• Title/Summary/Keyword: 9개의 패턴

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A Study on Creating Reference Pattern for Recognition of Korean Isolated Word (한국어 단독음 인식을 위한 표준패턴 설정에 관한 연구)

  • Kim, Gye-Guk;Go, Deok-Yeong;Lee, Jong-Ak
    • The Journal of the Acoustical Society of Korea
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    • v.6 no.1
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    • pp.23-28
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    • 1987
  • This paper discusses a reference pattern creation for a speaker-independent Korean isolated word by using the clustering. Tn this paper we permitted to top 3 clusters and created reference pattern by Minimax Criterion. The features parameter used the LPC Coefficients and Autocorrelation and simple Itakura distance measure was used to measure similarity between patterns. With word reference patterns obtained as described above the recognition rate was within one choice only $55.9\%$, two choice only $76.9\%$, three choice only $89.5\%$.

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A Study Context Aware Middle for Decision of Human Behavior Pattern (인간 행동패턴 결정을 위한 상황인식 미들웨어에 대한 연구)

  • 최순용;최종화;신동일;신동규
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.10b
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    • pp.538-540
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    • 2004
  • 이 논문에서 제안된 인간행동패턴 결정을 위한 상황인식 미들웨어는 Intelligent Home환경에서 인간과 Home환경과의 지능적인 Agent로써의 역할을 담당한다. 우리는 제시된 논문에서 인간행동패턴 결정을 위한 상황인식 미들웨어의 아키텍처를 제안하고 상황인식 미들웨어 내에서 동작하는 인간행동패턴 학습 및 결정 프로세서에 대한 구조와 구현내용에 대한 설명을 한다. 인간행동패턴을 결정하기 위한 기본 컨텍스트들을 환경 컨텍스트와 생체 컨텍스트로 크게 두 그룹으로 분리하였고 각 그룹은 세 개의 컨텍스트를 포함하고 있다. 환경과 생체로 나뉘어진 총 6개의 컨텍스트들을 정의하고 그 구성에 대하여 설명한다. 또한 컨텍스트는 9단계로 정규화 되어 상황인식 미들웨어에서의 다음 단계인 인간행동패턴 학습 및 결정 프로세서로 정규화 된 값을 전달된다. 인간행동패턴 학습 및 결정 프로세서에서는 패턴인식에 대한 세부사항을 설명한다.

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Order preserving matching with k mismatches (k개의 오차를 허용하는 순위 패턴 매칭)

  • Lee, Inbok
    • Smart Media Journal
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    • v.9 no.2
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    • pp.33-38
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    • 2020
  • Order preserving matching refers to the problem of reporting substrings of a given text where there exists order isomorphism with the pattern. In this paper, we propose a new algorithm based on filtering and evaluation. The proposed algorithm is simple and easy to implement, and runs in linear time on average. Experimental results show that it works efficiently with real world data.

Pattern Analysis of Personalized ECG Signal by Q, R, S Peak Variability (Q, R, S 피크 변화에 따른 개인별 ECG 신호의 패턴 분석)

  • Cho, Ik-Sung;Kwon, Hyeog-Soong;Kim, Joo-Man;Kim, Seon-Jong;Kim, Byoung-Chul
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.1
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    • pp.192-200
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    • 2015
  • Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.

Pattern Classification using the Block-based Neural Network (블록기반 신경망을 이용한 패턴분류)

  • 공성근
    • Journal of the Korean Institute of Intelligent Systems
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    • v.9 no.4
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    • pp.396-403
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    • 1999
  • 본 논문에서는 새로운 블록기반 신경망을 제안하고 블록기반 신경망의 패턴류 성능을 확인하였다. 블록기반 신경망은 4개의 가변 입출력을 가지는 블록을 기본 구성요소로하고 있으며 블록들의 2차원배열 형태로 이루어진다. 블록기반 신경망은 재구성가능 하드웨어에 의하여 구현이 용이하고 구조 및 가중치의 최적화에 진화 알고리즘을 적용시킬수 있는 새로운 신경망 모델이다. 블록 기반 신경망의 구조와 가중치를 재고성 가능 하드웨어(FPGA)의 비트열에 대응시키고 유전자 알고리즘에 의하여 전역최적화를 하여 구조와 가중치를 최적화한다. 유전 알고리즘에 의하여 설계된 블록기반 신경망을 비선형 결정평면을 가지는 여러 학습패턴에 적용하여 패턴분류 성능을 확인하였다.

