Zobrazeno 1 - 10
of 2 919
pro vyhledávání: '"Deep reinforcement learning (DRL)"'
Autor:
Ali RIZEHVANDI, Shahram AZADI
Publikováno v:
Scientific Journal of Silesian University of Technology. Series Transport, Vol 125, Pp 245-259 (2024)
The addition of Adaptive Cruise Control (ACC) to vehicles enables automatic speed adjustments based on traffic conditions after the driver sets the maximum speed, freeing them to concentrate on steering. This study is dedicated to the development of
Externí odkaz:
https://doaj.org/article/cd96d976a2ba4708a50b5e23aded1743
Autor:
Abdul Wahid, Syed Zain Ul Abideen, Manzoor Ahmed, Wali Ullah Khan, Muhammad Sheraz, Teong Chee Chuah, Ying Loong Lee
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 9, Pp 102215- (2024)
The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces
Externí odkaz:
https://doaj.org/article/3587242dff3f4c97a15bdfcef414848f
Publikováno v:
Nuclear Engineering and Technology, Vol 56, Iss 4, Pp 1472-1479 (2024)
Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operati
Externí odkaz:
https://doaj.org/article/1d78c027eff448e7bdd707a53510534d
Autor:
Hadar Szostak, Kobi Cohen
Publikováno v:
IEEE Access, Vol 12, Pp 130444-130459 (2024)
We consider a decentralized formulation of the active hypothesis testing (AHT) problem, where multiple agents gather noisy observations from the environment with the purpose of identifying the correct hypothesis. At each time step, agents have the op
Externí odkaz:
https://doaj.org/article/76aab702ac594c4aa50e29d07d1b7374
Autor:
Oluwatosin Ahmed Amodu, Chedia Jarray, Raja Azlina Raja Mahmood, Huda Althumali, Umar Ali Bukar, Rosdiadee Nordin, Nor Fadzilah Abdullah, Nguyen Cong Luong
Publikováno v:
IEEE Access, Vol 12, Pp 108000-108040 (2024)
Deep reinforcement learning (DRL) has emerged as a promising technique for optimizing the deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) and Internet of Things (IoT) applications. With DRL, UAV tr
Externí odkaz:
https://doaj.org/article/8ba0e34121ee415681c73b8c1d29ee30
Publikováno v:
IEEE Access, Vol 12, Pp 103764-103788 (2024)
Data-intensive applications coupled with limited mobile resources make opportunistic computation offloading imperative. Therefore, efficient and reliable offloading strategies are crucial for achieving optimal performance in terms of stabilizing data
Externí odkaz:
https://doaj.org/article/f85d34d9fe054a849949e66a6ed425a3
Publikováno v:
IEEE Access, Vol 12, Pp 83994-84004 (2024)
In the midst of a global health crisis, it is of utmost importance for healthcare technologies to possess the capability to regulate and monitor the physiological variables of patients remotely and automatically. The effective control of mean arteria
Externí odkaz:
https://doaj.org/article/d5dd151383874f0c88d0de055d70c6f8
Publikováno v:
IEEE Access, Vol 12, Pp 74558-74571 (2024)
Workload orchestration at the edge of the network has become increasingly challenging with the ever-increasing penetration of resource demanding mobile, and heterogeneous devices offering low latency services. Literature has addressed this challenge
Externí odkaz:
https://doaj.org/article/5c4e23e499f34f3b9fc2e54707ab6f97
Autor:
Muddsair Sharif, Dieter Uckelmann
Publikováno v:
IEEE Access, Vol 12, Pp 54049-54065 (2024)
The demand for personalized learning experiences and effective analytics in education has significantly increased. The integration of technology in education has brought about significant changes in teaching and learning practices. In the era of digi
Externí odkaz:
https://doaj.org/article/02eaa4fcccf246dc8698382220f2efd9
Autor:
Xuanchen Xiang, Ruisheng Diao, Shonda Bernadin, Simon Y. Foo, Fangyuan Sun, Ayodeji S. Ogundana
Publikováno v:
IEEE Access, Vol 12, Pp 44080-44090 (2024)
Precise modeling of power systems is vital to ensure stability, reliability, and secure operations. In power industrial settings, model parameters can become skewed over time due to prolonged device usage or modifications made to the control systems.
Externí odkaz:
https://doaj.org/article/7f6e8b9bca9e4b0b948888b5c3f4b07d