Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Peifang Dong"'
Autor:
Shaohong Qu, Songli Hu, Ting Li, Chaomin Wu, Yuexiu Chen, Linqian Zhao, Lihang Zhu, Jianjun Wu, Zhifeng Tang, Peifang Dong, Fengjiang Zhang
Publikováno v:
Sensing and Bio-Sensing Research, Vol 46, Iss , Pp 100692- (2024)
Viscosity measurement is crucial in medical diagnostics, pharmaceuticals, and analytical chemistry, where samples are frequently in small volumes and measurements are supposed to be conducted in a short time with convenient approaches. In this study,
Externí odkaz:
https://doaj.org/article/ebd19bad038e4e5ea0a07c6a97328ce4
Publikováno v:
Journal of Robotics, Vol 2018 (2018)
Dynamic path planning of unknown environment has always been a challenge for mobile robots. In this paper, we apply double Q-network (DDQN) deep reinforcement learning proposed by DeepMind in 2016 to dynamic path planning of unknown environment. The
Externí odkaz:
https://doaj.org/article/0149ca0249c441b7b43234628e457079
Publikováno v:
Communications in Computer and Information Science ISBN: 9789811379857
ICCSIP (2)
ICCSIP (2)
Recently, artificial intelligence algorithms represented by reinforcement learning and deep learning have promoted the development of autonomous driving technology. For the shipping industry, research and development of maritime autonomous surface sh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83f2388138bba1b808f516e54816a548
https://doi.org/10.1007/978-981-13-7986-4_12
https://doi.org/10.1007/978-981-13-7986-4_12
Publikováno v:
Journal of Robotics, Vol 2018 (2018)
Dynamic path planning of unknown environment has always been a challenge for mobile robots. In this paper, we apply double Q-network (DDQN) deep reinforcement learning proposed by DeepMind in 2016 to dynamic path planning of unknown environment. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7bb5c552b01ae8709198855ed90f3387
https://doaj.org/article/0149ca0249c441b7b43234628e457079
https://doaj.org/article/0149ca0249c441b7b43234628e457079