Results 121 to 130 of about 39,843 (280)

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

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

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

Material ESG Performance and Bid Premium in Merger and Acquisition Deals

International Journal of Finance &Economics, EarlyView.
ABSTRACT This study examines the firm‐level and country‐level environmental, social, and governance (ESG) performance on bid premiums in cross‐border mergers and acquisitions (M&A) transactions. We document considerable variations in bid premiums. Higher carbon emissions are associated with higher bid premiums, suggesting that acquirers may perceive ...
Ndubuisi Ezenwa, Ibrahim Ayoade Adekunle, Robin Maialeh   +2 more
wiley   +1 more source

Interplay Between Green Investment and Market Price Premia in Global Shipping

International Journal of Finance &Economics, EarlyView.
ABSTRACT Existing research emphasises that the driver of green investment is its future profitability. This paper shows that other investors' decisions also influence green investment. We take the example of scrubber installation in shipping, which is optional by regulation but has an established market for trading its underlying asset.
Yao Shi, Nikos C. Papapostolou, Malvina Marchese, Ioannis C. Moutzouris, Angelos Efstathiou   +4 more
wiley   +1 more source

Beyond the Metrics: Context‐Aware Calibration for Better Drain Flow Modelling

Irrigation and Drainage, EarlyView.
ABSTRACT Robust hydrological and water quality modelling is essential for advancing the understanding and management of engineered agricultural subsurface drainage systems. Achieving credible model output hinges on calibration strategies that are both rigorous and context sensitive. At the field scale, accurately capturing the dynamics of nutrient load
R. Caleb Bruhn, Anamelechi Falasy, Richard A. C. Cooke, Rabin Bhattarai   +3 more
wiley   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao, Qintao Guo, Jin Zhou, Cheng Yi   +3 more
wiley   +1 more source