• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.405-410
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• 2000

Generally, it is known that the reduced integration, modified shape function anisoparametric and non-conforming element can minimize the error induced by stiffness locking phenomenon in the finite element analysis. In this study, new anisoparametric curved beam elements are introduced by using different shape functions in each displacement field. When these shape functions are substitute for functional, we can expect that the undulate stress patterns are not appeared or minimized because there is no unmatched coefficient in the constrained energy equation. As a result of numerical test, the undulate stress patterns are disappeared, and displacement and stress are coincide with the exact solutions.

• 대한기계학회논문집A
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• 제25권4호
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• pp.582-591
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• 2001

It is known that the reduced integration, modified shape function, anisoparametric and non-conforming element can reduce the error induced by stiffness locking phenomenon in the finite element analysis. In this study, we propose new anisoparametric curved beam element. The new element based on reduced minimization theory is composed of different shape functions in each displacement field. By the substitution of this modified shape function, the unmatched coefficient that cause stiffness locking in the constraint energy is eliminated. To confirm the availability of this new model, we performed numerical tests for a simple model. As a result of numerical test, the undulate stress patterns are disappeared in static analysis, and displacements and stresses are close to exact solution. Not only in the static analysis but also in the eigen analysis of free vibrated curved beam model, this element shows successful convergent results.

• 대한기계학회논문집A
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• 제20권12호
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• pp.3792-3803
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• 1996

Since the skew beam element has two curvatures which are a curvature and a torsion, spatial behavior of curved beam which cannot be included in one plane can be anlayzed by emploting the skew beam element. The $C^{0}$-continuous skew beam element shows the stiffness locking phenomenon when full integration is employed. The locking phenomenpn is characterized by two typical phenomena ; one is the much smaller displacement thant the exact one and theother is the undelation phenomenon is stress distribution. In this paper, we examine how unmatched coefficient in the constrained energy brings about the locking by Reduced Minimization theory. We perform the numerical ones. These comparisons show that uniformly full integration(UFI), which employs full integration for the constrained energy, entails the locking phenomenon. But the use of uniformly reduced integration(URI) of selectively reduced integration(SRI), which employs reduced integration for constrained energy, does not produce the significant errors of displacements of the undulation phenomenon in stress distribution since they do not entails the locking, Additionally, the error due to the approximated parameters for describing the geometry of skew beam is examined.d.

• Korean Journal of Radiology
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• 제24권8호
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.