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Multi-Objective-Optimization Multi-AUV Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Learning

Autor: Ding, Yimian, Wang, Xinqi, Xu, Jingzehua, Xie, Guanwen, Liu, Weiyi, Li, Yi

The Internet of Underwater Things (IoUT) offers significant potential for ocean exploration but encounters challenges due to dynamic underwater environments and severe signal attenuation. Current methods relying on Autonomous Underwater Vehicles (AUV

Externí odkaz: http://arxiv.org/abs/2410.11282

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EFILN: The Electric Field Inversion-Localization Network for High-Precision Underwater Positioning

Autor: Ding, Yimian, Xu, Jingzehua, Xie, Guanwen, Wang, Haoyu, Liu, Weiyi, Li, Yi

Accurate underwater target localization is essential for underwater exploration. To improve accuracy and efficiency in complex underwater environments, we propose the Electric Field Inversion-Localization Network (EFILN), a deep feedforward neural ne

Externí odkaz: http://arxiv.org/abs/2410.11223

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Akademický článek

An Approximate Thermoelastic Theoretical Solution of Functional Graded Thick-Walled Tube Based on the Mori-Tanaka Method

Autor: LIU Weiyi, LI Zhiqiang, XIN Libiao

Publikováno v: Taiyuan Ligong Daxue xuebao, Vol 55, Iss 4, Pp 591-602 (2024)

Purposes There is a problem of not considering the microstructure influence of component materials when analyzing the thermoelastic problem of functionally graded thick-walled tubes at present. Evaluating the equivalent parameters of functionally gra

Externí odkaz: https://doaj.org/article/47401f0b03eb4b98b5bbdea4dc0984c3