• 한국공작기계학회논문집
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• 제16권6호
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• pp.62-70
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• 2007

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.237-244
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• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.150-154
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

• 대한전기학회논문지:전력기술부문A
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• 제48권10호
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• pp.1335-1343
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• 1999

This paper presents concrete pattern recognition method to identify the strength of concrete by evidence accumulation with multiple parameters based on artificial intelligence techniques. At first, zero-crossing(ZCR), mean frequency(MEANF), median frequency(MEDF) and autoregressive model coefficient(ARC) are extracted as feature parameters from ultrasonic signal of concrete. Pattern recognition is carried out through the evidence accumulation procedure using distance measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for concrete pattern recognition.

• 비파괴검사학회지
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• 제15권4호
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• pp.531-539
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• 1996

본 연구에서는 초음파 신호형상인식법을 이용하여 용접부의 인공 결함을 분류하기 위한 연구를 실시하였다. 이를 위해 신호처리 및 특징 변수를 추출할 때에 많은 사용자 정의 변수를 가지는 신호 형상 인식 패키지를 개발하였으며 디지탈 신호처리, 특징 변수 추출, 특징 변수의 선택, 분류기 선정 등의 과정을 일괄적으로 처리하였다. 특히, 선형 분류기, 경험적 Bayesian 분류기 등의 통계적 분류기와 신경회로망 분류기를 함께 사용하여 비교, 검토하였다. 이에 관한 적용 연구로 노치와 구멍으로 이루어진 인공 결함을 분류하였다. 그 결과 인공결함 분류에서 높은 인식률을 얻었으며, 특히 적절히 학습 시켰을 경우 신경회로망 분류기가 통계적 분류기에 비하여 인식률 면에서 유리하였다.

• Journal of Electrical Engineering and information Science
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• 제2권4호
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• pp.59-65
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• 1997

In this paper, a general fuzzy syntactic method for recognition of flaw patterns and for the measurement of flaw characteristic parameters for a non-destructive inspections signal, called eddy-current, is presented. Solutions are given to the subtasks of primitive pattern selection, signal to symbol transformation, pattern grammar formulation, and event-synchronous flaw pattern extraction based on the grammars. Fuzzy attribute grammars are used as the model for the pattern grammar because of their descriptive power in the face of uncertain constraints caused by nose or distortion in the signal waveform, due to their ability to handle syntactic as well as semantic information. This approach has been implemented and the performance of eh resultant system has been evaluated using a library of law patterns obtained from steam generator tubes in nuclear power plants by an eddy current-based non-destructive inspection method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.119-124
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• 1999

This paper presents new approach to technology pattern recognition of camera back cover and test of soldering. In real-time implementing of pattern recognition camera back cover and test of soldering, the MVB-03 vision board has been used. Image can be captured from standard CCD monochrome camera in resolutions up to 640$\times$480 pixels. Various options re available for color cameras, a synchronous camera reset, and linescan cameras. Image processing os performed using Texas Instruments TMS320C31 digital signal processors. Image display is via a standard composite video monitor and supports non-destructive color overlay. System processing is possible using c30 machine code. Application software can be written in Borland C++ or Visual C++

• 한국소음진동공학회논문집
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• 제18권12호
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• pp.1256-1261
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• 2008

This paper proposes a way to measure the displacement of a multi-point by using a pattern recognition from video signal. Generally in measuring displacement, gab sensor, which is a displacement sensor, is used. However, it is difficult to measure displacement by using a common sensor in places where it is unsuitable to attach a sensor, such as high-temperature areas or radioactive places. In this kind of places, non-contact methods should be used to measure displacement and in this study, images of CCD camera were used. When multi-point is measure by using a pattern recognition, it is possible to measure displacement with a non-contact method. It is simple to install and multi-point displacement measuring device so that it is advantageous to solve problems of spatial constraints.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1991년도 추계학술대회
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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.

• 대한기계학회논문집A
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• 제27권11호
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.