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Differentiation of Elytra Color Patterns in Multicolored Asian Ladybird Beetle, Harmonia axyridis (Coleoptera; Coccinellidae), using AFLP analyses (Amplified Fragment Length Polymorphism (AFLP)을 이용한 무당벌레(Harmonia axyridis : Coccinellidae)의 초시색상패턴의 변이 분석)

  • Park, Cho Rong;Kim, Jeong Hee;Yu, Yong Man;Youn, Young Nam
    • Korean journal of applied entomology
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    • v.55 no.3
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    • pp.245-256
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    • 2016
  • Elytra of Harmonia axyridis exhibit varied color patterns. In the present study, we deciphered the genetic basis for intraspecific diversity of elytra color patterns in H. axyridis, using amplified fragment length polymorphism (AFLP). Twenty-eight AFLP reactions were performed to generate a total of 2,741 bands. Of these, 20 bands were polymorphic for each color pattern. The polymorphic bands showed differences of genetic character among different color patterns of H. axyridis. Among them, ten candidate AFLP markers were color-linked. S1, S2, and S20 markers were detected in Succinea 1 and 2 variants of H. axyridis, whereas S3 and S5 were specifically detected in the Conspicua variant. S15, S18, and S19 were specific to the Succinea 2 variant. Polymerase chain reaction (PCR) products of these ten AFLP markers were sequenced. BLAST analysis of these sequences against the GenBank database revealed their homology to DNA fragments of unknown function. Based on the color-linked AFLP markers, sequence characterized amplified region (SCAR) markers were designed for PCR amplification of genomic DNA. Of the ten AFLP markers, five were successfully converted into SCAR markers, which could discriminate elytra color polymorphism in H. axyridis.

The Cucumber Cognizance for Back Propagation of Nerual Network (신경회로망의 오류역전파 알고리즘을 이용한 오이 인식)

  • Min, Byeong-Ro;Lee, Dae-Weon
    • Journal of Bio-Environment Control
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    • v.20 no.4
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    • pp.277-282
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    • 2011
  • We carried out shape recognition. We found out cucumber's feature shape by means of neural network and back propagation algorithm. We developed an algorithm which finds object position and shape in real image and we gained following conclusion as a result. It was processed for feature shape extraction of cucumber to detect automatic. The output pattern rates of the miss-detected objects was 0.1~4.2% in the output pattern which was recognized as cucumber. We were gained output pattern according to image resolution $445{\times}363$, $501{\times}391$, $450{\times}271$, $297{\times}421$. It was appeared that no change was detected. When learning pattern was increased to 25, miss-detection ratio was 16.02%, and when learning pattern had 2 pattern, it didn't detect 8 cucumber in 40 images.

Performance Analysis of TCAM-based Jumping Window Algorithm for Snort 2.9.0 (Snort 2.9.0 환경을 위한 TCAM 기반 점핑 윈도우 알고리즘의 성능 분석)

  • Lee, Sung-Yun;Ryu, Ki-Yeol
    • Journal of Internet Computing and Services
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    • v.13 no.2
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    • pp.41-49
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    • 2012
  • Wireless network support and extended mobile network environment with exponential growth of smart phone users allow us to utilize the network anytime or anywhere. Malicious attacks such as distributed DOS, internet worm, e-mail virus and so on through high-speed networks increase and the number of patterns is dramatically increasing accordingly by increasing network traffic due to this internet technology development. To detect the patterns in intrusion detection systems, an existing research proposed an efficient algorithm called the jumping window algorithm and analyzed approximately 2,000 patterns in Snort 2.1.0, the most famous intrusion detection system. using the algorithm. However, it is inappropriate from the number of TCAM lookups and TCAM memory efficiency to use the result proposed in the research in current environment (Snort 2.9.0) that has longer patterns and a lot of patterns because the jumping window algorithm is affected by the number of patterns and pattern length. In this paper, we simulate the number of TCAM lookups and the required TCAM size in the jumping window with approximately 8,100 patterns from Snort-2.9.0 rules, and then analyse the simulation result. While Snort 2.1.0 requires 16-byte window and 9Mb TCAM size to show the most effective performance as proposed in the previous research, in this paper we suggest 16-byte window and 4 18Mb-TCAMs which are cascaded in Snort 2.9.0 environment.

A study on creating Reference Pattern of speech by using the cluster (집단화를 이용한 음성의 표준 패턴설정에 관한 연구)

  • 김계국
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1985.10a
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    • pp.59-63
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    • 1985
  • 불특정 화자의 음성인식을 위해 150 숫자음에 대하여 10개의 표준패턴을 설정하는데 목적을 두고 기술했다. 남성화자 3인이 각숫자음(0-9)를 5번씩 반복 발음한 150음을 지단화하여 숫자음의 표준패턴을 설정하였다. 특징 파라미터는 포르만트 주파수를 이용하였고 유크리드 거리 측정법을 유사도 비교에 사용하였다. 실험결과 85.3%의 인식률을 얻었다.

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Visiting Pattern is Kyeryongsan National Park (계룡산국립공원의 탐방패턴)

  • 이준우;권태호;최송현
    • Korean Journal of Environment and Ecology
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    • v.14 no.4
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    • pp.341-346
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    • 2001
  • 계룡산국립공원의 탐방객 수는 매년 조금씩 감소하는 추세를 보이고 있으며, 천제 탐방객 중 56.4%가 접근성이 용이한 동학사지구로 탐방하고 있었다. 이용이 가장 많은 기간 중 일요일에 4개의 매표소를 기준으로 정상을 향한 등반비율을 각각 갑사매표소(72.2%), 천장(47.7%), 동학사(30.6%), 신원사(6.9%)의 순으로 조사되었으며, 나머지는 등반하지 않고 매표소 근처만을 탐방한 후에 되돌아가는 것으로 조사되었다. 계룡산국립공원의 탐방패턴은 지역별, 요일별로 차이를 보이는 것으로 조사되었다.

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