• Journal of the Korea Concrete Institute
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• v.19 no.4
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• pp.515-525
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• 2007

In this investigation, results of laboratory tests on four reinforced concrete flat plate interior connections with elongated rectangular column support which has been used widely in tall residential buildings are presented. The purpose of this study is to evaluate an effect of column aspect ratio (${\beta}_c={c_1}/{c_2}$=side length ratio of column section in the direction of lateral loading $(c_1)$ to the direction of perpendicular to $c_1$) on the hysteretic behavior under earthquake type loading. The aspect ratio of column section was taken as $0.5{\sim}3\;(c_1/c_2=1/2,\;1/1,\;2/1,\;3/1)$ and the column perimeter was held constant at 1200mm in order to achieve nominal vertical shear strength $(V_c)$ uniformly. Other design parameters such as flexural reinforcement ratio $(\rho)$ of the slab and concrete strength$(f_{ck})$ was kept constant as ${\rho}=1.0%$ and $f_{ck}=40MPa$, respectively. Gravity shear load $(V_g)$ was applied by 30 percent of nominal vertical shear strength $(0.3V_o)$ of the specimen. Experimental observations on punching failure pattern, peak lateral-load and story drift ratio at punching failure, stiffness degradation and energy dissipation in the hysteresis loop, and steel and concrete strain distributions near the column support were examined and discussed in accordance with different column aspect ratio. Eccentric shear stress model of ACI 318-05 was evaluated with experimental results. A fraction of transferring moment by shear and flexure in the design code was analyzed based on the test results.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.