New Routes from Minimal Approximation Error to Principal Components

Miranda, Abhilash Alexander; Borgne, Yann‐Aël Le; Bontempi, Gianluca

doi:10.1007/s11063-007-9069-2

Cited by 104 publications

(64 citation statements)

References 17 publications

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“…types of oil wells were determined Fig.1 Flow chart of cluster analysis Principal component analysis and cluster analysis were the key parts of the whole process [13] . First, by linear combination, the original multiple indicatiors had become a few independent indicators that fully reflected the overall information, so as to make further analysis [14] . Second, the main factors that caused oil wells corrosion and scaling were determined by removing the dependent variable.…”

Section: Clustering Analysis Methodsmentioning

confidence: 99%

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Application of clustering analysis in oil wells corrosion and scaling management

Yan¹,

Xie²,

Du³

et al. 2017

Proceedings of the 2016 4th International Conference on Renewable Energy and Environmental Technology (ICREET 2016)

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This article demonstrates a method to make classification and dosing control for oil wells. To find the main factors that caused the corrosion and scaling in oil wells, water quality data of one oil production plant were made principal component analysis. Then the oil wells were divided into four types by using spss19.0 clustering analysis software according to the differences among main factors. Finally, wells dosing were applied in the field according to the classification. The effect is obvious, the corrosion inhibition rate can reach 70% above, and scale inhibition rate of calcium carbonate can reach 90% above. Costs savings of all the wells add up to 5390 thousands.

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Section: Clustering Analysis Methodsmentioning

confidence: 99%

“…Principal components were selected according to the order of contribution rate of different components, then the selected principal components were as weights for linear weighting [14] . They were sorted according to the score value [16] .…”

Section: Principal Component Analysis Of Water Quality Datamentioning

confidence: 99%

Application of clustering analysis in oil wells corrosion and scaling management

Yan¹,

Xie²,

Du³

et al. 2017

Proceedings of the 2016 4th International Conference on Renewable Energy and Environmental Technology (ICREET 2016)

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“…(1) are difficult to determine quantitatively. In this subsection we employ principal component analysis [6] to reconstruct the Eq. (1).…”

Section: Principal Component Analysis Using Covariance Methodsmentioning

confidence: 99%

Geographic Routing Algorithm with Location Errors

Jing

Wang

2012

IEICE Trans. Inf. & Syst.

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SUMMARYGeographic routing uses the geographical location information provided by nodes to make routing decisions. However, the nodes can not obtain accurate location information due to the effect of measurement error. A new routing strategy using maximum expected distance and angle (MEDA) algorithm is proposed to improve the performance and promote the successive transmission rate. We firstly introduce the expected distance and angle, and then we employ the principal component analysis to construct the object function for selecting the next hop node. We compare the proposed algorithm with maximum expectation within transmission range (MER) and greedy routing scheme (GRS) algorithms. Simulation results show that the proposed MEDA algorithm outperforms the MER and GRS algorithms with higher successive transmission rate.

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“…φ r $. This formulation is common in Principal Component Analysis [37,38,39] where it is introduced in the framework of variance maximization or minimal error of the approximation matrixX [40,41,42].…”

Section: Pod Decomposition Of Piv Image Recordingsmentioning

confidence: 99%

POD-based background removal for particle image velocimetry

Mendez

Raiola

Masullo

et al. 2017

Experimental Thermal and Fluid Science

126

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State-of-art preprocessing methods for Particle Image Velocimetry (PIV) are severely challenged by time-dependent light reflections and strongly non-uniform background. In this work, a novel image preprocessing method is proposed. The method is based on the Proper Orthogonal Decomposition (POD) of the image recording sequence and exploits the different spatial and temporal coherence of background and particles. After describing the theoretical framework, the method is tested on synthetic and experimental images, and compared with well-known pre-processing techniques in terms of image quality enhancement, improvements in the PIV interrogation and computational cost. The results show that, unlike existing techniques, the proposed method is robust in the presence of significant background noise intensity, gradients, and temporal oscillations. Moreover, the computational cost is one to two orders of magnitude lower than conventional image normalization methods. A downloadable version of the preprocessing toolbox has been made available at http://seis.bris.ac.uk/~aexrt/PIVPODPreprocessing/.

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New Routes from Minimal Approximation Error to Principal Components

Cited by 104 publications

References 17 publications

Application of clustering analysis in oil wells corrosion and scaling management

Application of clustering analysis in oil wells corrosion and scaling management

Geographic Routing Algorithm with Location Errors

POD-based background removal for particle image velocimetry

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