Results 91 to 100 of about 15,297,754 (301)

Technical indicator enhanced ultra‐short‐term wind power forecasting based on long short‐term memory network combined XGBoost algorithm

IET Renewable Power Generation
The growing integration of renewable energy sources into the power grid has introduced unprecedented uncertainty. Ensuring an appropriately scheduled reserve is essential to accommodate renewable energy's intermittent and volatile nature.
Yingying Zheng, Shijie Guan, Kailei Guo, Yongning Zhao, Lin Ye   +4 more
doaj   +1 more source

Temporal Interference Stimulation of Centromedian‐Parafascicular Complex in Disorders of Consciousness: A Pilot Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Treatment of disorders of consciousness (DoC) remains a major clinical challenge, and noninvasive, targeted modulation of deep brain structures has emerged as a promising therapeutic strategy. We aimed to evaluate the feasibility/safety and preliminary effects of thalamic temporal interference stimulation (TIS) targeting centromedian‐
Gengyao Hu, Changgeng Song, Dianwei Wu, Xiaoqian Niu, Jianmin Hao, Lijuan Liu, Xiaogang Kang, Wen Jiang   +7 more
wiley   +1 more source

Long short-term memory recurrent neural networks for signature verification

, 2003
>Magister Scientiae - MScHandwritten signature verification is defined as the classification process that strives to learn the manner in which an individual makes use of the muscular memory of their hands, fingers, and wrist to reproduce a signature.
Tiflin, C
core  

Diabetes‐Associated Cognitive Impairment: Pathophysiology and Emerging Therapies

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Diabetes‐associated cognitive impairment (DACI) is a common yet underrecognized condition, manifesting along a spectrum from mild cognitive impairment to dementia. DACI encompasses patients who develop vascular dementia (VaD) or Alzheimer's disease (AD) with diabetes as a risk factor; however, in certain patients, particularly those lacking ...
Osman Corbali, Priyanka Menon, Halil I. Corbali, Ram Jagannathan, Allan I. Levey   +4 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model [PDF]

Wind energy is an environment friendly, low-carbon, and cost-effective renewable energy source. It is, however, difficult to integrate wind energy into a mixed energy grid due to its high volatility and intermittency.
Deo, Ravinesh C., Casillas-Perez, David, Joseph, Lionel P., Prasad, Ramendra, Salcedo-sanz, Sancho, Raj, Nawin   +5 more
core   +1 more source

Modelling the relationship between visual short-term memory capacity and recall ability [PDF]

, 2008
Previous cognitive modelling work has suggested that the decline of short-term memory (STM) capacity is the dominant factor of age-related decline on recall ability. We report the results of a set of experiments investigating in further detail the effect
Peter C.R. Lane, Fernand Gobet, Gobet, F., Lane, PCR, Richard L. Smith, Lane, P.C.R., Smith, R.L., Smith, RL, Gobet, F   +8 more
core   +1 more source

Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid

IET Smart Grid
During the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids.
Ahsan Zafar, Yanbo Che, Muhammad Faheem, Muhammad Abubakar, Shujaat Ali, Muhammad Shoaib Bhutta   +5 more
doaj   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source