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您的检索式：作者名="Luchao Han"
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| 1 | Climate Warming Mitigation from Nationally Determined Contributions显示文摘Individual countries are requested to submit nationally determined contributions(NDCs)to alleviate global warming in the Paris Agreement.However,the global climate effects and regional contributions are not explicitly considered in the countries’decision-making process.In this study,we evaluate the global temperature slowdown of the NDC scenario(ΔT=0.6°C)and attribute the global temperature slowdown to certain regions of the world with a compact earth system model.Considering reductions in CO_(2),CH_(4),N_(2)O,BC,and SO_(2),the R5OECD(the Organization for Economic Co-operation and Development in 1990)and R5ASIA(Asian countries)are the top two contributors to global warming mitigation,accounting for 39.3%and 36.8%,respectively.R5LAM(Latin America and the Caribbean)and R5MAF(the Middle East and Africa)followed behind,with contributions of 11.5%and 8.9%,respectively.The remaining 3.5%is attributed to R5REF(the Reforming Economies).Carbon Dioxide emission reduction is the decisive factor of regional contributions,but not the only one.Other greenhouse gases are also important,especially for R5MAF.The contribution of short-lived aerosols is small but significant,notably SO_(2)reduction in R5ASIA.We argue that additional species beyond CO_(2)need to be considered,including short-lived pollutants,when planning a route to mitigate climate change.It needs to be emphasized that there is still a gap to achieve the Paris Agreement 2-degree target with current NDC efforts,let alone the ambitious 1.5-degree target.All countries need to pursue stricter reduction policies for a more sustainable world. | Bo FU Jingyi LI Thomas GASSER Philippe CIAIS Shilong PIAO Shu TAO Guofeng SHEN Yuqin LAI Luchao HAN Bengang LI | 2022 | Advances in Atmospheric Sciences2022，39，8： | 1 |
| 2 | UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach显示文摘Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses. | Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，2： | 0 |
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