• 제목/요약/키워드: signal pattern recognition

검색결과 281건 처리시간 0.024초

신경회로망을 이용한 근전도 신호의 특성분석 및 패턴 분류 (Pattern Recognition of EMG Signal using Artificial Neural Network)

  • 이석주;이성환;조영조
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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    • pp.769-771
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    • 2000
  • In this paper, pattern recognition scheme for EMG signal using artificial neural network is proposed. For manipulating ability, the movements of human arm are classified into several categories EMG signals of appropriate muscles are collected during arm movement. Patterns of EMG signals of each movement are recognized as follows: 1) The features of each EMG signal are extracted. 2) With these features, the neural network is trained by using feedforward error back-propagation (FFEBP) algorithm. The results show that the arm movements can be classified with EMG signals at high accuracy.

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디지털 방식 무선 화재알림설비의 신호 패턴 인식기법 적용 (Application of Signal Pattern Recognition Technique of Digital Wireless Fire Alarm System)

  • 박승환;김두현;김성철
    • 한국안전학회지
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    • 제37권5호
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    • pp.14-21
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    • 2022
  • The purpose of this study was to apply the signal pattern recognition technique to the digital wireless fire-alarm system and to reduce unwanted fire alarms. In this study, the fire alarms of the K Institute, which operates the largest digital wireless fire-alarm system in Korea, were classified into normal operations and unwanted fire alarms, and these were analyzed and compared with actual fire signals. In addition, by designing a non-fire signal filter and applying it to the K Institute, we confirmed that the monthly unwanted fire alarm rate of all 5,713 detectors decreased sharply. In particular, the unwanted fire alarm rate for flame decreased from 1.09% to 0.11% and the unwanted fire alarm rate for smoke decreased from 0.65% to 0.035%.

감성 인식을 위한 강화학습 기반 상호작용에 의한 특징선택 방법 개발 (Reinforcement Learning Method Based Interactive Feature Selection(IFS) Method for Emotion Recognition)

  • 박창현;심귀보
    • 제어로봇시스템학회논문지
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    • 제12권7호
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    • pp.666-670
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    • 2006
  • This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

고차 반사계수 특성을 이용한 화자인식의 성능 향상에 관한 연구 (On a Study of the Improvement of Speaker Recognition with Characteristics of High Order Reflection Coefficients)

  • 이윤주;오세영;함명규;배명진
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1999년도 하계종합학술대회 논문집
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    • pp.667-670
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    • 1999
  • As the number of reference patterns increase in the text dependant speaker recognition, the recognition performance of the system degrades. So, if reference patterns were decreased the high recognition rate can be obtained. It’s because the speaker recognition can obtain the high discrimination. In this paper, to decrease the number of reference patterns, we choose candidate reference patterns to perform pattern matching with test pattern by high order component of the reflection coefficients of the uttered speech signal Consequently the total recognition rate of the proposed method is about 2% higher than that of the conventional method.

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화자인식을 위한 퍼지-상관차원과 퍼지-리아프노프차원의 평가 (The Evaluation of the Fuzzy-Chaos Dimension and the Fuzzy-Lyapunov Ddimension)

  • 유병욱;박현숙;김창석
    • 음성과학
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    • 제7권3호
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    • pp.167-183
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    • 2000
  • In this paper, we propose two kinds of chaos dimensions, the fuzzy correlation and fuzzy Lyapunov dimensions, for speaker recognition. The proposal is based on the point that chaos enables us to analyze the non-linear information contained in individual's speech signal and to obtain superior discrimination capability. We confirm that the proposed fuzzy chaos dimensions play an important role in enhancing speaker recognition ratio, by absorbing the variations of the reference and test pattern attractors. In order to evaluate the proposed fuzzy chaos dimensions, we suggest speaker recognition using the proposed dimensions. In other words, we investigate the validity of the speaker recognition parameters, by estimating the recognition error according to the discrimination error of an individual speaker from the reference pattern.

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오스테나이트계 스테인리스강 304 용접부의 초음파 형상 인식 평가를 위한 카오스 시뮬레이터의 구축 (Construction fo chaos simulator for ultrasonic pattern recognition evaluation of weld zone in austenitic stainless steel 304)

  • 이원;윤인식;장영권
    • Journal of Welding and Joining
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    • 제16권5호
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    • pp.108-118
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    • 1998
  • This study proposes th analysis and evaluation method of time series ultrasonic signal using the chaos feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaos time series signal analyze quantitatively weld defects. For this purpose, analysis objective in this study is fractal dimension and Lyapunov exponent. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaosity resulting from distance shifts such as 0.5 and 1.0 skip distance. Such differences in chaosity enables the evaluation of unique features of defects in the weld zone. In quantitative chaos feature extraction, feature values of 4.511 and 0.091 in the case of side hole and 4.539 and 0.115 in the case of vertical hole were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaos feature extraction in this study can enhances ultrasonic pattern recognition results from defect signals of weld zone such as side hole and vertical hole.

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딕셔너리 러닝을 이용한 음파 신호 분류기 설계 (Acoustic Signal Classifier Design using Dictionary Learning)

  • 박성민;사성진;오광명;이희승
    • 자동차안전학회지
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    • 제8권1호
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    • pp.19-25
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    • 2016
  • As new car technology is developing, temporal interaction is needed in automotive. Rhythmic pattern is one of the practical examples of temporal interaction in vehicle. To recognize rhythmic pattern and its input medium, dictionary learning is applicable algorithm. In this paper, performance and memory requirement of the learning algorithm is tested and is sufficiently good for use this acoustic sound.

3차원 공간상의 수신호 인식 시스템에 대한 연구 (A Study on Hand-signal Recognition System in 37dimensional Space)

  • 장효영;김대진;김정배;변증남
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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    • pp.215-218
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    • 2002
  • Gesture recognitions needed for various applications and is now gaining in importance as one method of enabling natural and intuitive human machine communication. In this paper, we propose a real time hand-signal recognition system in 3-dimensional space performs robust, real-time tracking under varying illumination. As compared with the existing method using classical pattern matching, this system is efficient with respect to speed and also presents more systematic way of defining hand-signals and developing a hand-signal recognition system. In order to verify the proposed method, we developed a virtual driving system operated by hand-signals.

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회전기계의 이상진단을 위한 진동신호 분류시스템에 관한 연구 (Classification System using Vibration Signal for Diagnosing Rotating Machinery)

  • 임동수;안경룡;양보석
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2000년도 춘계학술대회논문집
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    • pp.1133-1138
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    • 2000
  • This paper describes a signal recognition method for diagnosing the rotating machinery using wavelet-aided Self-Organizing Feature Map(SOFM). The SOFM specialized from neural network is a new and effective algorithm for interpreting large and complex data sets. It converts high-dimensional data items into simple order relationships with low dimension. Additionally the Learning Vector Quantization(LVQ) is used for reducing the error from SOFM. Multi-resolution and wavelet transform are used to extract salient features from the primary vibration signals. Since it decomposes the raw timebase signal into two respective parts in the time space and frequency domain, it does not lose either information unlike Fourier transform. This paper is focused on the development of advanced signal classifier in order to automatize vibration signal pattern recognition. This method is verified by the experiment and several abnormal vibrations such as unbalance and rubbing are classified with high flexibility and reliability by the proposed methods.

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