Results 141 to 150 of about 7,354 (203)

Multisource Heterogeneous Data Fusion-Based Process Monitoring of the Reheating Furnace in Steel Production. [PDF]

ACS Omega
Ban Y, Zhang Y, Wang X, Yang Y, Wu Z.
europepmc   +1 more source

Mine water inrush source discrimination model based on KPCA-ISSA-KELM. [PDF]

PLoS One
Wang W, Cui X, Qi Y, Xue K, Liang R, Sun Z, Tao H.   +6 more
europepmc   +1 more source

Research on the Bearing Remaining Useful Life Prediction Method Based on Optimized BiLSTM. [PDF]

Sensors (Basel)
Zou Y, Sun W, Xu T, Wang B.
europepmc   +1 more source

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm.

Heliyon
Ling J, Fu Z, Xue K.
europepmc   +1 more source

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Parallel PCA–KPCA for nonlinear process monitoring

Control Engineering Practice, 2018
Abstract Both linear and nonlinear relationships may exist among process variables, and monitoring a process with such complex relationships among variables is imperative. However, individual principal component analysis (PCA) or kernel PCA (KPCA) may not be able to characterize these complex relationships well.
Qingchao Jiang, Xuefeng Yan
exaly   +2 more sources

KPCA for semantic object extraction in images

Pattern Recognition, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dacheng Tao
exaly   +2 more sources

Online prediction model based on the SVD–KPCA method

ISA Transactions, 2013
This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). The proposed SVD-KPCA method uses the Singular Value Decomposition (SVD) technique to update the principal components.
Okba Taouali
exaly   +3 more sources

L1 norm based KPCA for novelty detection

Pattern Recognition, 2013
Novelty detection is a one class classification problem, and it builds up the model with only normal samples, based on which the novelty is detected. Though conventional KPCA is an effective method of building one class classification models, it is prone to being affected by the presence of outliers due to its inherent properties of L2 norm.
Huan-gang Wang
exaly   +2 more sources

Fall Detection Based on KPCA and 3D KPCA

2016 IEEE Trustcom/BigDataSE/ISPA, 2016
Falls in elderly remain a very important public health care issue. The wearable devices based on tri-axial accelerator proves to be an effective tool for fall detection in the recent years. In this paper, we propose an approach to distinguish falls and normal activities of daily living (ADL).
Hui Wang, Xiaohe Chen, Xinjian Chen, Peilin Zang, Yunqing Liu, Lirong Wang   +5 more
openaire   +1 more source

Face Hallucination Through KPCA

2009 2nd International Congress on Image and Signal Processing, 2009
This paper demonstrates how Kernel Principal Component Analysis (KPCA) can be used for face hallucination. Different with other KPCA-based methods, KPCA in this paper handles samples from two subspaces, namely the high- and low- resolution image spaces.
Yan Liang, Jian-Huang Lai, Yao-Xian Zou, Wei Zheng, Pong C. Yuen   +4 more
openaire   +1 more source