• Title, Summary, Keyword: pattern recognition

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Syntatic Pattern recognition of the ECG (심전도 신호의 신택틱 패턴인식)

  • Nam, Seung-Woo;Lee, Byung-Cha;Sin, Kun-Su;Lee, Jae-Jun;Lee, Myung-Hoo
    • Proceedings of the KOSOMBE Conference
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    • v.1991 no.11
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    • pp.129-132
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    • 1991
  • This paper describes the ECG pattern recognition using the syntatic pattern recognition algorithm. The algorithm uses the BNF rule wi th the semantic evaluation which has the structural Information of the ECG. This algorithm is constructed with (1) removing the baseline drift by the Cubic spline function and exract the significant point by the line-approximation algorithm, (2) syntatic peak recognition algorithm with the extracted significant point, (3) produce the token which is used pattern recognition, (4) pattern recognition of the ECG by the syntatic pattern recognition algorithm, (5) extract the parameter with the pattern recognized ECG signal.

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A Study of ECG Pattern Classification of Using Syntactic Pattern Recognition (신택틱 패턴 인식 알고리즘에 의한 심전도 신호의 패턴 분류에 관한 연구)

  • 남승우;이명호
    • Journal of Biomedical Engineering Research
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    • v.12 no.4
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    • pp.267-276
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    • 1991
  • This paper describes syntactic pattern recognition algorithm for pattern recognition and diagnostic parameter extraction of ECG signal. ECG signal which is represented linguistic string is evaluated by pattern grammar and its interpreter-LALR(1) parser for pattern recognition. The proposed pattern grammar performs syntactic analysis and semantic evaluation simultaneously. The performance of proposed algorithm has been evaluated using CSE database.

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Smart pattern recognition of structural systems

  • Hassan, Maguid H.M.
    • Smart Structures and Systems
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    • v.6 no.1
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    • pp.39-56
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    • 2010
  • Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.

A Study of the Pattern Kernels for a Lip Print Recognition

  • Paik, Kyoung-Seok;Chung, Chin-Hyun
    • 제어로봇시스템학회:학술대회논문집
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    • pp.64-69
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    • 1998
  • This paper presents a lip print recognition by the pattern kernels for a personal identification. A lip print recognition is developed less than the other physical attributes of a fingerprint, a voice pattern, a retinal blood/vessel pattern, or a facial recognition. A new method is proposed to recognize a lip print bi the pattern kernels. The pattern kernels are a function consisted of some local lip print pattern masks. This function converts the information on a lip print into the digital data. The recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. The results show that the proposed algorithm by the multi-resolution architecture can be efficiently realized.

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A Study of a Lip Print Recognition by the Pattern Kernels (Pattern kernels에 의한 Lip Print인식 연구)

  • Paik, Kyoung-Seok;Chung, Chin-Hyun
    • Proceedings of the KIEE Conference
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    • pp.2249-2251
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    • 1998
  • This paper presents a lip print recognition by the pattern kernels for a personal identification. A lip print recognition is developed less than the other physical attribute that is a fingerprint, a voice pattern, a retinal blood-vessel pattern, or a facial recognition. A new method by the pattern kernels is pro for a lip print recognition. The pattern kerne function consisted of some local lip print p masks. This function identifies the lip print known person or an unknown person. The results show that the proposed algorithm the pattern kernels can the efficiently realized.

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The Influence of Wife's Home Management Behavior Pattern and Husband's Perception about It on Family Life Satisfaction (주부의 가정관리 행동유형과 남편의 인지가 가정생활만족에 미치는 영향)

  • 김경숙
    • Journal of the Korean Home Economics Association
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    • v.36 no.1
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    • pp.99-116
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    • 1998
  • The purposes of this study were to find the influence of wife's home management behavior pattern and husband's perception about it on family life satisfaction, and to find out variables which influence them. For theses reviewing literature and empirical research were conducted. The major results were as follows; 1) The couple's psychological variables (ie, degree of life level recognition, of resourcefulness recognition and of communication) were relatively high. The wife's home management behavior pattern was relatively morphogenesis and the husband's perception about wive's it was relatively morphogenesis. And the couple's degree of family life satisfaction were relatively high. 2) Influential variables on wife's home management behavior pattern were level of education, degree of resourcefulness recognition and of communication. And influential variables on husband's perception about vive's it was degree of communication. 3) Influential variables on wive's the degree of family life satisfaction were degree of life level recognition, of resourcefulness recognition and of communication. And influential variables on husband's it were level of education, job, degree of life level recognition, of resourcefulness recognition and of communication. 4) The wife's home management behavior pattern and husband's perception about wive's it were to predict the couple's degree of family life satisfaction. 5) In cause-effect pathway mode. level of education·job·degree of life level recognition·of resourcefulness recognition and of communication showed direct and indirect effect on family life satisfaction through wife's home management behavior pattern or husband's perception about wive's it.

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A Pattern Recognition Based on Co-occurrence among Median Local Binary Patterns (중간값 국소이진패턴 사이의 동시발생 빈도 기반 패턴인식)

  • Cho, Yong-Hyun
    • Journal of Korean Institute of Intelligent Systems
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    • v.26 no.4
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    • pp.316-320
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    • 2016
  • In this paper, we presents a pattern recognition by considering the spatial co-occurrence among micro-patterns of texture images. The micro-patterns of texture image have been extracted by local binary pattern based on median(MLBP) of block image, and the recognition process is based on co-occurrence among MLBPs. The MLBP is applied not only to consider the local character but also analyze the pattern in order to be robust noise, and spatial co-occurrence is also applied to improve the recognition performance by considering the global space of image. The proposed method has been applied to recognized 17 RGB images of 120*120 pixels from Mayang texture image based on Euclidean distance. The experimental results show that the proposed method has a texture recognition performance.

Efficient two-step pattern matching method for off-line recognition of handwritten Hangul (필기체 한글의 오프라인 인식을 위한 효과적인 두 단계 패턴 정합 방법)

  • 박정선;이성환
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.4
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    • pp.1-8
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    • 1994
  • In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

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A Study on the Application of Digital Signal Processing for Pattern Recognition of Microdefects (미소결함의 형상인식을 위한 디지털 신호처리 적용에 관한 연구)

  • 홍석주
    • Journal of The Korean Society of Manufacturing Technology Engineers
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    • v.9 no.1
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    • pp.119-127
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    • 2000
  • In this study the classified researches the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing feature extraction feature selection and classifi-er selection is teated by bulk,. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function the empirical Bayesian classifier. Also the pattern recognition technology is applied to classifica-tion problem of natural flaw(i.e multiple classification problem-crack lack of penetration lack of fusion porosity and slag inclusion the planar and volumetric flaw classification problem), According to this result it is possible to acquire the recognition rate of 83% above even through it is different a little according to domain extracting the feature and the classifier.

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Training-Free sEMG Pattern Recognition Algorithm: A Case Study of A Patient with Partial-Hand Amputation (무학습 근전도 패턴 인식 알고리즘: 부분 수부 절단 환자 사례 연구)

  • Park, Seongsik;Lee, Hyun-Joo;Chung, Wan Kyun;Kim, Keehoon
    • The Journal of Korea Robotics Society
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    • v.14 no.3
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    • pp.211-220
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    • 2019
  • Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.