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- 저자 : Yuwei Lu (검색결과 4 건)
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Research on Operational Intention Identification of Quayside Container Crane Driver
Wei Yan,Yuwei Zhao,Houjun Lu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1
To study intelligent auxiliary drive system of port machinery, it needs to identify operational intention of quayside container crane driver. With the background of loading and unloading process of quayside container crane, upon Hidden Markov Model, double HMM model is established. The algorithm of revised Forward-Backward is applied to calculate each likelihood of HMM in operation layer, the model of the largest likelihood is selected to be the identify result of operation behavior. After combining them to constitute the observation sequence bunch, it will be sent to the intention layer of HMM to conduct the identification of operation intention of crane driver. Finally, HMM is realized by Matlab. By means of field statistics, the basic data can be determined and effectiveness is also verified. It turns out that this model can accurately identify the operational intention of quayside container crane driver, which is of great significance for studying intelligent auxiliary drive system of port machinery.
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Xie, Yuwei,Hong, Seongjin,Kim, Seonjin,Zhang, Xiaowei,Yang, Jianghua,Giesy, John P.,Wang, Tieyu,Lu, Yonglong,Yu, Hongxia,Khim, Jong Seong Elsevier 2017 CHEMOSPHERE - Vol.172 No.-
<P><B>Abstract</B></P> <P>Benthic communities in the aquatic ecosystem are influenced by both natural and anthropogenic stressors. To understand the ecogenomic responses of sediment communities to the multiple stressors of polluted environments, the bacteria, protistan and metazoan communities in sediments from marine and adjacent riverine areas of North Bohai Sea were characterized by environmental DNA meta-systematics, and their associations with environmental variables were assessed by multiple statistical approaches. The bacterial communities were dominated by <I>Firmicutes</I> (mean 22.4%), <I>Proteobacteria</I> (mean 21.6%) and <I>Actinobacteria</I> (mean 21.5%). The protistan communities were dominated by <I>Ochrophyta</I> (33.7%), <I>Cercozoa</I> (18.9%) and <I>Ciliophora</I> (17.9%). <I>Arthropoda</I> (71.1%) dominated the metazoan communities in sediments. The structures of communities in sediments were shaped by both natural variables (spatial variability and/or salinity (presented as Na and Ca)) and anthropogenic contaminants, including DDTs, PAHs or metals (Cu, Al, Co, Cr, Cu, Fe, K, Mg, Mn, Ni and Zn). Particularly, the correlation network of multiple communities was modulated by the concentrations of Na and DDTs at the family level. Overall, environmental DNA meta-systematics can provide a powerful tool for biomonitoring, sediment quality assessment, and key stressors identification.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Bacterial, protistan and metazoan communities were characterized with ecogenomics. </LI> <LI> The influences of salinity on benthic communities overwhelmed these of pollutants. </LI> <LI> Variations of community structures also associated with DDTs, PAHs or metals. </LI> </UL> </P>
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A Genome-Wide Association Study to Identify Single-Nucleotide Polymorphisms for Acute Kidney Injury
Zhao, Bixiao,Lu, Qiongshi,Cheng, Yuwei,Belcher, Justin M.,Siew, Edward D.,Leaf, David E.,Body, Simon C.,Fox, Amanda A.,Waikar, Sushrut S.,Collard, Charles D. American Thoracic Society 2017 American journal of respiratory and critical care Vol.195 No.4
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Yinhua Liu,Rui Sun,Yuwei Lu,Shiming Zhang 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.10
The real-time process fault detection in the multi-station assembly process is always a challenging problem for auto body manufactures. Traditionally, the fault diagnosis approaches for variation source identification are divided into two categories, i.e. the pattern matching methods and model-based estimation ones based on the collected data set. The measurements provide effective process monitoring, but the real-time process fault diagnosis in the assembly process is still difficult with the traditional diagnosis techniques, and always depends on the engineering experience in practice. Based on the assembly process knowledge, including multi-station assembly hierarchy, fixture scheme, measurement characteristics and tolerances etc. in the multi-station, a knowledge-based diagnostic methodology and procedures are proposed with the measurements of each body in white for part/component defections and faulty assembly station identification. For the station involved with defective parts/components, the sub-coordinate system of the part/component is established reflecting its position and pose in the space, and then the relative pose matrix to the “normally build” pose is calculated based on the deviations of sub-coordinates of the parts in this station. Finally, the assembly process malfunctions are determined by a proposed rule-based strategy with the relative pose matrix in real time. A simple 3 stations assembly process with 5 sheet metal parts was analyzed and compared with the traditional diagnostic method to verify the effectiveness and stability of the proposed method.
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