Results 81 to 90 of about 7,354 (203)

A New Modeling Method for Meteorological Information of Regional Distributed Photovoltaic Power Generation Based on Multi‐Source Information Fusion

Energy Science &Engineering, Volume 13, Issue 8, Page 4211-4229, August 2025.
ABSTRACT With the increasingly significance of distributed photovoltaic (DPV) generation in modern energy structures, requirements for intelligent operation and accurate power forecasting have grown significantly. Precise meteorological information is the foundation for achieving these functions.
Yuhang Wang, Dengxuan Li, Wenwen Ma, Xi Zhang, Honglu Zhu   +4 more
wiley   +1 more source

Conventional KPCA Approach Applied to Detect Simulated Faults in PV Systems Using Simulated Data

International Journal of Photoenergy
Photovoltaic (PV) installations have become integral for harnessing solar energy, yet ensuring uninterrupted power generation remains crucial. This study addresses the challenge of maintaining reliability in PV systems by proposing a method to detect and
Charlène Bernadette Lema, Steve Perabi Ngoffe, Francelin Edgar Ndi, Grégoire Abessolo Ondoua, Salomé Ndjakomo Essiane   +4 more
doaj   +1 more source

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

Frontiers in Systems Neuroscience, 2012
This article explores various preprocessing tools that select/create features to help a learner produce a classifier that can use fMRI data to effectively discriminate Attention-Deficit Hyperactivity Disorder (ADHD) patients from healthy controls.
Gagan S Sidhu, Gagan S Sidhu, Nasimeh eAsgarian, Russell eGreiner, Matthew R G Brown   +4 more
doaj   +1 more source

Fault Detection for Aircraft Turbofan Engine Using a Modified Moving Window KPCA

IEEE Access, 2020
As a typical data-driven fault detection approach, the moving window kernel principal component analysis (MWKPCA) method has attracted attention for fault detection of turbofan engines considering the presence of component degradation, but the ...
Hao Sun, Yingqing Guo, Wanli Zhao
doaj   +1 more source

Gene- or region-based association study via kernel principal component analysis

BMC Genetics, 2011
Background In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits).
Zhao Jinghua, Yuan Zhongshang, He Yungang, Gao Qingsong, Zhang Bingbing, Xue Fuzhong   +5 more
doaj   +1 more source

Neutron/gamma (n/γ) discrimination method based on KPCA-MPA-ELM

He jishu
BackgroundNeutrons/Gamma (n/γ) discrimination is critical for neutron detection in the presence of γ radiation and traditional pulse shape discrimination methods suffer from unstable discrimination accuracy.PurposeThis study aims to implement a machine ...
HU Wanping, ZHANG Guiyu, ZHANG Yunlong, TUO Xianguo, LI Hulin   +4 more
doaj   +1 more source

Semi-Supervised Kernel PCA [PDF]

, 2010
We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points.
Christian Walder, Lars Kai Hansen, Mathematical Modelling, Morten Mørup, Ricardo Henao   +4 more
core   +2 more sources

A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition

Entropy, 2015
The classic principal components analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) feature extraction methods evaluate the importance of components according to their covariance contribution, not considering the entropy ...
Shunfang Wang, Ping Liu
doaj   +1 more source

Affinity Weighted Embedding [PDF]

, 2013
Supervised (linear) embedding models like Wsabie and PSI have proven successful at ranking, recommendation and annotation tasks. However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear ...
Weiss, Ron, Weston, Jason, Yee, Hector
core  

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

EURASIP Journal on Advances in Signal Processing, 2009
Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines.
Mathieu Fauvel, Jocelyn Chanussot, Jón Atli Benediktsson   +2 more
doaj   +1 more source