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857 results on '"Xie, Miao"'

33. Interconnected Neural Linear Contextual Bandits with UCB Exploration

Author

Chen, Yang, Xie, Miao, Liu, Jiamou, Zhao, Kaiqi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gama, João, editor, Li, Tianrui, editor, Yu, Yang, editor, Chen, Enhong, editor, Zheng, Yu, editor, and Teng, Fei, editor

Published
2022
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34. SDGen: A Scalable, Reproducible and Flexible Approach to Generate Real World Cyber Security Datasets

Author

Koay, Abigail M. Y., Xie, Miao, Ko, Ryan K. L., Sterner, Charles, Choi, Taejun, Dong, Naipeng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Guojun, editor, Choo, Kim-Kwang Raymond, editor, Ko, Ryan K. L., editor, Xu, Yang, editor, and Crispo, Bruno, editor

Published
2022
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39. Geographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix)

Author

Xie, Miao, Jiang, Zhe, and Sainju, Arpan Man

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity in spectral features as well as implicit anisotropic spatial dependency across class labels. In this paper, we propose geographical hidden Markov tree, a probabilistic graphical model that generalizes the common hidden Markov model from a one dimensional sequence to a two dimensional map. Anisotropic spatial dependency is incorporated in the hidden class layer with a reverse tree structure. We also investigate computational algorithms for reverse tree construction, model parameter learning and class inference. Extensive evaluations on both synthetic and real world datasets show that proposed model outperforms multiple baselines in flood mapping, and our algorithms are scalable on large data sizes.

Published
2018

50. PERFORMANCE ANALYSIS AND WEAR MECHANISM RESEARCH OF FEEDING SCREW OF COAL CLEANING MACHINE

Author

XIE Miao, PEI BoBo, LIU ZhiXiang, LI Zheng, and GENG XiWang

Subjects
Conveying screw, Conveying characteristics, Wear, Optimization, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

In order to study the transportation efficiency of the aggregate device of coal cleaning machine, taking a certain type of coal cleaning machine as the research object, establishing the mechanical model of interaction between particles and blades, obtaining the relationship between force and speed.Based on the Archard wear theory and discrete element method, Coupling model of screw and coal was established by EDEM.Analyzing the transportation capacity of screw by controlling variable, the influence of coal particle size and rotating speed on the wear depth of screw was studied.The results show that increasing the rotating speed can improve the coal transportation efficiency in terms of speed control, but too fast speed will cause particles to fly out along the radial direction.The wear depth of the screw decreases first and then increases with speed changing in from 20 r/min to 65 r/min.When speed is increased from 35 r/min to 50 r/min, the efficiency improvement effect is the most significant.,the torque of screw is relatively small at the same time.In conclusion, the best range of screw speed is 35 r/min～ 50 r/min.In addition, the screw wear is serious when transporting large coal particles compared with small particles.Therefore, in practical application, the transportation of large coal should be carried out at low screw speed.Based on the above conclusion, in this paper, an optimal mathematical model is established to minimize the driving torque, obtaining the optimal structure and dynamic parameters of the screw, and proving the correctness of optimization process by simulation test.It provides a theoretical reference for improving the transportation efficiency of the coal cleaning machine, reducing the energy consumption of the equipment and prolonging the service life of the equipment.

Published
2022
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