Results 141 to 150 of about 132,701 (326)

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Bottlenecks‐Breaking in Zinc‐Iodine Batteries Toward Practical Implementation: A Review and Perspective

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Aqueous zinc–iodine batteries (Zn–I2Bs) offer promise for grid storage due to safety and cost advantages yet face critical bottlenecks: severe self‐discharge (polyiodide shuttling and HER), limited energy density, sluggish kinetics, and zinc anode instability.
Jia‐Lin Yang, Jun‐Ming Cao, Dai‐Huo Liu, Xing‐Long Wu   +3 more
wiley   +1 more source

Two-Player Nonzero-Sum Stochastic Differential Games with Switching Controls

Mathematics
In this paper, a two-player nonzero-sum stochastic differential game problem is studied with both players using switching controls. A verification theorem associated with a set of variational inequalities is established as a sufficient criterion for Nash
Yongxin Liu, Hui Min
doaj   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

High‐Fidelity Simulation‐Driven Control Framework for Robust Grid Integration of Renewable Energy Systems

Energy Science &Engineering, EarlyView.
This work proposes a high‐fidelity, simulation‐driven control framework for robust grid integration of hybrid PV–wind systems using a modular, hierarchical multi‐loop architecture with adaptive decision logic. The framework coordinates power, DC‐link voltage, and grid currents under fast load and generation changes, enabling safe exploration of extreme
Wulfran Fendzi Mbasso, Ambe Harrison, Idriss Dagal, Pradeep Jangir, Zhe Liu, Aseel Smerat   +5 more
wiley   +1 more source

Motivation to Run in One-Day Cricket

Mathematics
This paper presents a novel approach to identify an optimal coefficient for evaluating a player’s batting average, strike rate, and bowling average, aimed at achieving an optimal team score through dynamic modeling using a path integral method ...
Paramahansa Pramanik, Alan M. Polansky
doaj   +1 more source

On infinite dimensional stochastic differential games

, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

Energy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan, Suresh Panchanathan, Chan Choon Kit, Elumalai Perumal Venkatesan, Nasim Hasan   +4 more
wiley   +1 more source

Dynamic evolutionary analysis of opinion leaders’ and netizens’ uncertain information dissemination behavior considering random interference

Frontiers in Physics
This paper investigates the decision-making behaviors of opinion leaders and netizens in the context of uncertain information dissemination with the aim of effectively managing online public opinion crises triggered by major sudden events.
Lin Ma, Bowen Li, Junyao Wang
doaj   +1 more source

Using Deep Learning Conditional Value‐at‐Risk Based Utility Function in Cryptocurrency Portfolio Optimisation

International Journal of Finance &Economics, EarlyView.
ABSTRACT One of the critical risks associated with cryptocurrency assets is the so‐called downside risk, or tail risk. Conditional Value‐at‐Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio.
Xinran Huang, Linzhi Tan, Haozhe Su, Jeremy Eng‐Tuck Cheah   +3 more
wiley   +1 more source