• 제목/요약/키워드: self-recognition algorithm

검색결과 116건 처리시간 0.025초

Modeling of Positive Selection for the Development of a Computer Immune System and a Self-Recognition Algorithm

  • Sim, Kwee-Bo;Lee, Dong-Wook
    • International Journal of Control, Automation, and Systems
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    • 제1권4호
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    • pp.453-458
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    • 2003
  • The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

Dynamic gesture recognition using a model-based temporal self-similarity and its application to taebo gesture recognition

  • Lee, Kyoung-Mi;Won, Hey-Min
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제7권11호
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    • pp.2824-2838
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    • 2013
  • There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

인공면역계의 자기-인식 알고리즘 (Self-Recognition Algorithm of Artificial Immune System)

  • 선상준;이동욱;심귀보;성원기
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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    • pp.185-188
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    • 2001
  • According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. To block hacking that is intrusion of a person's computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive selection and negative selection of self-recognition process that is ability of T-cytotoxic cell that plays an important part in biological immune system. So we embody a self-nonself distinction algorithm in computer. To prove the efficacy of self-recognition algorithm, we use simulations by a cell change and a string change of self file.

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Scanning Acoustic Tomograph 방식을 이용한 지능형 반도체 평가 알고리즘 (The Intelligence Algorithm of Semiconductor Package Evaluation by using Scanning Acoustic Tomograph)

  • 김재열;김창현;송경석;양동조;장종훈
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2005년도 춘계학술대회 논문집
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    • pp.91-96
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    • 2005
  • In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

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한국어 연결 숫자음 인식을 일한 최대 사후 Eigenvoice에 근거한 자기적응 기법 (Self-Adaptation Algorithm Based on Maximum A Posteriori Eigenvoice for Korean Connected Digit Recognition)

  • 김동국;전형배
    • 한국음향학회지
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    • 제23권8호
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    • pp.590-596
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    • 2004
  • 본 논문에서는 한국어 연결 숫자음 인식을 위한 최대 사후 eigenvoice을 사용한 자기적응 기법을 제안한다. 제안된 최대 사후 eigenvoice 기법은 eigenvoice 계수에 대한 확률 밀도 함수를 가정함으로 구성된다. 제안된 알고리즘은 기존 eigenvoice 추정 과정에 선 분포 모델을 포함하는 일반적인 해를 제공하는 구조를 갖는다. 인식할 한 문장만을 사용하는 자기 적응 시스템을 위해 매우 강인한 특성을 갖는 최대 사후 eigenvoice 적응 기법을 사용하였다. 한국어 연결 숫자음에 대한 일련의 자기 적응 실험결과 제안된 알고리즘의 성능은 매우 적은 량의 적응 데이터에 대해 기존 eigenvoice 알고리즘에 비해 우수한 성능을 나타냈었다.

DYNAMICALLY LOCALIZED SELF-ORGANIZING MAP MODEL FOR SPEECH RECOGNITION

  • KyungMin NA
    • 한국음향학회:학술대회논문집
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    • 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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    • pp.1052-1057
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    • 1994
  • Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.

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인공면역계의 자기-인식 알고리즘 (Self-Recognition Algorithm of Artificial Immune System)

