• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.489-496
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• 2019

This paper is a study on the evaluation of loss factor of damping treatment materials to reduce the noise and vibration for panels of railway vehicles and automobiles. In order to determine the modal parameters of damping materials, beam excitation tests were carried out using different type PVC coated aluminum and steel base beam specimens. The specimens were excited from 10 Hz to 1000 Hz frequency range using sinusoidal force, and transfer mobility data were measured by using an accelerometer. The loss factors were determined by using integrated program, based on theories of Half Power Method, Minimum Tangent Error Method, Minimum Angle Error Method and Phase Change Method, which enable to evaluate the parameters using modal circle fit and least squares error method. In the case of lower loss factor and data of linear characteristics, any method could be applied for evaluation of parameters, however the case of higher loss factor or data including non-linear characteristics, the minimum angle error method could reduce the loss factor evaluation. The obtained dynamic properties of the coating material could be used for application of Finite Element Method analyzing the noise control effects of complex structures such as carbody or under-floor boxes of rolling stock. The damping material will be very useful to control the structural noise, because the obtained modal loss factors of each mode show very good effect on over $2^{nd}$ mode frequency range.

• Journal of the Korean Society of Radiology
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• v.3 no.1
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• pp.29-39
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• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.