Results 81 to 90 of about 152,215 (282)

Nonstationary Continuous Time Markov Decision Processes with Discounted Criterion

Journal of Mathematical Analysis and Applications, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Quality Performance Criterion Model for Distributed Automated Control Systems Based on Markov Processes for Smart Grid

Applied Sciences
This paper addresses the problem of decision-making support for the modernization of distributed automated control systems (ACS) in power engineering by proposing an integral quality criterion that combines similarity-driven Markov process modeling with ...
Waldemar Wojcik, Ainur Ormanbekova, Muratkali Jamanbayev, Maria Yukhymchuk, Vladyslav Lesko   +4 more
doaj   +1 more source

A Time-Varying Opportunistic Multiple Access for Delay-Sensitive Inference in Wireless Sensor Networks

IEEE Access, 2019
We consider distributed transmission scheduling for inference over multiple access channels (MAC) using a wireless sensor network (WSN). The sensors transmit their data simultaneously using common shaping waveforms through finite-state Markovian fading ...
Kobi Cohen, Dean Malachi
doaj   +1 more source

On gradual-impulse control of continuous-time Markov decision processes with multiplicative cost

, 2018
In this paper, we consider the gradual-impulse control problem of continuous-time Markov decision processes, where the system performance is measured by the expectation of the exponential utility of the total cost. We prove, under very general conditions
Guo, Xin, Kurushima, Aiko, Piunovskiy, Alexey, Zhang, Yi   +3 more
core  

Optimal Time-Abstract Schedulers for CTMDPs and Markov Games

, 2010
We study time-bounded reachability in continuous-time Markov decision processes for time-abstract scheduler classes. Such reachability problems play a paramount role in dependability analysis and the modelling of manufacturing and queueing systems ...
Rabe, Markus, Schewe, Sven
core   +2 more sources

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

Artificial intelligence based smart energy community management: A reinforcement learning approach

CSEE Journal of Power and Energy Systems, 2019
This paper presents a smart energy community management approach which is capable of implementing P2P trading and managing household energy storage systems. A smart residential community concept is proposed consisting of domestic users and a local energy
Suyang Zhou, Zijian Hu, Wei Gu, Meng Jiang, Xiao-Ping Zhang   +4 more
doaj   +1 more source

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

Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks

IEEE Transactions on Machine Learning in Communications and Networking
In this paper, we investigate federated learning (FL) efficiency improvement in practical edge computing systems, where edge workers have non-independent and identically distributed (non-IID) local data, as well as dynamic and heterogeneous computing and
Yangchen Li, Lingzhi Zhao, Tianle Wang, Lianghui Ding, Feng Yang   +4 more
doaj   +1 more source