Yanjun Yu¹, Lei Yu^1,*, Huiqi Wang², Haodong Zheng¹, Yi Deng¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2225-2243, 2024, DOI:10.32604/cmc.2024.047641

Abstract Bone age assessment (BAA) helps doctors determine how a child’s bones grow and develop in clinical medicine. Traditional BAA methods rely on clinician expertise, leading to time-consuming predictions and inaccurate results. Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations. This operation is costly and subjective. To address these problems, we propose a multi-scale attentional densely connected network (MSADCN) in this paper. MSADCN constructs a multi-scale dense connectivity mechanism, which can avoid overfitting, obtain the local features effectively and prevent gradient vanishing even in limited training data. First, MSADCN designs… More >

Pandi Zhao¹, Zebang Zheng^1,*, Mei Zhan¹, Hongwei Li¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09508

Abstract Metallic sealing rings made from nickel-based superalloys are critical components of aero engines that prevent the leakage of high-pressure liquid or gas fuel. As one of the main failure modes, fatigue cracking has been a concern for the aerospace industries because the formation of even a micro-crack may cause an aviation accident. For the purpose of manufacturing fatigue-resisting sealing rings, much effort has been spent on the lifetime predicting under fatigue loadings. However, the fatigue analysis of metallic sealing rings is challenging due to several aspects. On the one hand, the diameter of the rings (>100mm) is orders of magnitude… More >