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Anti-sparse representation for structural model updating using l∞ norm regularization
Ziwei Luo,Ling Yu,Huan-Lin Liu,Zexiang Chen 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.75 No.4
Finite element (FE) model based structural damage detection (SDD) methods play vital roles in effectively locating and quantifying structural damages. Among these methods, structural model updating should be conducted before SDD to obtain benchmark models of real structures. However, the characteristics of updating parameters are not reasonably considered in existing studies. Inspired by the l∞ norm regularization, a novel anti-sparse representation method is proposed for structural model updating in this study. Based on sensitivity analysis, both frequencies and mode shapes are used to define an objective function at first. Then, by adding l∞ norm penalty, an optimization problem is established for structural model updating. As a result, the optimization problem can be solved by the fast iterative shrinkage thresholding algorithm (FISTA). Moreover, comparative studies with classical regularization strategy, i.e. the l2 norm regularization method, are conducted as well. To intuitively illustrate the effectiveness of the proposed method, a 2-DOF spring-mass model is taken as an example in numerical simulations. The updating results show that the proposed method has a good robustness to measurement noises. Finally, to further verify the applicability of the proposed method, a six-storey aluminum alloy frame is designed and fabricated in laboratory. The added mass on each storey is taken as updating parameter. The updating results provide a good agreement with the true values, which indicates that the proposed method can effectively update the model parameters with a high accuracy.
Metagenomics analysis of the gut microbiome in healthy and bacterial pneumonia forest musk deer
Wei Zhao,Ziwei Ren,Yan Luo,Jianguo Cheng,Jie Wang,Yin Wang,Zexiao Yang,Xueping Yao,Zhijun Zhong,Wei Yang,Xi Wu 한국유전학회 2021 Genes & Genomics Vol.43 No.1
Background The forest musk deer (FMD, Moschus berezovskii) is an threatened species in China. Bacterial pneumonia was found to seriously restrict the development of FMD captive breeding. Historical evidence has demonstrated the relationship between immune system and intestinal Lactobacillus in FMD. Objective We sought to elucidate the diferences in the gut microbiota of healthy and bacterial pneumonia FMD. Methods The bacterial pneumonia FMD was demonstrated by bacterial and pathological diagnosis, and the gut microbiome of healthy and bacterial pneumonia FMD was sequenced and analysed. Results There are three pathogens (Pseudomonas aeruginosa, Streptococcus equinus and Trueperella pyogenes) isolated from the bacterial pneumonia FMD individuals. Compared with the healthy group, the abundance of Firmicutes and Proteobacteria in the pneumonia group was changed, and a high level of Proteobacteria was found in the pneumonia group. In addition, a higher abundance of Acinetobacter (p=0.01) was observed in the population of the pneumonia group compared with the healthy group. Several potentially harmful bacteria and disease-related KEGG subsystems were only found in the gut of the bacterial pneumonia group. Analysis of KEGG revealed that many genes related to type IV secretion system, type IV pilus, lipopolysaccharide export system, HTH-type transcriptional regulator/antitoxin MqsA, and ArsR family transcriptional regulator were signifcantly enriched in the metagenome of the bacterial pneumonia FMD. Conclusion Our results demonstrated that the gut microbiome was signifcantly altered in the bacterial pneumonia group. Overall, our research improves the understanding of the potential role of the gut microbiota in the FMD bacterial pneumonia.
Huan-Lin Liu,Ling Yu,Ziwei Luo,Zexiang Chen 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.75 No.4
Recently, many structural damage detection (SDD) methods have been proposed to monitor the safety of structures. As an important modal parameter, mode shape has been widely used in SDD, and the difference of vectors was adopted based on sensitivity analysis and mode shapes in the existing studies. However, amplitudes of mode shapes in different measured points are relative values. Therefore, the difference of mode shapes will be influenced by their amplitudes, and the SDD results may be inaccurate. Focus on this deficiency, a multi-strategy SDD method is proposed based on the included angle of vectors and sparse regularization in this study. Firstly, inspired by modal assurance criterion (MAC), a relationship between mode shapes and changes in damage coefficients is established based on the included angle of vectors. Then, frequencies are introduced for multi-strategy SDD by a weighted coefficient. Meanwhile, sparse regularization is applied to improve the ill-posedness of the SDD problem. As a result, a novel convex optimization problem is proposed for effective SDD. To evaluate the effectiveness of the proposed method, numerical simulations in a planar truss and experimental studies in a six-story aluminum alloy frame in laboratory are conducted. The identified results indicate that the proposed method can effectively reduce the influence of noises, and it has good ability in locating structural damages and quantifying damage degrees.