Zobrazeno 1 - 10
of 592
pro vyhledávání: '"XIA Xiaoyu"'
Publikováno v:
Journal of Aeronautical Materials, Vol 44, Iss 1, Pp 143-151 (2024)
The microstructure，tensile and damage tolerance properties in different directions of Al-Mg-Sc-Zr alloy fabricated through selective laser melting（SLM）have been investigated. The results show that the YZ plane is a bimodal grain morphology comp
Externí odkaz:
https://doaj.org/article/9473b7039cc64544a661377cf9364fd8
The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. Removing data from the dataset alone is inadequate, as machine learning models can memorize information fr
Externí odkaz:
http://arxiv.org/abs/2410.10128
Autor:
YANG Yi, DANG Yuanyuan, XIA Xiaoyu, XU Long, CHEN Xueling, GENG Xiaoli, LIU Weiming, HE Jianghong
Publikováno v:
Di-san junyi daxue xuebao, Vol 43, Iss 15, Pp 1430-1436 (2021)
Objective To study the changes of pituitary-related hormones in patients with prolonged disorders of consciousness (pDOC) and its influence on the prognosis of patient in order to explore the risk factor of hormone levels with consciousness level and
Externí odkaz:
https://doaj.org/article/63b9f6be24924cd18348855566d3eb7d
The high-performance generative artificial intelligence (GAI) represents the latest evolution of computational intelligence, while the blessing of future 6G networks also makes edge intelligence (EI) full of development potential. The inevitable enco
Externí odkaz:
http://arxiv.org/abs/2401.02668
Autor:
Qi, Houyi, Liwang, Minghui, Hosseinalipour, Seyyedali, Xia, Xiaoyu, Cheng, Zhipeng, Wang, Xianbin, Jiao, Zhenzhen
Publikováno v:
IEEE Transactions on Services Computing, 2023
By opportunistically engaging mobile users (workers), mobile crowdsensing (MCS) networks have emerged as important approach to facilitate sharing of sensed/gathered data of heterogeneous mobile devices. To assign tasks among workers and ensure low ov
Externí odkaz:
http://arxiv.org/abs/2306.14156
Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned
Externí odkaz:
http://arxiv.org/abs/2210.06679
Federated learning (FL) represents a promising distributed machine learning paradigm that allows smart devices to collaboratively train a shared model via providing local data sets. However, problems considering multiple co-existing FL services and d
Externí odkaz:
http://arxiv.org/abs/2206.05885
The ever-growing concerns regarding data privacy have led to a paradigm shift in machine learning (ML) architectures from centralized to distributed approaches, giving rise to federated learning (FL) and split learning (SL) as the two predominant pri
Externí odkaz:
http://arxiv.org/abs/2206.01906
Publikováno v:
In International Journal of Fatigue January 2025 190
Distributed data processing frameworks (e.g., Hadoop, Spark, and Flink) are widely used to distribute data among computing nodes of a cloud. Recently, there have been increasing efforts aimed at evaluating the performance of distributed data processi
Externí odkaz:
http://arxiv.org/abs/2201.01948