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- 저자 : Gao Yiran (검색결과 2 건)
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Weimin Sun,Yiran Dong,Pin Gao,Meiyan Fu,Kaiwen Ta,Jiwei Li 한국미생물학회 2015 The journal of microbiology Vol.53 No.6
Although oilfields harbor a wide diversity of microorganisms with various metabolic potentials, our current knowledge about oil-degrading bacteria is limited because the vast majority of oil-degrading bacteria remain uncultured. In the present study, microbial communities in nine oil-contaminated soils collected from Daqing and Changqing, two of the largest oil fields in China, were characterized through highthroughput sequencing of 16S rRNA genes. Bacteria related to the phyla Proteobacteria and Actinobacteria were dominant in four and three samples, respectively. At the genus level, Alkanindiges, Arthrobacter, Pseudomonas, Mycobacterium, and Rhodococcus were frequently detected in nine soil samples. Many of the dominant genera were phylogenetically related to the known oil-degrading species. The correlation between physiochemical parameters within the microbial communities was also investigated. Canonical correspondence analysis revealed that soil moisture, nitrate, TOC, and pH had an important impact in shaping the microbial communities of the hydrocarbon-contaminated soil. This study provided an in-depth analysis of microbial communities in oilcontaminated soil and useful information for future bioremediation of oil contamination.
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Partial Discharge Detection Method Based on DD-DT CWT and Singular Value Decomposition
Wu Chao,Gao Yiran,Wang Ruoyan,Wang Kai,Liu Siyang,Nie Yongjie,Wang Ping 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.4
Partial discharge (PD) detection is signifi cant for insulation condition evaluation of electrical equipment. However, it often happens that the PD signals are submerged by interferences, which will cause the inaccurate detection results. In this paper, we propose a PD detection method based on double-density dual-tree complex wavelet transform (DD-DT CWT) and singular value decomposition (SVD) to solve this problem. The denoising method based on DD-DT CWT has better performance in both removing interferences and retaining features of PD signals. The inner product of the singular value matrix obtained by applying SVD to denoised high-frequency wavelet coeffi cient matrix can concisely represent the complexity of the tested signal, which can be used as a basis to judge the existence of the PD signal. Besides, Otsu algorithm is introduced to calculate the threshold to locate the appearance time of the PD signal. Experimental results show that the proposed method can detect the PD signal with the accuracy rate of 77.8% when PD signals are submerged by noises, while the traditional method cannot detect the existence of the PD signal. In addition, only the method proposed in this paper can detect the appearance time of the PD signal with the accuracy rate of 97.2%.
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