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RISS 인기검색어
- 검색키워드
- 저자 : Yonghui Fu (검색결과 3 건)
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Yonghui Huang,Airong Liu,Yong-Lin Pi,Mark A. Bradford,Jiyang Fu 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.34 No.1
This paper presents experimental and numerical studies on effects of local damages on the in-plane elastic-plastic buckling and strength of a fixed parabolic steel tubular arch under a vertical load distributed uniformly over its span, which have not been reported in the literature hitherto. The in-plane structural behaviour and strength of ten specimens with different local damages are investigated experimentally. A finite element (FE) model for damaged steel tubular arches is established and is validated by the test results. The FE model is then used to conduct parametric studies on effects of the damage location, depth and length on the strength of steel arches. The experimental results and FE parametric studies show that effects of damages at the arch end on the strength of the arch are more significant than those of damages at other locations of the arch, and that effects of the damage depth on the strength of arches are most significant among those of the damage length. It is also found that the failure modes of a damaged steel tubular arch are much related to its initial geometric imperfections. The experimental results and extensive FE results show that when the effective cross-section considering local damages is used in calculating the modified slenderness of arches, the column bucking curve b in GB50017 or Eurocode3 can be used for assessing the remaining in-plane strength of locally damaged parabolic steel tubular arches under uniform compression. Furthermore, a useful interaction equation for assessing the remaining in-plane strength of damaged steel tubular arches that are subjected to the combined bending and axial compression is also proposed based on the validated FE models. It is shown that the proposed interaction equation can provide lower bound assessments for the remaining strength of damaged arches under in-plane general loading.
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Lei Zhou,Lin Fu,Na Lv,Jing Liu,Yan Li,Xiaosu Chen,Qingyu Xu,Guofeng Chen,Baoxu Pang,Lili Wang,Yonghui Li,Xiaodong Zhang,Li Yu 생화학분자생물학회 2018 Experimental and molecular medicine Vol.50 No.-
The AML1-ETO fusion protein (A/E), which results from the t(8;21) translocation, is considered to be a leukemiainitiating event. Identifying the mechanisms underlying the oncogenic activity of A/E remains a major challenge. In this study, we identified a specific down-regulation of brain acid-soluble protein 1 (BASP1) in t(8;21) acute myeloid leukemia (AML). A/E recognized AML1-binding sites and recruited DNA methyltransferase 3a (DNMT3a) to the BASP1 promoter sequence, which triggered DNA methylation-mediated silencing of BASP1. Ectopic expression of BASP1 inhibited proliferation and the colony-forming ability of A/E-positive AML cell lines and led to apoptosis and cell cycle arrest. The DNMT inhibitor decitabine up-regulated the expression of BASP1 in A/E-positive AML cell lines. In conclusion, our data suggest that BASP1 silencing via promoter methylation may be involved in A/E-mediated leukemogenesis and that BASP1 targeting may be an actionable therapeutic strategy in t(8;21) AML.
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Real Scene Text Image Super-Resolution Based on Multi-Scale and Attention Fusion
Xinhua Lu,Haihai Wei,Li Ma,Qingji Xue,Yonghui Fu 한국정보처리학회 2023 Journal of information processing systems Vol.19 No.4
Plenty of works have indicated that single image super-resolution (SISR) models relying on synthetic datasetsare difficult to be applied to real scene text image super-resolution (STISR) for its more complex degradation. The up-to-date dataset for realistic STISR is called TextZoom, while the current methods trained on this datasethave not considered the effect of multi-scale features of text images. In this paper, a multi-scale and attentionfusion model for realistic STISR is proposed. The multi-scale learning mechanism is introduced to acquiresophisticated feature representations of text images; The spatial and channel attentions are introduced to capturethe local information and inter-channel interaction information of text images; At last, this paper designs amulti-scale residual attention module by skillfully fusing multi-scale learning and attention mechanisms. Theexperiments on TextZoom demonstrate that the model proposed increases scene text recognition’s (ASTER)average recognition accuracy by 1.2% compared to text super-resolution network.
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