• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.4 s.247
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• pp.435-441
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• 2006

The anterior cruciate ligament(ACL) is an important stabilizer of knee joint. The ACL injury of knee is common and a serious ACL injury leads to ligament reconstruction surgery. Gait analysis is essential to identify knee condition of patients who display abnormal gait. The purpose of this study is to evaluate and classify knee condition of ACL deficient patients using a nonlinear dynamic method. The nonlinear method focuses on understanding how variations in the gait pattern change over time. The experiments were carried out for 17 subjects(l2 healthy subjects and five subjects with unilateral deficiency) walking on a motorized treadmill for 100 seconds. Three dimensional kinematics of the lower extremity were collected by using four cameras and KWON 3D motion analysis system. The largest Lyapunov exponent calculated from knee joint flexion-extension time series was used to quantify knee stability. The results revealed the difference between healthy subjects and patients. The deficient knee was significantly unstable compared with the contralateral knee. This study suggests an evaluation scheme of the severity of injury and the level of recovery. The proposed Lyapunov exponent can be used in rehabilitation and diagnosis of recoverable patients.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.10
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• pp.130-136
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• 2008

The purpose of this study was to investigate chaotic characteristics of major joint motions during treadmill walking. Gait experiments were carried out for 20 healthy young women. The subjects were asked to walk on a treadmill at their own natural speeds. The chaos analysis was used to quantify nonlinear motions of eleven major joints of each woman. The joints analyzed included the neck and the right and left shoulders, elbows, hips, knees and ankles. The recorded gait patterns were digitized and then coordinated by motion analysis software. Lyapunov exponent for every joint was calculated to evaluate joint characteristics from a state space created by time series and its embedding dimension. This study shows that differences in joint motion were statistically significant.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.226-235
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• 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
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• v.16 no.5
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.7 s.166
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• pp.1065-1074
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• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.194-201
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• 2006

Gait analysis is essential to identify accurate cause and knee condition from patients who display abnormal walking. Traditional linear tools can, however, mask the true structure of motor variability, since biomechanical data from a few strides during the gait have limitation to understanding the system. Therefore, it is necessary to propose a more precise dynamic method. The chaos analysis, a nonlinear technique, focuses on understand how variations in the gait pattern change over time. Eight healthy eight subjects walked on a treadmill for 100 seconds at 60 Hz. Three dimensional walking kinematic data were obtained using two cameras and KWON3D motion analyzer. The largest Lyapunov exponent from the measured knee angular displacement time series was calculated to quantify local stability. This study quantified the variability present in time series generated from gait parameter via chaos analysis. Knee flexion-extension patterns were found to be chaotic. The proposed Lyapunov exponent can be used in rehabilitation and diagnosis of recoverable patients.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.2 s.179
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• pp.164-171
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• 2006

Anterior cruciate ligament(ACL) injury of the knee is common and a serious ACL injury leads to ligament reconstruction surgery. Gait analysis is used to identify the result of surgery. The purpose of this study is to numerically evaluate and classify knee condition of patients through the chaos analysis. Experiments were carried out for 13 subjects (8 healthy subjects, 5 ACL deficient patients) walking on a treadmill. Sagittal kinematic data of the right lower extremity were collected by using a 3D motion analysis system. The recorded gait patterns were digitized and then coordinated by KWON3D. The largest Lyapunov exponent from the measured knee angular displacement time series was calculated to quantify local stability. It was found that the Lyapunov exponent becomes larger as the knee condition becomes worse. This study suggested a method of the severity of injury and the level of recovery. The proposed method discerns difference between healthy subjects and patients.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.167-174
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• 1998

The ultrasonic test is recognized for its significance as a non-destructive testing method to detect volume defects such as porosity and incomplete penetration which reduce strength in the weld zone. This paper illustrates the defect detection in the weld zone of ferritic carbon steel using ultrasonic wave and the evaluation of pattern recognition by chaotic feature extraction using time series signal of detected defects as data. Shown in the time series data were that the time delay was 4 and the embedding dimension was 6 which indicate the geometric dimension of the subject system and the extent of information correlation. Based on fractal dimension and lyapunov exponent in quantitative chaotic feature extraction, feature value of 2.15, 0.47 is presented for porosity and 2.24, 0.51 for incomplete penetration The precision rate of the pattern recognition is enhanced with these values on the total waveform of defect signal in the weld zone. Therefore, we think that the ultrasonic pattern recognition method of weld zone defects of ferritic carbon steel by ultrasonic-chaotic feature extraction proposed in this paper can boost precision rate further than the existing method applying only partial waveform.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.69-74
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• 1997

Sleep deprivation may affect the brain functions such as cognition and, consequentoy, dynamics of the EEG. we examiced the effects of sleep deprivation on chaoticity of EEG. Five volunteers were sleep-deprived over a period of 24 hours, They were checked by EEG during two days, the first day of baseline period, EEGs were reorded form 16 channels for nonlinear analysis. We dmployed a method of minimum cmbedding dimension to calculate the first positive Lyapunov exponent. For limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Our results show that the sleep deprived volunteers had lower values of the first positive Lyapunov exponent at ten channels (Fp$\_$1/, F$\_$4/, F$\_$8/, T$\_$4/, T$\_$5/, C$\_$3/, C$\_$4/, P$\_$3/, p$\_$4, O$\_$1/) compared with the values of baseline periods. These results suggested that sleep deprivation leads to decreawe of chaotic activity in brain and impairment of the information processing in the brain. We suggested that nonlinear analysis of the EEG before and after sleep deprivation may offer fruitful perspectives for understanding the role o f sleep deprivation on the brain function.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.152-165
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• 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.