• 한국정밀공학회지
• /
• 제15권11호
• /
• pp.152-165
• /
• 1998

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaoral post-process system for precision rate enhancement of ultrasonic pattern recognition. Chaos features extracted from time series data for analysis quantitatively weld defects For this purpose, feature extraction objectives in this study are fractal dimension, Lyapunov exponent, shape of strange attrator. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shifts such as nearby 0.5, 1.0 skip distance. Such difference in chaoticity enables the evaluation of unique features of defects in the weld zone. In quantitative chaos fenture extraction, feature values of 0.835 and 0.823 in the case of slag inclusion and 0.609 and 0.573 in the case of crack were suggested on the basis of fractal dimension and Lyapunov exponent. Proposed chaoral post-process system in this study can enhances precision rate of ultrasonic pattern recognition results from defect signals of weld zone, such as slag inclusion and crack.

• 음성과학
• /
• 제1권
• /
• pp.285-293
• /
• 1997

This paper was constructed to investigate strange attractor in considering speech which is regarded as chaos in that the random signal appears in the deterministic raising system. This paper searches for the delay time from AR model power spectrum for constructing fit attractor for speech signal. As a result of applying Taken's embedding theory to the delay time, an exact correlation dimension solution is obtained. As a result of this consideration of speech, it is found that it has more speaker recognition characteristic parameter, and gains a large speaker discrimination recognition rate.

• 한국멀티미디어학회논문지
• /
• 제4권6호
• /
• pp.518-523
• /
• 2001

미세한 차이를 고감도 식별하는 카오스 이론의 프랙탈 차원과 에농 시스템에서 발생하는 이상한 끌개(Strange Attractor)를 이용하여 문자 특징을 추출, 문자 인식에 적용하는 새로운 방법을 제안함으로써 일반문자 뿐만 아니라, 문자들의 유사성에 의해 오인식되는 혼동 문자를 프랙탈 차원 해석에 의해 해소하는 고정도 문자 인식 시스템을 구현한다. 먼저, 문자 영상으로부터 문자의 고유 성질을 나타내는 망 특징 및 투영 특징, 교차거리 특징 등을 1차 구한 후, 이들 특징을 시계열 데이터로 변환한 다음, 이를 본 논문에서 제안한 수정된 에농 시스템을 이용하여, KS C 5601 표준 한글 2,350자에 대 한 각각의 문자 어트랙터를 재구성한다. 다음 단계에서는 개별 문자 어트랙터의 혼돈도를 분석하기 위해 각각의 문자에 대하여, 프랙탈 차원을 나타내는 정보 차원값(Box-counting Dimension, Natural Measure, Information Bit, Information Dimension)을 계산하여 문자 영상의 최종 특징을 구한다. 실험결과 한글 2,350자에 대하여 99.49%은 분류율을 나타내어 제안된 방법의 유효성을 보였다.

• 충청수학회지
• /
• 제20권4호
• /
• pp.577-585
• /
• 2007

We introduce the inverse shadowing property of geometric Lorenz flows and prove that the geometric Lorenz flows do not have the inverse shadowing property.

• 한국정밀공학회지
• /
• 제15권12호
• /
• pp.226-235
• /
• 1998

This study proposes the analysis and evaluation of method of time series of ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. The features are extracted from time series data for analysis of weld defects quantitatively. For this purpose, analysis objectives in this study are fractal dimension, Lyapunov exponent, and strange attractor on hyperspace. The Lyapunov exponent is a measure of rate in which phase space diverges nearby trajectories. Chaotic trajectories have at least one positive Lyapunov exponent, and the fractal dimension appears as a metric space such as the phase space trajectory of a dynamical system. In experiment, fractal(correlation) dimensions and Lyapunov exponents show the mean value of 4.663, and 0.093 relatively in case of learning, while the mean value of 4.926, and 0.090 in case of testing in slag inclusion(weld defects) are shown. Therefore, the proposed chaotic feature extraction can be enhancement of precision rate for ultrasonic pattern recognition in defecting signals of weld zone, such as slag inclusion.

• Journal of Welding and Joining
• /
• 제16권5호
• /
• pp.108-118
• /
• 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.

• 대한기계학회논문집A
• /
• 제23권7호
• /
• pp.1065-1074
• /
• 1999

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaotic time series signal analysis quantitatively welding 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 chaoticity resulting from distance shills such as 0.5 and 1.0 skip distance. Such differences in chaoticity enables the evaluation of unique features of defects in the weld zone. In experiment fractal(correlation) dimension and Lyapunov exponent extracted from 6dB ultrasonic defect signals of weld zone showed chaoticity. In quantitative chaotic feature extraction, feature values(mean values) of 4.2690 and 0.0907 in the case of porosity and 4.2432 and 0.0888 in the case of incomplete penetration were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaotic feature extraction in this study enhances ultrasonic pattern recognition results from defect signals of weld zone such as vertical hole.

• 전자공학회논문지S
• /
• 제36S권1호
• /
• pp.115-122
• /
• 1999

본 논문은 음성신호가 카오스 신호임을 확인하고 화자인식 파라미터로 사용하기 위해 상관차원을 분석하였다. 화자식별과 인식 향상을 위하여 개인의 성도특성을 매우 잘 나타내는 음성의 스트레인지 어트렉터를 구성하고 퍼지유사도를 상관차원에 적용하여 퍼지상관차원을 제안하였다. 퍼지상관차원은 어트렉터 구성점들의 상관관계글 퍼지상관적분으로 추정하고 공간차원에 따라 퍼지상관지수가 일정하게 수렴되는 차원값을 구하여 표준패턴 어트렉터와 시험패턴 어트렉터의 변동을 흡수하였다. 퍼지상관차원에 대해 화자와 표준패턴별로 식별오차의 평균값에 따른 거리를 추정함으로써 화자인식파라미터의 타당성을 검토하였다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1997년도 하계학술대회 논문집 D
• /
• pp.1144-1148
• /
• 1997

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 하계학술대회 논문집 B
• /
• pp.578-580
• /
• 1995

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time, For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor font mentioned above. The one day ahead forecast errors are about 1.4% of absolute percentage average error.