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853 results on '"Wang, Yaodong"'

5. Fully Automated UAV Cluster Pollution Source Detection Algorithm

Author

Wang, Yaodong, Wang, Tengyu, Liao, Lihong, Zhang, Tianxian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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6. Size-dependent atomic strain localization mechanism in Nb/amorphous CuNb nanolayered composites.

Author

Chen, Yangheng, Wang, Yaodong, and Li, Jianjun

Subjects
*MATERIAL plasticity, *MOLECULAR dynamics, *SHEAR strain, *COMPRESSIVE strength, *ATOMS
Abstract

Experiments have shown that crystalline–amorphous nanolayered composites show a strong size dependence in strength and plasticity. However, the underlying mechanism remains unknown. Here, the layer thickness (h)-dependent compressive strength and plastic deformation of Nb/amorphous CuNb nanolayered composites with h ranging from 2.8 to 20 nm have been studied by molecular dynamics simulations. It is found that the strength increases monotonically with the decrease of h, which can be well captured by the refined confined layer slip model. Furthermore, the shear banding-induced strain localization was analyzed in detail based on the evolution of the von Mises strain distribution with the applied strain and a strain localization parameter that represents the deviation of the specific atomic Mises strain from the average one of all atoms. The results show that the strain localization of the composite shows a significant size dependence and its magnitude increases with the decrease in the layer thickness. The prevention of shear band propagation in thicker layer samples is attributed to that the amorphous phase is capable of forming an atomic vortex to alleviate the strain concentration caused by dislocation absorption. The thicker amorphous layers accommodate enhanced homogeneous plasticity than the thinner ones by forming a larger vortex. Note that the above size dependence of strength and strain localization in the composite agrees well with existing experimental measurements and observations in the layer thickness range considered. As a result, the present work gives a deeper insight into the understanding of the size-dependent strengthening and strain localization mechanism in the amorphous/crystalline composites. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Reinforced Pedestrian Attribute Recognition with Group Optimization Reward

Author

Ji, Zhong, Hu, Zhenfei, Wang, Yaodong, and Li, Shengjia

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. Different from them, this paper addresses it as a decision-making task via a reinforcement learning framework. Specifically, PAR is formulated as a Markov decision process (MDP) by designing ingenious states, action space, reward function and state transition. To alleviate the inter-attribute imbalance problem, we apply an Attribute Grouping Strategy (AGS) by dividing all attributes into subgroups according to their region and category information. Then we employ an agent to recognize each group of attributes, which is trained with Deep Q-learning algorithm. We also propose a Group Optimization Reward (GOR) function to alleviate the intra-attribute imbalance problem. Experimental results on the three benchmark datasets of PETA, RAP and PA100K illustrate the effectiveness and competitiveness of the proposed approach and demonstrate that the application of reinforcement learning to PAR is a valuable research direction.

Published
2022

48. RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection

Author

Wang, Yang, Wang, Dong, Wang, Yaodong, and Zhang, You

Subjects
Quantitative Finance - Risk Management
Abstract

Portfolio selection is the central task for assets management, but it turns out to be very challenging. Methods based on pattern matching, particularly the CORN-K algorithm, have achieved promising performance on several stock markets. A key shortage of the existing pattern matching methods, however, is that the risk is largely ignored when optimizing portfolios, which may lead to unreliable profits, particularly in volatile markets. We present a risk-aversion CORN-K algorithm, RACORN-K, that penalizes risk when searching for optimal portfolios. Experiments on four datasets (DJIA, MSCI, SP500(N), HSI) demonstrate that the new algorithm can deliver notable and reliable improvements in terms of return, Sharp ratio and maximum drawdown, especially on volatile markets.

Published
2018

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