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361 results on '"Wu, Yanbo"'

12. The land of homesickness: The impact of homesteads on the social integration of rural migrants.

Author

Wu, Yanbo, Xu, Minghao, Li, Shichen, Wang, Hongwei, and Dong, Qi

Subjects
*SOCIAL integration, *SOCIAL impact, *HOMESICKNESS, *INHIBITION (Chemistry), *RURAL-urban relations, *RURAL poor, *CITY dwellers
Abstract

Promoting the social integration of rural migrants is key to improving the mechanism of rural-urban integration and development. This study utilizes the 2017 China Migrants Dynamic Survey and matched urban macro data to systematically explore the impact of homesteads on the social integration of rural migrants. Research has shown that social integration of rural migrants will be inhibited if they own homesteads. Simultaneously, the degree of inhibition varies according to the individual characteristics of rural migrants, the region to which they belong, and other factors. Specifically, when rural migrants aged 18 to 50 own homesteads, their degree of social integration into the cities they move into will be low. At the same time, for rural migrants in the central region, homestead ownership will not affect their degree of social integration. In addition, the mechanism analysis shows that increased housing expenditure inhibits rural migrants' willingness to integrate. Meanwhile, owning contracted land and owning a house in the city also affect the degree of social integration of rural migrants to a certain extent. The findings of this study can broaden research on the effects of land on the free movement of population factors. In the meantime, it provides theoretical references for improving the level of social integration of migrants, enhancing people's well-being, and improving the mechanism of urban-rural integration and development. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Joint equalization and decoding with per-survivor processing based on super-trellis in time-varying underwater acoustic channels.

Author

Kou, Xu, Wu, Yanbo, and Zhu, Min

Subjects
UNDERWATER acoustic communication, TRELLIS-coded modulation, INTERSYMBOL interference, QUADRATURE amplitude modulation, MAXIMUM likelihood statistics
Abstract

This Letter proposes a low-complexity joint equalization and decoding reception scheme based on super-trellis per-survivor processing, making it possible to apply maximum likelihood sequence estimation in high-order underwater acoustic communications under fast time-varying channels. The technique combines trellis-coded modulation states and intersymbol interference states and uses per-survivor processing to track channel parameters. Furthermore, a general trellis configuration for arbitrary order quadrature amplitude modulation signal is provided when truncate the channel is used to describe the intersymbol interference state to 1. Sea trials results show that the performance of proposed method can be more than 1.4 dB superiority than conventional schemes. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Synergistic evolution of resilience and efficiency in the real estate industry: evidence from 35 large and medium-sized cities in China.

Author

Wang, Linyan, Hu, Haiqing, Wang, Xianzhu, Zhang, Xincheng, and Wu, Yanbo

Subjects
REAL estate business, CITIES & towns, REAL estate development, REAL property
Abstract

This study measures the resilience and efficiency of the real estate industry in 35 large and medium-sized cities in China through the core variable method and the slacks-based measure of super-efficiency. The Haken model identifies the advantages of synergistic evolutionary states between systems and describes the synergistic evolution of resilience and efficiency in the real estate industry. The results show that (1) the evolution of the synergy in the real estate industry can be divided into three major cycles caused by the alternating effects of resilience and efficiency; (2) there are significant spatial and temporal differences in the evolution of the synergy between real estate resilience and efficiency, with the distribution of the synergy values being highly polarized; and (3) policy regulations tend to have short-term direct effects on the resilience of the real estate system, while for efficiency, the effects tend to be long-term and result in cross-cycle adjustments. This study enriches the literature on the robust and sustainable development of the real estate industry. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Two-Phase Layered Learning Recommendation via Category Structure

Author

Ji, Ke, Shen, Hong, Tian, Hui, Wu, Yanbo, Wu, Jun, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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37. A Reinforcement Learning-Based Pantograph Control Strategy for Improving Current Collection Quality in High-Speed Railways

Author

Wang, Hui, Han, Zhiwei, Liu, Wenqiang, and Wu, Yanbo

Abstract

In high-speed railways, the pantograph-catenary system (PCS) is a critical subsystem of the train power supply system. In particular, when the double-PCS (DPCS) is in operation, the passing of the leading pantograph (LP) causes the contact force of the trailing pantograph (TP) to fluctuate violently, affecting the power collection quality of the electric multiple units (EMUs). The actively controlled pantograph is the most promising technique for reducing the pantograph-catenary contact force (PCCF) fluctuation and improving the current collection quality. Based on the Nash equilibrium framework, this study proposes a multiagent reinforcement learning (MARL) algorithm for active pantograph control called cooperative proximity policy optimization (Coo-PPO). In the algorithm implementation, the heterogeneous agents play a unique role in a cooperative environment guided by the global value function. Then, a novel reward propagation channel is proposed to reveal implicit associations between agents. Furthermore, a curriculum learning approach is adopted to strike a balance between reward maximization and rational movement patterns. An existing MARL algorithm and a traditional control strategy are compared in the same scenario to validate the proposed control strategy’s performance. The experimental results show that the Coo-PPO algorithm obtains more rewards, significantly suppresses the fluctuation in PCCF (up to 41.55%), and dramatically decreases the TP’s offline rate (up to 10.77%). This study adopts MARL technology for the first time to address the coordinated control of double pantographs in DPCS.

Published
2024
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38. Rapid Adaptation for Active Pantograph Control in High-Speed Railway via Deep Meta Reinforcement Learning

Author

Wang, Hui, Liu, Zhigang, Han, Zhiwei, Wu, Yanbo, and Liu, Derong

Abstract

Active pantograph control is the most promising technique for reducing contact force (CF) fluctuation and improving the train’s current collection quality. Existing solutions, however, suffer from two significant limitations: 1) they are incapable of dealing with the various pantograph types, catenary line operating conditions, changing operating speeds, and contingencies well and 2) it is challenging to implement in practical systems due to the lack of rapid adaptability to a new pantograph-catenary system (PCS) operating conditions and environmental disturbances. In this work, we alleviate these problems by developing a revolutionary context-based deep meta-reinforcement learning (CB-DMRL) algorithm. The proposed CB-DMRL algorithm combines Bayesian optimization (BO) with deep reinforcement learning (DRL), allowing the general agent to adapt to new tasks quickly and efficiently. We evaluated the CB-DMRL algorithm’s performance on a proven PCS model. The experimental results demonstrate that meta-training DRL policies with latent space swiftly adapt to new operating conditions and unknown perturbations. The meta-agent adapts quickly after two iterations with a high reward, which require only ten spans, approximately equal to 0.5 km of PCS interaction data. Compared with state-of-the-art DRL algorithms and traditional solutions, the proposed method can promptly traverse scenario changes and reduce CF fluctuations, resulting in an excellent performance.

Published
2024
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49. A Framework for Distributed Managing Uncertain Data in RFID Traceability Networks

Author

Ma, Jiangang, Sheng, Quan Z., Ranasinghe, Damith, Chuah, Jen Min, Wu, Yanbo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, X. Sean, editor, Cruz, Isabel, editor, Delis, Alex, editor, and Huang, Guangyan, editor

Published
2012
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50. Modeling Sovereign RFID Data Streams in Collaborative Traceable Networks

Author

Wu, Yanbo, Sheng, Quan Z., Zeng, Rui, Ma, Jiangang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, X. Sean, editor, Cruz, Isabel, editor, Delis, Alex, editor, and Huang, Guangyan, editor

Published
2012
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