Outmoded 发表于 2025-3-28 15:09:17
Elastodynamic Response of a Cracked Fiber-Reinforced Body to a Non-Uniform Transient Plane-Strain L manner through integral transforms, an analytic-function decoupling technique, asymptotics and convolutions. Our results provide the time variation of the crack-tip stress intensity factor. These results may serve to quantify the fracture resistance of fiber-reinforced composite materials.Compass 发表于 2025-3-28 20:01:31
http://reply.papertrans.cn/47/4606/460594/460594_42.png遗产 发表于 2025-3-29 00:48:44
http://reply.papertrans.cn/47/4606/460594/460594_43.pngBrocas-Area 发表于 2025-3-29 04:45:21
standalone component, providing a deep integration of Explainable AI (XAI) into the CBR cycle. Besides the CBR methods, the methodology was also conceptualized to make use of the currently popular machine learning methods, such as recurrent and convolutional neural networks (RNN, ConvNet) or generaMeditate 发表于 2025-3-29 10:54:35
http://reply.papertrans.cn/47/4606/460594/460594_45.pngfluoroscopy 发表于 2025-3-29 11:39:23
F. Ziegleron a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN) which learns an embedd拱形大桥 发表于 2025-3-29 17:37:32
http://reply.papertrans.cn/47/4606/460594/460594_47.pngCLOUT 发表于 2025-3-29 21:25:21
Claudio Pecorari of magnitude and was over 3,700 times faster than a comparable monolithic CBR system when retrieving from half a million cases. Microservices are cloud-native architectures and with the rapid increase in cloud-computing adoption, it is timely for the CBR community to have access to such a framework财产 发表于 2025-3-30 01:47:56
Hui-Hui Dai,Kelin Pan,Yibin Fuon a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN) which learns an embedd大猩猩 发表于 2025-3-30 04:14:00
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