• Title/Summary/Keyword: Pattern Matching Algorithm

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The Study on Lossy and Lossless Compression of Binary Hangul Textual Images by Pattern Matching (패턴매칭에 의한 이진 한글문서의 유.무손실 압축에 관한 연구)

  • 김영태;고형화
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.4
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    • pp.726-736
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    • 1997
  • The textual image compression by pattern matching is a coding scheme that exploits the correlations between patterns. When we compress the Hangul (Korean character) text by patern matching, the collerations between patterns may decrease due to randoem contacts between phonemes. Therefore in this paper we separate connected phonemes to exploit effectively the corrlation between patterns by inducting the amtch. In the process of sequation, we decide whether the patterns have vowel component or not, and then vowels connected with consonant ae separated. When we compare the proposed algorithm with the existing algorith, the compression ratio is increased by 1.3%-3.0% than PMS[5] in lossy mode, by 3.4%-9.1% in lossless mode than that of SPM[7] which is submitted to standard committe for second generation binary compression algorithm.

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A Novel Cryptosystem Based on Steganography and Automata Technique for Searchable Encryption

  • Truong, Nguyen Huy
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.5
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    • pp.2258-2274
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    • 2020
  • In this paper we first propose a new cryptosystem based on our data hiding scheme (2,9,8) introduced in 2019 with high security, where encrypting and hiding are done at once, the ciphertext does not depend on the input image size as existing hybrid techniques of cryptography and steganography. We then exploit our automata approach presented in 2019 to design two algorithms for exact and approximate pattern matching on secret data encrypted by our cryptosystem. Theoretical analyses remark that these algorithms both have O(n) time complexity in the worst case, where for the approximate algorithm, we assume that it uses ⌈(1-ε)m)⌉ processors, where ε, m and n are the error of our string similarity measure and lengths of the pattern and secret data, respectively. In searchable encryption, our cryptosystem is used by users and our pattern matching algorithms are performed by cloud providers.

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

  • Menalsh Laishram;Satyendra Nath Mandal;Avijit Haldar;Shubhajyoti Das;Santanu Bera;Rajarshi Samanta
    • Animal Bioscience
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    • v.36 no.6
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    • pp.980-989
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    • 2023
  • Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

DFT integration for Face Detection (DFT를 이용한 Face Detection)

  • Han, Seok-Min;Choi, Jin-Young
    • Proceedings of the KIEE Conference
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    • 2006.04a
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    • pp.117-119
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    • 2006
  • In this work, we suggest another method to localize DFT in spatial domain. This enables DFT algorithm to be used for local pattern matching. Once calculated, it costs same load to calculate localized DFT regardless of the size or the position of local region In spatial domain. We applied this method to face detection problem and got the results which prove the utility of our method.

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Point Pattern Matching Based Global Localization using Ceiling Vision (천장 조명을 이용한 점 패턴 매칭 기반의 광역적인 위치 추정)

  • Kang, Min-Tae;Sung, Chang-Hun;Roh, Hyun-Chul;Chung, Myung-Jin
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1934-1935
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    • 2011
  • In order for a service robot to perform several tasks, basically autonomous navigation technique such as localization, mapping, and path planning is required. The localization (estimation robot's pose) is fundamental ability for service robot to navigate autonomously. In this paper, we propose a new system for point pattern matching based visual global localization using spot lightings in ceiling. The proposed algorithm us suitable for system that demands high accuracy and fast update rate such a guide robot in the exhibition. A single camera looking upward direction (called ceiling vision system) is mounted on the head of the mobile robot and image features such as lightings are detected and tracked through the image sequence. For detecting more spot lightings, we choose wide FOV lens, and inevitably there is serious image distortion. But by applying correction calculation only for the position of spot lightings not whole image pixels, we can decrease the processing time. And then using point pattern matching and least square estimation, finally we can get the precise position and orientation of the mobile robot. Experimental results demonstrate the accuracy and update rate of the proposed algorithm in real environments.

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Design and Implementation of High-Speed Pattern Matcher in Network Intrusion Detection System (네트워크 침입 탐지 시스템에서 고속 패턴 매칭기의 설계 및 구현)

  • Yoon, Yeo-Chan;Hwang, Sun-Young
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.11B
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    • pp.1020-1029
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    • 2008
  • This paper proposes an high speed pattern matching algorithm and its implementation. The pattern matcher is used to check patterns from realtime input packet. The proposed algorithm can find exact string, range of string values, and combination of string values from input packet at high speed. Given string and rule set are modelled as a state transition graph which can find overlapped strings simultaneously, and the state transition graph is partitioned according to input implicants to reduce implementation complexity. The pattern matcher scheme uses the transformed state transition graph and input packet as an input. The pattern matcher was modelled and implemented in VHDL language. Experimental results show the proprieties of the proposed approach.

A study on fault diagnosis for chemical processes using hybrid approach of quantitative and qualitative method (정성적, 정량적 기법의 혼합 전략을 통한 화학공정의 이상진단에 관한 연구)

  • 오영석;윤종한;윤인섭
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.714-717
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    • 1996
  • This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.

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A Study on Accuracy Estimation of Service Model by Cross-validation and Pattern Matching

  • Cho, Seongsoo;Shrestha, Bhanu
    • International journal of advanced smart convergence
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    • v.6 no.3
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    • pp.17-21
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    • 2017
  • In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

Comparison of the Dynamic Time Warping Algorithm for Spoken Korean Isolated Digits Recognition (한국어 단독 숫자음 인식을 위한 DTW 알고리즘의 비교)

  • 홍진우;김순협
    • The Journal of the Acoustical Society of Korea
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    • v.3 no.1
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    • pp.25-35
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    • 1984
  • This paper analysis the Dynamic Time Warping algorithms for time normalization of speech pattern and discusses the Dynamic Programming algorithm for spoken Korean isolated digits recognition. In the DP matching, feature vectors of the reference and test pattern are consisted of first three formant frequencies extracted by power spectrum density estimation algorithm of the ARMA model. The major differences in the various DTW algorithms include the global path constrains, the local continuity constraints on the path, and the distance weighting/normalization used to give the overall minimum distance. The performance criterias to evaluate these DP algorithms are memory requirement, speed of implementation, and recognition accuracy.

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Fault diagnosis for chemical processes using weighted symptom model and pattern matching (가중증상모델과 패턴매칭을 이용한 화학공정의 이상진단)

  • Oh, Young-Seok;Mo, Kyung-Ju;Yoon, Jong-Han;Yoon, En-Sup
    • Journal of Institute of Control, Robotics and Systems
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    • v.3 no.5
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    • pp.520-525
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    • 1997
  • This paper presents a fault detection and diagnosis methodology based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. In the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model is used to generate those candidates. The weight is determined from dynamic simulation. Using WSM, the methodology can generate the cause candidates and rank them according to the probability. Second, the fault propagation trends identified from the partial or complete sequence of measurements are compared with the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies, and the results showed satisfactory diagnostic resolution.

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