• Journal of Chest Surgery
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• v.19 no.1
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• pp.165-173
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• 1986

During the 4 year period from 1982 through 1985, twelve patients have undergone operations for discrete subaortic stenosis with good short-term clinical result at Department of Thoracic and cardiovascular Surgery, S.N.U.H. According to the cineangiographic and operative findings, nine of the 12 patients were classified as Deutsch type I, the other 3 as type II, and eleven of the 12 had one or more associated anomalies of the cardiovascular system such as PDA[5], VSD[5], left SVC[2], MS[1], COA[1], supramitral membrane[1], DORY[1], right aortic arch[1], DCRV[1], and TOF[1] [one with Shone`s complex], and three of them had secondary cardiac disorders such as aortic regurgitation[3],mitral regurgitation[2], and tunnel shaped dynamic obstruction of left ventricular outflow tract[2]. We have performed membrane resection via oblique aortotomy with retraction of the aortic cusps in 7 cases and via VSD from right cardiac chamber in 5 cases with large VSD and have also performed the operations on the correctable associated anomalies. There was only one operative death in patient with associated TOF due to neurologic complication and no other postoperative difficulties except in one patient with transient heart block resolved spontaneously on postoperative 3rd day. To our knowledge, this article is the first report of operation for discrete subaortic stenosis in Korean literature.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.