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RISS 인기검색어
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- 저자 : Xiyang Liu (검색결과 6 건)
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Optimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm
Feng Liu,Haitao Wu,Xiaochun Lu,Xiyang Liu 보안공학연구지원센터 2014 International Journal of Grid and Distributed Comp Vol.7 No.6
Massive data storage is one of the great challenges for cloud computing service, and reliable storage of sensitive data directly affects quality of storage service. In this paper, based on analysis of data storage process in cloud environment, the cost of massive data storage is considered to be comprised of data storage price, data migration and communication; and the storage reliability consists of data transmission reliability and hardware dependability. A multi-objective optimization model for reliable massive storage is proposed, in which storage cost and reliability are the objectives. Then, a genetic algorithm for solving the model is designed. Finally, experimental results indicate that the proposed model is positive and effective.
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A rolling bearing fault evolution state indicator based on deep learning and its application
Xiyang Liu,Guo Chen,Xunkai Wei,Yaobin Liu,Hao Wang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.6
Aiming at the limitation of early fault warning and the diagnosis of aero-engine main bearing when there are only normal operation data, a rolling bearing fault evolution state indicator based on deep convolutional neural network (CNN) and wavelet analysis was proposed. To be specific, firstly, the wavelet band envelope method was adopted to identify the early fault evolution process, and the feature distance between the degraded data and the normal ones was extracted by using deep CNN to develop the evolution state indicator. Then, the evolution stages were divided by using unsupervised clustering method. Finally, the remaining useful life (RUL) was predicted based on particle filter (PF). Three different groups of whole life cycle data of rolling bearings under various working conditions were used to prove the feasibility of the indicator. The results show that the wavelet-CNN features of completely different fault data show similar evolution trends, and the normalization of warning threshold can be realized based on the train labels. In conclusion, the results are of great significance for the early fault evolution monitoring, condition evaluation and remaining useful life prediction of rolling bearings without the absence of fault samples under actual aeroengine operation.
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Parallel Distributed Acceleration Based on MPI and OpenMP Technology
Feng Liu,Haitao Wu,Xiaochun Lu,Xiyang Liu 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.6
In order to speed up data processing in a signal monitoring and evaluation system, we need to use a parallel method. It is obvious that the traditional stand-alone store has no ability to satisfy the performance requirements, and the use of single core CPU is unable to content the severe requirement of speed. Consequently, multi-machine parallel acceleration technique based on MPI (cooperated with multi-core parallel acceleration technique based on OpenMP) can effectively solve all above problems. In this paper, a parallel distributed acceleration framework based on MPI and Open MP technology was given. Experimental tests were carried to verify our proposal. Finally, some suggestions to speed up the data processing was given.
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Xue Li,Tingting Liu,Fei Tian,Xiyang Tao,Zhansheng Wu 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.11
The high cost and low carrier separation efficiency of g-C3N4/Ag2O photocatalysts affect its application in the degradation of organic pollutants. In this study, the CA-βCD/g-C3N4/Ag2O 2D/0D heterojunction photocatalysts were successfully prepared to enhance the visible light response and inhibit the electron-hole recombination simultaneously during pollutant degradation. The 10:1:1 CA-βCD/g-C3N4/Ag2O showed the outstanding photochemical catalysis performance for the degradation of organic pollutants. The degradation efficiency of methyl orange, reactive black and norfloxacin was 2.53, 1.92 and 1.14 times than that of 1:1 g-C3N4/Ag2O. In addition, 10:1:1 CA-β-CD/g-C3N4/Ag2O also showed excellent photocatalytic stability. The free radical scavenging experiment and electron spin resonance proved that ·O − 2 was the chief active specie in the degradation process. The mechanism research results showed that the formation of heterojunction improved the utilization rate of sunlight and promoted the separation efficiency of photo-generated electrons and holes, which significantly advanced the photocatalytic activity of the composite catalyst. The preparation of CA-βCD/g-C3N4/Ag2O provided ideas for modification of photocatalyst by macromolecular organic matter.
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Viet Ha Vu,Xiyang Li,Mengyuan Wang,Rongmei Liu,Guojian Zhang,Wei Liu,Baixue Xia,Qun Sun 아세아·태평양축산학회 2019 Animal Bioscience Vol.32 No.7
Objective: This study aimed to gain deeper insights into the dynamic changes in spoilage fungi populations during fermentation and the influence of traditional additives on silage quality. Methods: Elephant grass (Pennisetum purpureum) was prepared without any additive (control), and with the addition of 0.5% salt, and 0.5% salt–0.2% sugar mixture. The fungal community was then determined using a classic culturing method and high-throughput sequencing at 0, 5, 15, and 60 days after ensiling. Results: The results showed that the fungal community of elephant grass silage varied significantly between the natural fermentation without any additive and the two additive groups. The diversity and relative abundance of spoilage molds in the control group were much higher than those in the two treatment groups (p<0.05). Three species of yeasts (Candida sp., Pichia sp., Trichosporon sp.) and four spoilage molds (Fusarium sp., Aspergillus sp., Muco sp. and Penicillin sp.) were the predominant fungi in elephant grass during natural fermentation from 0 to 60 days, which were found to be significantly decreased in salt and sugar additive groups (p<0.05). Meanwhile, the diversity and relative abundance of undesirable molds in the 0.5%-salt additive group were the lowest among all groups. Conclusion: Adding salt and sugar, particularly 0.5% salt, is a promising effective approach to reduce the amount of undesirable fungi thus, improving the silage quality of elephant grass in northern Vietnam.
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Wenhui Wang,Wang Zhang,Xiyang Liu,Xu Zhang,Weiqiang Huang,Zejian Deng 한국정밀공학회 2024 International Journal of Precision Engineering and Vol.25 No.5
Traditional manual teaching or offline programming welding modes may lead to long teaching time, low efficiency, and inability to adapt to changing welding environments when facing complex trajectory workpieces or Multi-welded workpieces. In this paper, a teaching-free welding method based on visual sensing system for robotic is proposed. Firstly, the three-dimensional information of the workpiece surface is captured by a monocular structured light camera and characterized by point cloud data. Point cloud stitching is performed on multiple local images of large-size workpieces to reconstruct the welding surface. Then, statistical filtering and deep learning methods are used to preprocess and segment the point cloud to obtain the reference points of the welding path. Finally, according to the shape characteristics of the workpiece, the auxiliary projection method is used to automatically generate the robot surfacing path. Experimental results show that under the condition of camera accuracy of ± 0.05 mm, the maximum planning path error is less than 1 mm, which meets the actual welding needs. This method is significant for achieving welding automation and improving production efficiency.
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