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188 results on '"GUO Wenfei"'

1. Detrital zircon trace elements from the Mesozoic Jiyuan Basin, central China and its implication on tectonic transition of the Qinling Orogenic Belt

Author

Wang Min, Guo Wenfei, and Yang Wentao

Subjects
zircon, basin-mountain interaction, subduction-collision transition, mianlve ocean, Geology, QE1-996.5
Abstract

The Qinling Orogen and the Jiyuan Basin constitute a basin-mountain system during the Early Mesozoic. Therefore, sediments from the Jiyuan Basin can be used to deduce the orogenic process of the Qinling Orogen. This paper attempts to use detrital zircon trace elements with ages ranging from the Late Carboniferous to the Middle Triassic that were obtained from the Jiyuan Basin to discuss the tectonic evolution of Qinling Orogen. On the tectonic setting discriminating diagrams, most grains are concentrated in convergent continental margins/orogenic settings,whereas the remaining samples (268 Ma, 265Ma, 264 Ma and 254Ma) are plotted in anorogenic field. Compared to the Early Paleozoic (400-500Ma) zircons, 306Ma and 281Ma grains represent higher Th/ Nb ratios, which might be related to the Mianlve oceanic crust subduction. The lower Th/Nb ratios containing 268 Ma, 265Ma, 264 Ma and 254Ma grains might indicate lithospheric extension subsequently. The final continent-continent collision between South China and North China blocks took place after the Middle Triassic (242Ma).

Published
2019
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2. Efficient and accurate numerical-projection of electromagnetic multipoles for scattering objects

Author

Guo, Wenfei, Cai, Zizhe, Xiong, Zhongfei, Chen, Weijin, and Chen, Yuntian

Subjects
Physics - Optics
Abstract

In this paper, we develop an efficient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical integration. Firstly, we briefly review the principles of multipole decomposition, highlighting two numerical projection methods including surface and volume integration. Secondly, we discuss the Lebedev and Gaussian quadrature methods, provide a detailed recipe to select the quadrature points and the corresponding weighting factor, and illustrate the integration accuracy and numerical efficiency (that is, with very few sampling points) using a unit sphere surface and regular tetrahedron. In the demonstrations of an isotropic dielectric nanosphere, a symmetric scatterer, and an anisotropic nanosphere, we perform multipole decomposition and validate our numerical projection procedure. The obtained results from our procedure are all consistent with those from Mie theory, symmetry constraints, and finite element simulations.

Published
2023
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21. Network Equipment Anomaly Detection Based on Time Delay Feature

Author

CUI Jingsong, ZHANG Tongtong, GUO Chi, GUO Wenfei

Subjects
anomaly detection, delay, network equipment, one-class support vector machine, peak position, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

With the rapid development of the Internet,the security of network equipment has received extensive attention.Aiming at the problems of that the existing network equipment anomaly detection technology is destructive and difficult to detect,the paper uses the packets delay spent by the network equipment to transmit and process data packets as the detection basis,and proposes an anomaly detection scheme based on delay characteristics.The proposed scheme adopts side channel analysis,and it does not need to upgrade the equipment's software or hardware.It has the characteristics of non-intrusive and easy to implement.Firstly,the method uses the high-precision timing technology time stamp machine to collect the time delay information,and uses the genetic algorithm to extract the peak position feature of the delay distribution.Secondly,to solve the problem of the imbalance of data set,the method uses one-class support vector machine algorithm to construct anomaly detection algorithm.Finally,the validity of the method is verified by building an experimental platform,and the experimental results are evaluated.Experimental results show that the proposed method is feasible and effective.

Published
2023
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40. Ribosomal Protein SA-Positive Neutrophil Elicits Stronger Phagocytosis and Neutrophil Extracellular Trap Formation and Subdues Pro-Inflammatory Cytokine Secretion Against Streptococcus suis Serotype 2 Infection

Author

Sun, Qiang, primary, Li, Na, additional, Jia, Li, additional, Guo, Wenfei, additional, Jiang, Hexiang, additional, Liu, Baijun, additional, Bao, Chuntong, additional, Liu, Mengmeng, additional, Huang, Jing, additional, and Lei, Liancheng, additional

Published
2021
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43. Research on the Purchase Intention of Electric Vehicles Based on Customer Evaluation and Personal Information.

Author

Chen, Jian, Zhang, Zhenshuo, Zhao, Chenyu, Zhang, Shuai, Guo, Wenfei, Lu, Cunhao, and Sun, Xiaoguang

Subjects
ELECTRIC vehicle industry, CONSUMERS, RANDOM forest algorithms, ELECTRIC automobiles
Abstract

With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers' purchase intentions. According to the data of customers' vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers' purchases are different for different vehicle brands. However, the customer's evaluation of the vehicle experience has a greater influence on the customer's purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers. [ABSTRACT FROM AUTHOR]

Published
2024
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