Zobrazeno 1 - 10
of 26 873
pro vyhledávání: '"Hybrid data"'
Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel prediction methods have limitations: classical model-based methods often str
Externí odkaz:
http://arxiv.org/abs/2411.11576
Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and runtime layer configurable DNN acc
Externí odkaz:
http://arxiv.org/abs/2409.04976
Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. Moreover, their computation accuracy and efficiency are heavily subject to unknown
Externí odkaz:
http://arxiv.org/abs/2407.19253
Vision Transformers (ViTs) have achieved significant advancement in computer vision tasks due to their powerful modeling capacity. However, their performance notably degrades when trained with insufficient data due to lack of inherent inductive biase
Externí odkaz:
http://arxiv.org/abs/2407.07516
Autor:
Chen, Lei, Liu, Shi, Wang, Chenxi, Ma, Haoran, Qiao, Yifan, Wang, Zhe, Wu, Chenggang, Lu, Youyou, Feng, Xiaobing, Cui, Huimin, Lu, Shan, Xu, Harry
With rapid advances in network hardware, far memory has gained a great deal of traction due to its ability to break the memory capacity wall. Existing far memory systems fall into one of two data paths: one that uses the kernel's paging system to tra
Externí odkaz:
http://arxiv.org/abs/2406.16005
Autor:
Li, Yang
Publikováno v:
Artificial Intelligence for Reliability and Maintainability of Energy Systems 2025 (ISBN: 9780443247224)
This chapter addresses the increasing vulnerability of coastal regions to typhoons and the consequent power outages, emphasizing the critical role of power transmission systems in disaster resilience. It introduces a framework for assessing and enhan
Externí odkaz:
http://arxiv.org/abs/2406.10298
Autor:
Min, Dehai, Hu, Nan, Jin, Rihui, Lin, Nuo, Chen, Jiaoyan, Chen, Yongrui, Li, Yu, Qi, Guilin, Li, Yun, Li, Nijun, Wang, Qianren
Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain specific data has attracted wide attention. However, domain data often exists in a hybrid format, including text and semi-structured tables, posing challenges for the sea
Externí odkaz:
http://arxiv.org/abs/2402.12869
Akademický článek
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Autor:
Zajac, Markus, Störl, Uta
Quantum computers promise polynomial or exponential speed-up in solving certain problems compared to classical computers. However, in practical use, there are currently a number of fundamental technical challenges. One of them concerns the loading of
Externí odkaz:
http://arxiv.org/abs/2403.07491
To enhance localization accuracy in urban environments, an innovative LiDAR-Visual-Inertial odometry, named HDA-LVIO, is proposed by employing hybrid data association. The proposed HDA_LVIO system can be divided into two subsystems: the LiDAR-Inertia
Externí odkaz:
http://arxiv.org/abs/2403.06590