• Smart Structures and Systems
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• v.3 no.1
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• pp.23-49
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• 2007

Wavelet transforms are the emerging signal-processing tools for damage identification and time-frequency localization. A small perturbation in a static or dynamic displacement profile could be captured using multi-resolution technique of wavelet analysis. The paper presents the wavelet analysis of damaged linear structural elements using DB4 or BIOR6.8 family of wavelets. Starting with a localized reduction of EI at the mid-span of a simply supported beam, damage modeling is done for a typical steel and reinforced concrete beam element. Rotation and curvature mode shapes are found to be the improved indicators of damage and when these are coupled with wavelet analysis, a clear picture of damage singularity emerges. In the steel beam, the damage is modeled as a rotational spring and for an RC section, moment curvature relationship is used to compute the effective EI. Wavelet analysis is performed for these damage models for displacement, rotation and curvature mode shapes as well as static deformation profiles. It is shown that all the damage indicators like displacement, slope and curvature are magnified under higher modes. A localization scheme with arbitrary location of curvature nodes within a pseudo span is developed for steady state dynamic loads, such that curvature response and damages are maximized and the scheme is numerically tested and proved.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.330-335
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• 2017

Cardiac arrest is owing to the failure of the heart that makes the blood circulation stop. Arrested blood circulation prevents the supply of the oxygen and the glucose and it results the loss of consciousness and, finally, brain death. Many public institution installed the AED for emergency treatment, but, it is not efficient when the patient is alone. In this paper, we made multiplexed wearable device for cardiac arrest detection. With this device, we measure the individual's electrocardiography, heart sound and motion. If the cardiac arrest is detected, the device make a warning horn and transmit the signal for defibrillation. We obtain 98.33% of ECG data, 94.5% of PCG data and 98.38% of IMU data accuracy for each evaluation and 93.33% accuracy for integrated evaluation.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1251-1256
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• 2022

Auscultation of heart sounds using a stethoscope is the basic method to diagnose the cardiovascular disease and observation of abnormalities. However, the heart sound transmitted to the ear through the stethoscope is greatly affected by internal sounds such as organ movement or breathing. In addition, the user's experience significantly influences the accuracy of the auscultation result. Therefore, in this paper, we developed a wearable device that simultaneously measures heart sound and PPG signals for cardiac condition monitoring. The structure of the proposed device is designed to simultaneously measure heart sound and PPG signals when worn on a finger and placed on the chest. A prototype was implemented according to the design structure, and it was confirmed that the performance of measurements and collection for physiological signals was excellent through experiments.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.305-311
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• 2006

In this paper the performance of a array invariant method is evaluated for source-range estimation in horizontally stratified shallow water ocean waveguide. The method has advantage of little computationally effort over existing source-localization methods. such as matched field processing or the waveguide invariant and array gain is fully exploited. And. no knowledge of the environment is required except that the received field should not be dominated by purely interference This simple and instantaneous method is applied to simulated acoustic propagation filed for testing range estimation performance. The result of range estimation according to the SNR for the underwater impulsive source with broadband spectrum is demonstrated. The spatial smoothing method is applied to suppress the effect of mutipath propagation by high frequency signal. The result of performance test for range estimation shows that the error rate is within 20% at the SNR above 10dB.