• 제어로봇시스템학회논문지
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• 제4권6호
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• pp.765-771
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• 1998

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.284-288
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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 is 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. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.347-350
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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 is 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. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 음성과학
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• 제8권3호
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• pp.149-157
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• 2001

Based on the chaos theory, a new method of presentation of speech signal has been presented in this paper. This new method can be used for pattern matching such as speaker recognition. The expressions of attractors are represented very well by the logistic maps that show the chaos phenomena. In the speaker recognition field, a speaker's vocal habit could be a very important matching parameter. The attractor configuration using change value of speech signal can be utilized to analyze the influence of voice undulations at a point on the vocal loudness scale to the next point. The attractors arranged by the method could be used in research fields of speech recognition because the attractors also contain unique information for each speaker.

• 로봇학회논문지
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• 제14권3호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.694-698
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• 1987

In this paper, the primitive and double combined motion classification of the arm is discussed using pattern recognition of EM signal. The EM signals are detected from Ag-Ag/Cl surface electrodes, and IBM PC, calculated the Likelyhood probability and the decision function on the feature space of integral absolute value. Multiclass decision rule is introduced for higher decision rate. On our experimental results from expert simulator, the decision rate of more than 78% can be obtained.

• 한국음향학회지
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• 제19권4호
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• pp.93-100
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• 2000

수중 능동소나에 의해 표적을 분류하는데 있어 표적신호의 특징파라미터는 매우 중요하다. 광대역이고 상관성이 높은 두 개의 펄스가 시간 T의 간격으로 분리되어 있을 때, 스펙트럼에서 리플간의 1/T Hz에 해당하는 TSP, 즉 피치 성분을 가진다. 음향산란 실험에 사용된 축소표적신호 또한 이러한 TSP 특징을 잘 반영하고 있다. 본 논문에서는 각 표적신호의 특징에 해당하는 TSP 정보를 FFT를 이용하여 효과적으로 추출하였다. 네 개의 표적과 각 표적의 자세각에 따라 추출된 TSP 특징파라미터를 패턴인식 기법에 적용하여 표적을 분류하고 각 표적의 특징을 분석하였다.

• 비파괴검사학회지
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• 제13권2호
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• pp.11-19
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• 1993

원자력발전소의 주요 압력용기 용접부에 대한 초음파검사시 결함의 특성과 형태에 대한 정확한 분류는 원자력 발전소의 안전성을 확보하기 위한 결함평가에 중요한 요소이다. 본 연구에서 초음파검사에서 얻어진 결함신호를 digital signal processing 기법으로 처리하여 결함의 특성과 형태를 구분할 수 있는 feature vector를 추출하고 결함의 형태를 형상 인식법을 사용하여 분류 하였다. Training specimen(slit, hole)의 신호와 testing specimen(crack, slag)의 신호를 구분하기 위한 실험에서 사용된 통계적 pattern recognition algorithm은 minimum distance classifier와 maximum likelihood classifier이다. 이러한 형상 classifier를 이용하여 결함의 특성을 정량적으로 분류하여 결함 평가 능력을 향상시켰다.

• 한국음향학회지
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• 제23권3호
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• pp.235-241
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• 2004

본 논문은 화자 및 문장 독립적 감정 인식을 위한 특징 파라메터와 패턴인식 알고리즘에 관하여 연구하였다. 본 논문에서는 기존 감정 인식 방법과의 비교를 위하여 KNN을 이용한 알고리즘을 사용하였고, 화자 및 문장 독립적 감정 인식을 위하여 VQ와 GMM을 이용한 알고리즘을 사용하였다. 그리고 특징으로 사용한 음성 파라메터로 피치, 에너지, MFCC, 그리고 그것들의 1, 2차 미분을 사용하였다. 실험을 통해 피치와 에너지 파라메터를 사용하였을 때보다 MFCC와 그 미분들을 특징 파라메터로 사용하였을 때 더 좋은 감정 인식 성능을 보였으며, KNN과 VQ보다 GMM을 기반으로 한 인식 알고리즘이 화자 및 문장 독립적 감정 인식 시스템에서 보다 적합하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.377-380
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• 2002

In this paper, speaker and context independent emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and to evaluate the performance of the conventional pattern matching algorithms. The vector quantization based emotion recognition system is proposed for speaker and context independent emotion recognition. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the Pitch and energy Parameters.