• Composites Research
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• v.34 no.3
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• pp.161-166
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• 2021

In this study, we present a research method to develop a shoe that prevents foot injury by inducing the foot pressure. An orthogonal grid sensor was used to check the foot pressure in the upright standing position, and the change in the foot pressure distribution for various conditions was compared. We checked the conditions for distributing foot pressure efficiently by changing the spring constant of the spring inserted into the sole of the shoe and the foot pressure generated with or without the arch of the insole. In order to minimize the experimental error from the randomness of the human body's behavior, it is possible to predict through foot pressure under certain conditions through finite element analysis that simulates the pressure distribution. By checking the change of foot pressure according to the number and arrangement of springs through finite element analysis, conditions were established to provide more efficient foot pressure. The result can be used for designing footwear for patients with diabetic feet.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.153-159
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• 2011

The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.