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829 results on '"Wang, Yaodong"'

3. 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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4. 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

47. 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

50. Three-Dimensional Simulation Research on Discharge Characteristics of Inductively Coupled Plasma

Author

Guo Xu, Li Yinghui, Qiu Chenlin, Chang Yipeng, Wang Yaodong

Subjects
inductively coupled plasma, fluid mechanics, discharge characteristics, finite element method, radar echo, stealth, aircraft, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Aiming at the discharge problem of inductively coupled plasma (ICP), a closed wave-transmitting cavi-ty configuration is designed, the fluid mechanics model of ICP is established, and the multi physical simulation software COMSOL is used for three-dimensional simulation research. The variation laws of typical parameters such as electron density, electron temperature and plasma potential with time in the discharge process are analyzed. On this basis, the power of radio frequency power supply, the air pressure in the cavity and other conditions are changed to obtain the parameter range and spatial gradient distribution of ICP. The results show that the discharge power mainly affects the numerical range of plasma parameters, and the change of air pressure has an effect on the numerical value and spatial distribution of plasma parameters. When the power of radio frequency power supply increases from 100 W to 300 W, the peak electron density increases from 2.54×1017 m-3 to 5.68×1017 m-3, and the electric potential induced in the heating zone and the corresponding electron temperature decrease slightly after the reaction is stable. Increasing the air pressure in the cavity within a certain range will significantly increase the electron density. The air pressure increases from 10 Pa to 30 Pa, and the electron density increases from 9.12×1017 m-3 to 3.62×1018 m-3. However, the uniformity of plasma parameter distribution will deteriorate if the air pressure is too high.

Published
2022
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