Results 61 to 70 of about 13,903 (231)

Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

Energy and AI
The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with ...
Pu He, Jun-Hong Chen, Chen-Zi Zhang, Zi-Yan Yu, Ming-Yang Wang, Jun-Yu Chen, Jia-Le Song, Yu-Tong Mu, Kun-Ying Gong, Wen-Quan Tao   +9 more
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

Hybrid Temporal Autoencoder and Similarity Matching for Low Aggregation Level Long Time Series Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Deep learning‐based long time series forecasting (LTSF) has achieved high accuracy by effectively capturing the underlying trends, seasonality, and temporal dependencies within time series data. However, at the individual entity level, termed the low aggregation level (LAL), intermittency, irregularity, and data sparsity undermine the ...
Hanbyeol Park, Sunghyun Sim, Kikun Park, Yongjae Lee, Eunhee Park, Hyerim Bae   +5 more
wiley   +1 more source

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source

Combined Prediction Energy Model at Software Architecture Level

IEEE Access, 2020
Accurate prediction of software energy consumption is of great significance for the sustainable development of the environment. In order to overcome the limitations of a single prediction method and further improve the prediction accuracy, a combined ...
Junke Li, Kai Liu, Mingjiang Li, Deguang Li   +3 more
doaj   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Adaptive Neural Sliding Mode Control of Active Power Filter

Journal of Applied Mathematics, 2013
A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode
Juntao Fei, Zhe Wang
doaj   +1 more source

Molecular Functions and Drug Prediction of MMP1 and TGFBR2 in Nasopharyngeal Carcinoma

Journal of Clinical Laboratory Analysis, EarlyView.
MMP1 and TGFBR2 were validated by machine learning and external datasets as the candidate genes. Dexamethasone showed strong binding affinity to MMP1 (−7.72 kcal/mol) and TGFBR2 (−7.27 kcal/mol) in docking studies. Knockdown of MMP1 repressed the malignant phenotypes of NPC cells.
Feng Wang, Binbin Ge, Ming Li, Xiaochun Xia, Fuming Zhang, Pengqin Xu   +5 more
wiley   +1 more source

High‐Fidelity Synthetic Raman Spectra Generation for Sinter Basicity Prediction Using β$$ \beta $$‐Variational Autoencoders

Journal of Raman Spectroscopy, EarlyView.
A β$$ \beta $$‐variational autoencoder with β$$ \beta $$ = 0.1 generates high‐fidelity synthetic Raman spectra of industrial sinter with a 16‐fold improvement in spectral fidelity over SMOTE‐based augmentation (KL divergence: 0.0075 vs. 0.121), enabling reliable basicity prediction (R2 = 0.83) from limited labeled datasets.
Marjorie Ariele Pereira, Daniel Cruz Cavalieri, Adilson Ribeiro Prado, Cassius Zanetti Resende, Erica Simões Rodrigues   +4 more
wiley   +1 more source

Bayesian Optimization of Grayscale Patterns for Layer‐Height Accuracy in Projection Multi‐Photon 3D Printing

Laser &Photonics Reviews, EarlyView.
Bayesian optimization combined with in situ quantitative phase imaging enables autonomous correction of layer‐height deviations in projection multi‐photon lithography. By jointly tuning model parameters and grayscale exposure settings, the method achieves more uniform and accurate layers within 300 prints, offering a fast, data‐efficient route to ...
Jason E. Johnson, Xianfan Xu
wiley   +1 more source