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- 저자 : Yao LingFang (검색결과 3 건)
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Lingfang Li,Xiaoyou Wang,Wei Tian,Yao Du,Rong-rong Hou,Yong Xia 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The singlepeaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.
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Lingfang Li,Yao Du,Rong-rong Hou,Xiaoyou Wang,Wei Tian,Yong Xia 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.
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A novel CHD7 variant in a chinese family with CHARGE syndrome
Shan Yanhong,Yao LingFang,Li Linli,Gao Xueping,Jiang Jinghan 한국유전학회 2024 Genes & Genomics Vol.46 No.3
Objective CHARGE syndrome is a rare autosomal dominant (AD) multi-system disorder with a broad and variable clinical manifestation and occurs in approximately 1/10,000 newborns in the world. Mutations in the CHD7 gene are the genetic cause of over 90% of patients with typical CHARGE syndrome. The present study reported a novel variant in the CHD7 gene in a Chinese family with an abnormal fetus. Methods Routine prenatal ultrasound screening showed fetal heart abnormality and left foot varus. Chromosomal microarray analysis (CMA) and fetus-parent whole-exome sequencing (trio-WES) were performed to determine the genetic cause of the fetus. The candidate variant was further verified using Sanger sequencing. Results CMA analysis revealed normal results. However, WES analysis identified a de novo heterozygous variant of c.2919_2922del (NM_017780.4) on exon 11 of CHD7 gene, resulting in a premature truncation of the CHD7 protein (p.Gly975*). The variant was classified as Pathogenic (PVS1 + PS2_Moderate + PM2_Supporting) based on the ACMG guidelines. Combined with the clinical phenotype of fetal heart abnormalities, it was confirmed CHARGE syndrome. Conclusion We identified a novel heterozygous variant c.2919_2922del in CHD7 of a Chinese fetus with CHARGE syndrome, enriching the genotype-phenotype spectrum of CHD7. These results suggest that genetic testing could help facilitate prenatal diagnosis of CHARGE syndrome, thus promoting the appropriate genetic counseling. Objective CHARGE syndrome is a rare autosomal dominant (AD) multi-system disorder with a broad and variable clinical manifestation and occurs in approximately 1/10,000 newborns in the world. Mutations in the CHD7 gene are the genetic cause of over 90% of patients with typical CHARGE syndrome. The present study reported a novel variant in the CHD7 gene in a Chinese family with an abnormal fetus. Methods Routine prenatal ultrasound screening showed fetal heart abnormality and left foot varus. Chromosomal microarray analysis (CMA) and fetus-parent whole-exome sequencing (trio-WES) were performed to determine the genetic cause of the fetus. The candidate variant was further verified using Sanger sequencing. Results CMA analysis revealed normal results. However, WES analysis identified a de novo heterozygous variant of c.2919_2922del (NM_017780.4) on exon 11 of CHD7 gene, resulting in a premature truncation of the CHD7 protein (p.Gly975*). The variant was classified as Pathogenic (PVS1 + PS2_Moderate + PM2_Supporting) based on the ACMG guidelines. Combined with the clinical phenotype of fetal heart abnormalities, it was confirmed CHARGE syndrome. Conclusion We identified a novel heterozygous variant c.2919_2922del in CHD7 of a Chinese fetus with CHARGE syndrome, enriching the genotype-phenotype spectrum of CHD7. These results suggest that genetic testing could help facilitate prenatal diagnosis of CHARGE syndrome, thus promoting the appropriate genetic counseling.
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