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- 저자 : Yikai Hao (검색결과 2 건)
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Research on Cold-Formed Steel Stiffened-Web Built-up I-Section Columns with Complex Edge Stiffeners
Zhuangnan Zhang,Yudong Li,Chungang Wang,Yikai Hao,Jianqiao Song,Qinglin Guo 한국강구조학회 2022 International Journal of Steel Structures Vol.22 No.1
A series of double-limbs built-up I-section simply supported specimens subjected to compression were examined. The test columns contained 18 concentric compression columns and 12 eccentric compression columns with three section forms respectively. The infl uence of web stiff eners on buckling mode, ultimate bearing capacity and interaction between limbs were studied. It was shown that stiff eners in web eff ectively decreased the element width-to-thickness ratio and increased the bearing capacity of built-up I-section columns. But when larger eccentricity made the moment control the failure of eccentric compression specimens, the bearing capacity of the specimens was not improved obviously by web stiff eners. Distortional buckling could replace local buckling to control the failure modes of columns. Compared with double-limbs channel steel built-up open-sections with complex edge stiff eners under the same steel quantity and eccentricity, the load carrying capacity of double limb Σ-shaped channel built-up sections increased from 4.4% to 20.3%. While for double limb channel with V-type web stiff ener sections, the increment was from 2.2% to 17.4%. Furthermore, parametric study of 90 Σ-shaped built-up section members was investigated to obtain the optimization proportion of the web sub-element and relationship of carrying capacity between single Σ-shaped section and double limb Σ-shaped I-section. Finally, the ultimate load-carrying capacity of built-up columns subjected to axial compression were calculated by two kinds of direct strength method formulas, which were compared with the tests values. The results showed that the method considered the interaction between distortional buckling and fl exural buckling could obtain conservative results.
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Yan Chenggong,Lin Jie,Li Haixia,Xu Jun,Zhang Tianjing,Chen Hao,Woodruff Henry C.,Wu Guangyao,Zhang Siqi,Xu Yikai,Lambin Philippe 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.6
Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signedrank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
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