Measurement of the phase equilibria in the Al–Zr–Y system at 673 and 823 K

Liu, Xiaoxu; Du, Yong; Liu, Shuhong; Pan, Yafei

doi:10.1016/j.calphad.2019.101726

Cited by 7 publications

(16 citation statements)

References 21 publications

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“…In many MTC scenarios, the messages are transmitted in a cooperative manner from the senders to receivers or APs due to the resource constraints. Then, the routing path design or the relay selection affects both the network performance and power consumption [212], [213]. Different from the message transmission in wired networks, the path and relay selection of MTC scenarios needs to consider more issues, such as energy dynamics [213], [214], node mobility [204], [215], spectrum efficiency [212], [216], QoS [217], [218], and even the information security [219], [220].…”

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

“…Then, the routing path design or the relay selection affects both the network performance and power consumption [212], [213]. Different from the message transmission in wired networks, the path and relay selection of MTC scenarios needs to consider more issues, such as energy dynamics [213], [214], node mobility [204], [215], spectrum efficiency [212], [216], QoS [217], [218], and even the information security [219], [220]. The following paragraphs and Table III show the related research.…”

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

“…1) Routing: Liu et al study the routing problem in the wide area mesh IoT networks and consider the RL technique to address the limitations of conventional methods in terms of energy sensitivity [212]. In their proposal, the model-free RL method called temporal difference learning is adopted to populate and update the routing table.…”

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

“…In [223], authors adopt supervised learning-based MLP algorithm to improve the routing performance and energy efficiency for the IoT low power networks. Different from the other works [212], [221], [222] which utilize AI models to predict the next node directly, [223] aims at optimizing the value of transmission range of each node to improve the routing performance and minimizing energy consumption. In this paper, the authors first construct an IoT network to collect the labeled data including node positions and corresponding transmission range.…”

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

“…In this section, we analyze the AI-based research toward green MTCs. Compared with conventional methods, the ad-vantage of AI is that it can address the uncertainty and alleviate the failure ratio during the access and transmission process [185], [203], [212], [214], [222]. For energy harvesting process, AI enables more knowledge about future available power and battery status, which enables necessary configurations towards improved energy efficiency [17], [235].…”

Section: E Summarymentioning

confidence: 99%

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AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In current 5G and future 6G era, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, 6G will inevitably have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and even intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be adopted widely in 6G, further complicates the power control and network management. To address these challenges and reduce human intervene, Artificial Intelligence (AI) has been widely recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present the main considerations for green communications and survey the related research on AI-based green communications. We focus on how AI techniques are adopted to manage the network and improve energy harvesting toward the green era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning (DL) techniques can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and optimize the accuracy rate to accelerate the applications in 6G. Finally, we discuss the existing problems and envision the challenges for these emerging techniques in 6G.

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Section: Energy-efficient Transmissionsmentioning

confidence: 99%

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

Section: Energy-efficient Transmissionsmentioning

confidence: 99%

Section: E Summarymentioning

confidence: 99%

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AI based Service Management for 6G Green Communications

Mao,

Tang,

Yuichi

et al. 2021

Preprint

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Perspectives Toward Damage‐Tolerant Nanostructure Ceramics

Fan,

Wen,

Chen

et al. 2024

Advanced Science

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Advanced ceramic materials and devices call for better reliability and damage tolerance. In addition to their strong bonding nature, there are examples demonstrating superior mechanical properties of nanostructure ceramics, such as damage‐tolerant ceramic aerogels that can withstand high deformation without cracking and local plasticity in dense nanocrystalline ceramics. The recent progresses shall be reviewed in this perspective article. Three topics including highly elastic nano‐fibrous ceramic aerogels, load‐bearing nanoceramics with improved mechanical properties, and implementing machine learning‐assisted simulations toolbox in understanding the relationship among structure, deformation mechanisms, and microstructure‐properties shall be discussed. It is hoped that the perspectives present here can help the discovery, synthesis, and processing of future structural ceramic materials that are insensitive to processing flaws and local damages in service.

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