided image is classified into 48 patterns with respect to the change of the RGB color of the image, joint probability between the same pattern from the surrounding blocks existing at the fixed distance and the center pattern is calculated. Experimental results show that the proposed method can outperform the conventional methods as regards the precision and the size of the feature vector dimension.

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A CHARACTERIZATION OF MINIMAL SEMIPOSITIVITY OF SIGN PATTERN MATRICES

  • Park, S.W.;Seol, H.G.;Lee, S.G.
    • Communications of the Korean Mathematical Society
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    • v.13 no.3
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    • pp.465-473
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    • 1998
  • A real m $\times$ n matrix A is semipositive (SP) if there is a vector x $\geq$ 0 such that Ax > 0, inequalities being entrywise. A is minimally semipositive (MSP) if A is semipositive and no column deleted submatrix of A is semipositive. We give a necessary and sufficient condition for the sign pattern matrix with n positive entries to be minimally semipositive.

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A Person Identification Algorithm Utilizing Hybrid Features : Finger Crease Pattern and Finger Thickness Profiles (손가락 마디지문 패턴을 이용한 개인식별 알고리즘 성능 향상에 관한 연구)

  • 신창호;정희철;이현열;최환수
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.556-559
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    • 1999
  • This paper proposes an hybrid person identification algorithm utilizing finger crease pattern and finger thickness profiles. We have observed that by adding finger thickness profiles as a feature vector, we could improve the performance of the person identification system utilizing only finger crease pattern. We presented the comparative evaluation of the proposed algorithm in detail.

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Fault Detection of Cutting Force in Turning Process using RBF/ART-1 (RBF/ART1을 이용한 선삭에서 절삭력을 이상신호 검출)

  • 임상만;이명재;유봉환
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1994.10a
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    • pp.15-19
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    • 1994
  • The application of neural network for fault dection of cutting force in turning was introduced. This monitoring system consist of a RBF predicton model and a ART-1 pattern classifier. RBF prediction model predict a cutting force signal. Prediction error of predictor is used for a input vector of ART-1 pattern classifier. Prediction error could be successfully performed to fault signal monitoring of ART-1 pattern classifier.

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EEG Pattern Recognition (EEG 패턴인식)

  • Lee, Yong-Gu;Jung, Kyung-Kwon;Eom, Ki-Hwan
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.1017-1018
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    • 2006
  • We measured EEG, extracted the feature vectors using alpha and beta rhythm from the measured EEG and pattern recognition was simulated by using the feature vector and the algorithms which are conventional LVQ and Forward only Counter Propagation Networks. And then the successful rate of pattern class of EEG data had about 76 %.

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Generation of Pattern Classifier using LFSRs (LFSR을 이용한 패턴분류기의 생성)

  • Kwon, Sook-Hee;Cho, Sung-Jin;Choi, Un-Sook;Kim, Han-Doo;Kim, Na-Roung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.6
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    • pp.673-679
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    • 2014
  • The important requirements of designing a pattern classifier are high throughput and low memory requirements, and low cost hardware implementation. A pattern classifier by using Multiple Attractor Cellular Automata(MACA) proposed by Maji et al. reduced the complexity of the classification algorithm from $O(n^3)$ to O(n) by using Dependency Vector(DV) and Dependency String(DS). In this paper, we generate a pattern classifier using LFSR to improve efficiently the space and time complexity and we propose a method for finding DV by using the 0-basic path. Also we investigate DV and the attractor of the generated pattern classifier. We can divide an n-bit DS by m number of $DV_i$ s and generate various pattern classifiers.