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您的检索式：作者名="Mingfei Shen"
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| 1 | Data augmentation in microscopic images for material data mining显示文摘Recent progress in material data mining has been driven by high-capacity models trained on large datasets.However,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise required.Here,we develop a novel transfer learning strategy to address problems of small or insufficient data.This strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining procedure.For a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real images.The results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real data.Because the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation. | Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su | 2020 | npj Computational Materials2020，，1： | 1 |
| 2 | Author Correction:Data augmentation in microscopic images for material data mining显示文摘The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent Science and Engineering,University of Science and Technology Beijing”.This has now been corrected in both the PDF and HTML versions of the article. | Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su | 2020 | npj Computational Materials2020，，1： | 0 |
| 3 | Immobilization and Properties of Polyphenol Oxidase显示文摘[Objectives]This study was conducted to investigate the effects of embedding conditions on activity and catalytic properties of immobilized polyphenol oxidase.[Methods]Polyphenol oxidase was immobilized in a polymer material by the embedding method,and the optimal immobilization conditions were obtained by single factor tests:CaCl_(2) concentration 2.0%,sodium alginate concentration 2.0%,immobilization time 2 h and mass ratio of enzyme to carrier 10 mg/100 g,under which the immobilized enzyme activity was 93.33 U/g.Under the above conditions,the properties of polyphenol oxidase immobilized by sodium alginate(A-PPO)and free polyphenol oxidase were studied.[Results]The thermostability of A-PPO was better than that of the free enzyme,but the pH stability of A-PPO was inhibited.The Michaelis constant K_(m) values of free polyphenol oxidase and A-PPO were 0.37 and 0.48 mmol/L,respectively,and the maximum reaction rate V_(max) values were 0.38 and 0.51 mmol/(L·g),respectively.[Conclusions]This study provides a theoretical basis for the study of the properties of polyphenol oxidase. | Zheng LI Pan WANG Yanxi SHEN Mingfei WEI Yimin HE Aoyu JI Jianfeng ZHAN | 2021 | Agricultural Biotechnology2021，10，6： | 0 |
| 4 | ARoad Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network显示文摘Convolutional neural networks(CNN)based on U-shaped structures and skip connections play a pivotal role in various image segmentation tasks.Recently,Transformer starts to lead new trends in the image segmentation task.Transformer layer can construct the relationship between all pixels,and the two parties can complement each other well.On the basis of these characteristics,we try to combine Transformer pipeline and convolutional neural network pipeline to gain the advantages of both.The image is put into the U-shaped encoder-decoder architecture based on empirical combination of self-attention and convolution,in which skip connections are utilized for localglobal semantic feature learning.At the same time,the image is also put into the convolutional neural network architecture.The final segmentation result will be formed by Mix block which combines both.The mixture model of the convolutional neural network and the Transformer network for road segmentation(MCTNet)can achieve effective segmentation results on KITTI dataset and Unstructured Road Scene(URS)dataset built by ourselves.Codes,self-built datasets and trainable models will be available on http://gffzz188fe103f8f1460asqp9v95w9cxxv6bc6.ffgz.tsg.suse.edu.cn/xflxfl1992/MCTNet. | Fenglei Xu Haokai Zhao Fuyuan Hu Mingfei Shen Yifei Wu | 2023 | Computer Modeling in Engineering & Sciences2023，，5： | 0 |
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