Results 51 to 60 of about 13,256,052 (164)

DuMES: Deep Reinforcement Learning‐Based EV Charging Scheduling With Dual‐Layer Safety Modules

IET Smart Energy Systems, Volume 1, Issue 3, Page 232-246, December 2025.
We propose DuMES, an adaptive DRL framework with safety layers and reward shaping, ensuring safe EV charging optimisation, grid stability and cost reduction. Tested on a renewable‐powered charging station, DuMES effectively meets user demands and outperforms baseline methods.
Ao Zhang, Cong Liu, Konstantinos Makantasis, Xiaomin Chen, Tomas Ward, Long Cheng   +5 more
wiley   +1 more source

Deep Reinforcement Learning-Based Scheduling for an Electric–Hydrogen Integrated Station Using a Data-Driven Electrolyzer Model

Applied Sciences
To address the inaccurate scheduling of electric–hydrogen integrated stations (EHISs) caused by the limited accuracy of conventional mechanistic models for proton exchange membrane (PEM) electrolyzers, this study proposes a deep reinforcement learning ...
Dongdong Li, Liang Liu, Haiyu Liao
doaj   +1 more source

Classifications and Analysis of Caching Strategies in Information‐Centric Networking for Modern Communication Systems

Engineering Reports, Volume 7, Issue 2, February 2025.
The graphical abstract illustrates an in‐depth discussion of ICN features, architectures, and naming schemes. It presents the categories of caching, followed by an examination of existing caching strategies, and explores the role of caching in diverse applications, including IoT, IoV, mobility, edge computing, and fog computing.
Firdous Qaiser, Khoula Said Al Harthy, Mudassar Hussain, Jaroslav Frnda, Rashid Amin, Rahma Gantassi, Muhammad D. Zakaria   +6 more
wiley   +1 more source

Vehicle Lateral Control Based on Augmented Lagrangian DDPG Algorithm

Applied Sciences
This paper studies the safe trajectory tracking control of intelligent vehicles, which is still an open and challenging problem. A deep reinforcement learning algorithm based on augmented Lagrangian safety constraints is proposed to the lateral control ...
Zhi Li, Meng Wang, Haitao Zhao
doaj   +1 more source

A Review of Machine‐Learning‐Based Control Methods for Permanent Magnet Synchronous Machines

IET Electric Power Applications, Volume 19, Issue 1, January/December 2025.
This article provides a comprehensive review and comparative analysis of machine learning (ML)‐based control methods for PMSM drives, focusing on reinforcement learning (RL) and supervised ML approaches. Key contributions include a detailed exploration of RL‐based control methods, covering both conventional and robust RL strategies for current control,
Nabil Farah, Gang Lei, Gerd Bramerdorfer, Jianguo Zhu   +3 more
wiley   +1 more source

Distributionally robust chance-constrained Markov decision processes

, 2022
Markov decision process (MDP) is a decision making framework where a decision maker is interested in maximizing the expected discounted value of a stream of rewards received at future stages at various states which are visited according to a controlled ...
Singh, Vikas Vikram, Nguyen, Hoang Nam, Lisser, Abdel   +2 more
core   +2 more sources

Learn to Solve Constrained Routing Problems in V2V Social Vehicle Networks

IEEE Open Journal of the Communications Society
In vehicle social networks (VSN), multi-hop vehicle-to-vehicle (V2V) routing must minimize end-to-end delay while satisfying a social trust budget. This trust-constrained routing can be formulated as an NP-hard resource-constrained shortest path problem (
Wei He, Junliang Ye, Lijun Wang, Xiaohu Ge, Tao Han   +4 more
doaj   +1 more source

Machine learning approach of multi‐RAT selection for travelling users in 5G NSA networks

IET Networks, Volume 14, Issue 1, January/December 2025.
The authors present RAT selection algorithm for efficient resource allocation for public bus travelling user(s), utilising live data from multiple 5G NSA base stations. Models of the supervised machine learning algorithms: SVM, DNN and XGBoost were deployed for implementation to predict or select the appropriate RAT between 4G and 5G RATs.
Nurudeen O. Salau, Sanaullah Manzoor, Muhammad Z. Shakir   +2 more
wiley   +1 more source

Dealing with non-stationarity in Constrained Markov Decision Processes [PDF]

LAUREA MAGISTRALENei Processi Decisionali di Markov Vincolati (Constrained Markov Decision Processes o CMDP) con ricompense e vincoli avversari, un noto risultato di impossibilità impedisce a qualsiasi algoritmo di ottenere sia un regret sublineare che ...
Lunghi, Anna
core  

Robot introspection through learned hidden Markov models [PDF]

, 2006
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability.
Maria Fox, Malik Ghallab, Fox, Maria, Infantes, Guillaume, Ghallab, M., Long, D., Ghallab, Malik, Fox, M., Guillaume Infantes, Infantes, G., Long, Derek, Derek Long   +11 more
core   +2 more sources