  • 심귀보;선상준
    • 한국지능시스템학회논문지
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    • 제11권9호
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    • pp.801-806
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    • 2001
  • 최근 컴퓨터의 사용이 보편화되면서 악의적 사용자에 의해 발생하는 컴퓨터 바이러스와 해킹에 의한 피해가 급속히 증가하고 있다. 남의 컴퓨터에 침입하는 해킹이나 데이터를 파괴하는 컴퓨터 바이러스에 의한 피해를 막기 위해 최근에 생명체의 면역시스템의 특징을 이용해 인공면역계를 구성해 시스템 침입탐지와 바이러스 탐지 및 치료에 대한 연구가 활발히 진행 중에 있다. 생체 면역계는 외부에서 침입해 세포나 장기에 피해를 주는 물질인 항원을 스스로 자기세포와 구분해 인식, 제거하는 기능이 있다. 이러한 면역계의 특징인 항원을 인식하는 기능은 자기세포의 확실한 인식을 가지고 있는 상태에서 다른 물질을 구분하는 자기/비자기(self/non-self) 인식방법으로 볼 수 있다. 본 논문에서는 생체 면역계에서 세포독성 T세포의 생성과정의 하나인 Positive Selection을 모델링하여 침입에 의한 데이터 변경과 바이러스에 의한 데이터 감염 등을 탐지할 때 가장 중요한 요소인 자기-인식 알고리즘을 구현하였다. 제안한 알고리즘은 큰 파일에서의 Detection을 구성하기 용이한 점을 가지며 극소변경과 블록변경에 대한 자기인식률을 통해 알고리즘을 유효성을 검증한다.

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Improvement of Three Mixture Fragrance Recognition using Fuzzy Similarity based Self-Organized Network Inspired by Immune Algorithm

  • Widyanto, M.R.;Kusumoputro, B.;Nobuhara, H.;Kawamoto, K.;Yoshida, S.;Hirota, K.
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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    • pp.419-422
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    • 2003
  • To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy Similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed. Minimum, average, and maximum values of fragrance data acquisitions are used to form triangular fuzzy numbers. Then the fuzzy similarity treasure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of input vectors and the connection strengths of hidden units. In experiments, performances of the proposed method is compared with the conventional Self-Organized Network inspired by Immune Algorithm (SONIA), and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9%. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-25%.

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자기조직화 지도를 이용한 반도체 패키지 내부결함의 패턴분류 알고리즘 개발 (The Development of Pattern Classification for Inner Defects in Semiconductor Packages by Self-Organizing Map)

  • 김재열;윤성운;김훈조;김창현;양동조;송경석
    • 한국공작기계학회논문집
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    • 제12권2호
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    • pp.65-70
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    • 2003
  • In this study, researchers developed the estimative algorithm for artificial defect in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages : Crack, Delamination and Normal. According to the results, we were confirmed that estimative algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

윤곽선 추적과 개선된 ART1 기반 자가 생성 지도 학습 알고리즘을 이용한 운송 컨테이너 영상의 식별자 인식 (The Identifier Recognition from Shipping Container Image by Using Contour Tracking and Self-Generation Supervised Learning Algorithm Based on Enhanced ART1)

  • 김광백
    • 지능정보연구
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    • 제9권3호
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    • pp.65-79
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    • 2003
  • 운송 컨테이너의 식별자를 추출하고 인식하는 것은 컨테이너 식별자들의 크기나 위치가 정형화되어 있지 않고 외부의 잡음으로 인하여 식별자의 형태가 훼손되어 있기 때문에 어렵다. 본 논문에서는 이러한 특성을 고려하여 컨테이너 영상에 대해 Canny 마스크를 이용하여 에지를 검출하고, 검출된 에지 정보를 이용하여 수직블록과 수평블록을 추출하여 컨테이너의 식별자 영역을 추출한다. 추출된 컨테이너의 식별자 영역에서 윤곽선 추적 알고리즘을 이용하여 개별 식별자를 추출하며, 그들의 인식을 위해서는 개선된 ARTl과 지도 학습 방법을 결합한 개선된 성능의 자가 생성 지도 학습 알고리즘을 제안하여 적용한다. 제안된 방법의 성능을 확인하기 위하여 운송 컨테이너 영상들을 대상으로 실험 결과, 윤곽선 추적 알고리즘을 이용한 식별자의 추출 방법이 히스토그램을 이용한 식별자의 추출 방법보다 추출률이 개선되었고 인식 결과에서도 개선된 ART1 기반 자가 생성 지도 학습 방법이 기존의 ART1 기반 자가 생성 지도 학습 방법보다 인식률이 향상되었다.

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