Zobrazeno 1 - 10
of 220
pro vyhledávání: '"Physics informed deep learning"'
Publikováno v:
EJNMMI Physics, Vol 11, Iss 1, Pp 1-24 (2024)
Abstract Background Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET s
Externí odkaz:
https://doaj.org/article/3b1fab1654bb4fa79d49ca88c63847c8
Publikováno v:
Journal of Intelligent and Connected Vehicles, Vol 7, Iss 2, Pp 138-150 (2024)
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the safety and efficiency of automated driving in highly interactive traffic environments. Numerous studies in this area have focused on physics-based approaches
Externí odkaz:
https://doaj.org/article/3be80af6f912430595462b9766f7e282
Autor:
Sajjad, Uzair a, b, ⁎, 1, Mehdi, Sadaf c, 1, Hussain, Imtiyaz a, 1, Rehman, Tauseef-ur d, Sultan, Muhammad e, Rashidi, Mohammad Mehdi f, ⁎⁎, Yan, Wei-Mon a, b, ⁎
Publikováno v:
In Alexandria Engineering Journal March 2025 116:112-128
Publikováno v:
In Transportation Research Part C February 2025 171
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 1, Pp 385-401 (2024)
DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed over the past decade. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the da
Externí odkaz:
https://doaj.org/article/650b7aed23bf443889941e1b4b483b5a
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 1246-1256 (2024)
Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics
Externí odkaz:
https://doaj.org/article/d43cf5e5fcc2477f90ab148ec640b92c
Autor:
Archie J. Huang, Shaurya Agarwal
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 279-293 (2023)
Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the systems governed by physical laws in terms of partial differential equations (PDEs). However, empirical evidence points to the
Externí odkaz:
https://doaj.org/article/83f8f571d2e240e0b827af7415738eda
Autor:
Jun ZHANG, Peidong XU, Siyuan CHEN, Tianlu GAO, Yuxin DAI, Ke ZHANG, Hang ZHAO, Jiemai GAO, Yuyang BAI, Jinxing LI, Haoran ZHANG, Xiang LI, Jiuxiang CHEN
Publikováno v:
智能科学与技术学报, Vol 4, Pp 571-583 (2022)
The core theories, methods and technologies of contemporary system cognition, management, and control have been transferred to big data and artificial intelligence technology, resulting in a gap between the limitations of current artificial intellige
Externí odkaz:
https://doaj.org/article/972ab8864c0e43fa804fe7fa81ad4f87
Publikováno v:
Chinese Journal of Mechanical Engineering, Vol 35, Iss 1, Pp 1-14 (2022)
Abstract Electric cable shovel (ECS) is a complex production equipment, which is widely utilized in open-pit mines. Rational valuations of load is the foundation for the development of intelligent or unmanned ECS, since it directly influences the pla
Externí odkaz:
https://doaj.org/article/ee772e7fe34947deb26988fa2d5e9456
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