Zobrazeno 1 - 10
of 14 997
pro vyhledávání: '"Q learning"'
Publikováno v:
EURASIP Journal on Information Security, Vol 2024, Iss 1, Pp 1-20 (2024)
Abstract The Internet of Things (IoT) is now an essential component of our day-to-day lives. In any case, the association of various devices presents numerous security challenges in IoT. In some cases, ubiquitous data or traffic may be collected by c
Externí odkaz:
https://doaj.org/article/9bf7f6f45df64771808baa91a0d9cf3a
Autor:
Zhongyi Huang
Publikováno v:
Alexandria Engineering Journal, Vol 106, Iss , Pp 381-391 (2024)
Addressing the requirements and challenges of traffic light control, a reinforcement learning based adaptive optimal control model for traffic lights in intelligent transportation systems is proposed. In the model design, we combined Markov decision
Externí odkaz:
https://doaj.org/article/8cd36fe2df924ff8ad6faff699dc4f17
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-34 (2024)
Abstract To endow the prey with intelligent movement behavior and improve the performance of Golden Jackal Optimization (GJO), a Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm is proposed. This paper introduces five update mechanisms
Externí odkaz:
https://doaj.org/article/cb42bacd09ef44048b0aff7bb2010e16
Publikováno v:
Open Geosciences, Vol 16, Iss 1, Pp 36-9 (2024)
Clearly determining the magnitude of fracture pressure is a crucial indicator for fracturing design. Traditional methods for predicting fracture pressure suffer from challenges such as difficulties in obtaining required data, low prediction accuracy,
Externí odkaz:
https://doaj.org/article/a7a7b7097ff44fb3872fd3460b893c0a
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-29 (2024)
Abstract Predicting rock tunnel squeezing in underground projects is challenging due to its intricate and unpredictable nature. This study proposes an innovative approach to enhance the accuracy and reliability of tunnel squeezing prediction. The pro
Externí odkaz:
https://doaj.org/article/2f04156f290b4509966a95f76a65af17
Publikováno v:
Complex System Modeling and Simulation, Vol 4, Iss 3, Pp 223-235 (2024)
The lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan. In response, an improved dynamic Q-learning (IDQL) algorithm is propos
Externí odkaz:
https://doaj.org/article/a3cc85ac87714590b624bae307e68e36
Publikováno v:
Intelligent and Converged Networks, Vol 5, Iss 3, Pp 207-221 (2024)
Currently, edge Artificial Intelligence (AI) systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars, and supported diverse applications and services. This fundamental supports come from co
Externí odkaz:
https://doaj.org/article/8a75837a71394fd5b4cbb1de8a1646c1
Publikováno v:
ESPOCH Congresses, Vol 3, Iss 4, Pp 130-145 (2024)
Abstract Industry 4.0 has revolutionized the way industrial processes are managed, introducing concepts such as advanced automation, the Internet of Things (IoT), and machine learning into production and process management. This article presents the
Externí odkaz:
https://doaj.org/article/5e68b04cec534771b852b43b705955fa
Publikováno v:
Metrology, Vol 4, Iss 3, Pp 489-505 (2024)
As modern systems become more complex, their control strategy no longer relies only on measurement data from probes; it also requires information from mathematical models for non-measurable places. On the other hand, those mathematical models can lea
Externí odkaz:
https://doaj.org/article/a2e62ec8a78342b7b2e3d0e8561e7325
Autor:
Railkar Dipali, Joshi Shubhalaxmi
Publikováno v:
Cybernetics and Information Technologies, Vol 24, Iss 3, Pp 182-196 (2024)
Penetration Testing (PT), which mimics actual cyber attacks, has become an essential procedure for assessing the security posture of network infrastructures in recent years. Automated PT reduces human labor, increases scalability, and allows for more
Externí odkaz:
https://doaj.org/article/e355c361100f403d80d2b28255270e24