Results 111 to 120 of about 12,894,975 (292)

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Stochastic energy management of DC photovoltaic microgrids using Markov decision process

Results in Engineering
The increasing reliance on renewable energy sources, particularly photovoltaic (PV) systems, in off-grid applications presents a critical challenge: managing energy supply under random load behavior and intermittent resource availability.
Mohamed Aatabe, Rachid Latif, Mohamed I. Mosaad, Shimaa A. Hussien   +3 more
doaj   +1 more source

On the Practical Art of State Definitions for Markov Decision Process Construction

IEEE Access, 2018
Many problems faced by decision makers today involve the management of large scale, complex systems that can be modeled as state-based control problems, specifically discrete Markov decision process (MDP). Typical examples include transportation systems,
William T. Scherer, Stephen Adams, Peter A. Beling   +2 more
doaj   +1 more source

Algebraic Markov Decision Processes

, 2005
In this paper, we provide an algebraic approach to Markov Decision Processes (MDPs), which allows a unified treatment of MDPs and includes many existing models (quantitative or qualitative) as particular cases. In algebraic MDPs, rewards are expressed in a semiring structure, uncertainty is represented by a decomposable plausibility measure valued on a
Perny, Patrice, Spanjaard, Olivier, Weng, Paul   +2 more
openaire   +2 more sources

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

Safe Reinforcement Learning for Arm Manipulation with Constrained Markov Decision Process

Robotics
In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible ...
Patrick Adjei, Norman Tasfi, Santiago Gomez-Rosero, Miriam A. M. Capretz   +3 more
doaj   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

Novel Data Transmission Schemes for Inter-WBAN Networks Using Markov Decision Process. [PDF]

Wirel Pers Commun, 2023
Roy S, Roy M, Chowdhury C.
europepmc   +1 more source

Toward Complex In‐Car Environment Human–Vehicle Interactions Through Smart Glasses and sEMG‐Based Gesture Recognition

Advanced Intelligent Systems, EarlyView.
This study proposes a novel weighted random forest multimodal fusion method that combines smart glasses and sEMG data for in‐vehicle gesture interaction. It realizes stable performance in dim, occluded, and other constrained scenarios, providing feasible solutions and laying a foundation for universal human–machine interaction.
Wenbo Zhang, Wenting Wei, Kuang Li, Chuyuan Bian, Junkai Xu, Jianing Qiu, Yuxi Lu, Bin He, Shuo Jiang   +8 more
wiley   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source