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RISS 인기검색어
- 검색키워드
- 저자 : Ge Xiaolin (검색결과 4 건)
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A Fast Multi-level Layout for Social Network Visualization
Xiaolin Du,Yunming Ye,Yueping Li,Ge Song 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.12
We describe a fast multi-level layout for visualizing social networks, which can visualize social networks high quality and rapidly. There are two innovations in our fast multi-level layout. Firstly, we proposed a new graph multi-layered compression method based on random walk. The multi-layered compression process groups vertices to form “planet” systems and then abstract these “planet” systems as new vertices to define a new graph and is repeated until the graph size falls below some threshold. And we also proposed a new single level force-directed layout based on sampling. The multi-level layout process can be accelerated based on these two innovations. Finally, we have evaluated our layout on several well-known data sets. The experimental results show that our layout outperforms the state-of-the-art method.
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( Yufeng Ge ),( Bin Wei ),( Siyu Wang ),( Zhiguo Guo ),( Xiaolin Xu ) 한국화학공학회 2015 Korean Chemical Engineering Research(HWAHAK KONGHA Vol.53 No.3
A large amount of dye wastewater poses a threat to environmental safety. Disperse blue, an anthraquinone dye that is widely used in textile dyes, is difficult to degrade in wastewater. In this work, one fungus was screened according to the decolorization rate of disperse blue. The fungus was identified and named Aspergillus XJ-2 on the basis of its morphological characteristics and 18s rDNA. Response surface method was used to optimize culture conditions for A. XJ-2. The optimum values of obtained responses were as follows: temperature, 35 °C; pH, 5.2; carbon-to nitrogen ratio, 30:5.5; and rotation ratio, 175 r·min-1. Under optimized conditions, the decolorization rate of A. XJ-2 was up to 94.8% in 48 h.
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Hu Zhigang,Ge Liyan,Zhang Huilin,Liu Xiaolin 아세아·태평양축산학회 2022 Animal Bioscience Vol.35 No.1
Objective: FKBP prolyl isomerase 5 (FKBP5) has been shown to play an important role in metabolically active tissues such as skeletal muscle. However, the expression of FKBP5 in Muscovy duck tissues and its association with body weight are still unclear. Methods: In this study, real-time quantitative polymerase chain reaction was used to detect the expression of FKBP5 in different tissues of Muscovy duck at different growth stages. Further, single nucleotide polymorphisms (SNPs) were detected in the exon region of FKBP5 and were combined analyzed with the body weight of 334 Muscovy ducks. Results: FKBP5 was highly expressed in various tissues of Muscovy duck at days 17, 19, 21, 24, and 27 of embryonic development. In addition, the expression of FKBP5 in the tissues of female adult Muscovy ducks was higher than that of male Muscovy ducks. Besides, an association analysis indicated that 3 SNPs were related to body weight trait. At the g.4819252 A>G, the body weight of AG genotype was significantly higher than that of the AA and the GG genotype. At the g.4821390 G>A, the genotype GA was extremely significantly related to body weight. At the g.4830622 T>G, the body weight of TT was significantly higher than GG and TG. Conclusion: These findings indicate the possible effects of expression levels in various tissues and the SNPs of FKBP5 on Muscovy duck body weight trait. FKBP5 could be used as molecular marker for muscle development trait using early marker-assisted selection of Muscovy ducks. Objective: FKBP prolyl isomerase 5 (FKBP5) has been shown to play an important role in metabolically active tissues such as skeletal muscle. However, the expression of FKBP5 in Muscovy duck tissues and its association with body weight are still unclear.Methods: In this study, real-time quantitative polymerase chain reaction was used to detect the expression of FKBP5 in different tissues of Muscovy duck at different growth stages. Further, single nucleotide polymorphisms (SNPs) were detected in the exon region of FKBP5 and were combined analyzed with the body weight of 334 Muscovy ducks.Results: FKBP5 was highly expressed in various tissues of Muscovy duck at days 17, 19, 21, 24, and 27 of embryonic development. In addition, the expression of FKBP5 in the tissues of female adult Muscovy ducks was higher than that of male Muscovy ducks. Besides, an association analysis indicated that 3 SNPs were related to body weight trait. At the g.4819252 A>G, the body weight of AG genotype was significantly higher than that of the AA and the GG genotype. At the g.4821390 G>A, the genotype GA was extremely significantly related to body weight. At the g.4830622 T>G, the body weight of TT was significantly higher than GG and TG.Conclusion: These findings indicate the possible effects of expression levels in various tissues and the SNPs of FKBP5 on Muscovy duck body weight trait. FKBP5 could be used as molecular marker for muscle development trait using early marker-assisted selection of Muscovy ducks.
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Fu Yang,Wang Shuo,Jia Feng,Zhou Quan,Ge Xiaolin 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
Due to the limited accessibility of wind turbines (WTs) and the complexity of operation and maintenance (O&M), it is increasingly important to early warn the component faults of WTs, and the difculties lie in balancing the comprehensiveness and delicacy of early warning. In this paper, a two-stage cascaded high-precision fault early warning method based on machine learning (ML) and data graphization is proposed. The frst stage copes with the early warning of the main components, in which the supervisory control and data acquisition (SCADA) data are converted into Gramian Angular Field (GAF) images to establish the potential relationship of fault features at diferent time points, and the fault characteristics are extracted by convolutional neural network (CNN) to realize fault early warning for multiple main components simultaneously. The second stage focus on the fault subcomponents inside the main components further, in which the time generative adversarial network (TimeGAN) is adopted to enhance the fault code data samples, then the enhanced data in the form of grayscale images is input into the Vision Transformer (ViT) to train the subcomponent early warning model. The proposed method is validated with real SCADA data, the results show the efectiveness of the proposed method.
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