• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.558-561
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• 1993

The main difference between a linear system and a nonlinear system is the existence of direct interactions between input signals. These interactions will be classified into three types, (1) self-interaction among different order terms of control signals, (2) static mutual interactions between the control signals, and (3) dynamic interactions through the coefficient venctor fields of the control variables. In this paper, we will show that interactions of type (2) and (3) can be avoided by applying an appropriate dynamic compensator, while the interaction of type (1) is fatal.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.63-72
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• 2014

The current study assessed the utility of acoustic analyses the most commonly used in routine clinical voice assessment including perturbation, nonlinear dynamic analysis, and Spectral/Cepstrum analysis based on signal typing of dysphonic voices and investigated their applicability of clinical acoustic analysis methods. A total of 70 dysphonic voice samples were classified with signal typing using narrowband spectrogram. Traditional parameters of %jitter, %shimmer, and signal-to-noise ratio were calculated for the signals using TF32 and correlation dimension(D2) of nonlinear dynamic parameter and spectral/cepstral measures including mean CPP, CPP_sd, CPPf0, CPPf0_sd, L/H ratio, and L/H ratio_sd were also calculated with ADSV(Analysis of Dysphonia in Speech and VoiceTM). Auditory perceptual analysis was performed by two blinded speech-language pathologists with GRBAS. The results showed that nearly periodic Type 1 signals were all functional dysphonia and Type 4 signals were comprised of neurogenic and organic voice disorders. Only Type 1 voice signals were reliable for perturbation analysis in this study. Significant signal typing-related differences were found in all acoustic and auditory-perceptual measures. SNR, CPP, L/H ratio values for Type 4 were significantly lower than those of other voice signals and significant higher %jitter, %shimmer were observed in Type 4 voice signals(p<.001). Additionally, with increase of signal type, D2 values significantly increased and more complex and nonlinear patterns were represented. Nevertheless, voice signals with highly noise component associated with breathiness were not able to obtain D2. In particular, CPP, was highly sensitive with voice quality 'G', 'R', 'B' than any other acoustic measures. Thus, Spectral and cepstral analyses may be applied for more severe dysphonic voices such as Type 4 signals and CPP can be more accurate and predictive acoustic marker in measuring voice quality and severity in dysphonia.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.95-102
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signals onto frequency domain. The raw signals can not show the significant feature, therefore difference values between the signal of the health conditions and that of the fault conditions are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the fault type. This study describes the results of detecting fault using wavelet analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.883-887
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• 2002

We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic and vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful fur the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We experimented vibrations by acceleration sensors and noises by microphones, compared and analysed for normal products, artificially deformed products. We tried to search a change of the dynamic signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method lot a detection of machine malfunction or fault diagnosis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.375-382
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• 2010

This paper presents the extraction of natural frequencies and mode shapes of a cable-stayed bridge using data acquired from GNSS. The response signals of 6 GNSS measuring points installed at the Seohae cable-stayed bridge are used for analysis of dynamic characteristics. Using normalization process and a third order Butterworth filter for the measured signals, the related pass band's signals have been isolated. Then, the acceleration data by double differentiation for these signals are obtained. Total five natural frequencies have been extracted by the fast Fourier transform and compared to the results of different studies. For the acceleration obtained from GNSS signals, the mode shapes of the bridge have been successfully extracted by TDD technique.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1062-1065
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• 2002

We have evaluated the ability of a time-delayed artificial neural network (TDANN) to predict muscle forces using only eletromyographic(EMG) signals. To achieve this goal, tendon forces and EMG signals were measured simultaneously in the gastrocnemius muscle of a dog while walking on a motor-driven treadmill. Direct measurements of tendon forces were performed using an implantable force transducer and EMG signals were recorded using surface electrodes. Under dynamic conditions, the relationship between muscle force and EMG signal is nonlinear and time-dependent. Thus, we adopted EMG amplitude estimation with adaptive smoothing window length. This approach improved the prediction ability of muscle force in the TDANN training. The experimental results indicated that dynamic tendon forces from EMG signals could be predicted using the TDANN, in vivo.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.58-68
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• 2000

This paper proposes a sinusoidal modeling of polyphonic audio signals. Sinusoidal modeling which has been applied well to speech and monophonic signals cannot be applied directly to polyphonic signals because a window size for sinusoidal analysis cannot be determined over the entire signal. In addition, for high quality synthesized signal transient parts like attacks should be preserved which determines timbre of musical instrument. In this paper, a multiresolution filter bank is designed which splits the input signal into six octave-spaced subbands without aliasing and sinusoidal modeling is applied to each subband signal. To alleviate smearing of transients in sinusoidal modeling a dynamic segmentation method is applied to subbands which determines the analysis-synthesis frame size adaptively to fit time-frequency characteristics of the subband signal. The improved dynamic segmentation is proposed which shows better performance about transients and reduced computation. For various polyphonic audio signals the result of simulation shows the suggested sinusoidal modeling can model polyphonic audio signals without loss of perceptual quality.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.522-528
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• 2000

Dynamic characteristics of seated human body were investigated by measuring apparent masses of eight different seating subjects exposed to vertical vibration. Two types of vibration signals - one is random signals over 1 to 30Hz having flat spectral density and the other is signals measured on seat rail in passenger car under driving conditions - were employed. It was found that the apparent masses are highly dependent on vibration level rather than type of the vibration signals. Based on the apparent mass measurements, a mathematical model of the human body in seating posture was developed by using genetic algorithm. Three-degree-of-freedom model was satisfactory in describing apparent mass of seated human body.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.409-428
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• 2004

In this paper, a Holder exponent, a measure of the degree to which a signal is differentiable, is presented to detect the presence of a discontinuity and when the discontinuity occurs in a dynamic signal. This discontinuity detection has potential applications to structural health monitoring because discontinuities are often introduced into dynamic response data as a result of certain types of damage. Wavelet transforms are incorporated with the Holder exponent to capture the time varying nature of discontinuities, and a classification procedure is developed to quantify when changes in the Holder exponent are significant. The proposed Holder exponent analysis is applied to various experimental signals to reveal underlying damage causing events from the signals. Signals being analyzed include acceleration response of a mechanical system with a rattling internal part, acceleration signals of a three-story building model with a loosing bolt, and strain records of an in-situ bridge during construction. The experimental results presented in this paper demonstrate that the Holder exponent can be an effective tool for identifying certain types of events that introduce discontinuities into the measured dynamic response data.