scholarly journals Improving K-Means by Outlier Removal

2005 ◽  
pp. 978-987 ◽  
Author(s):  
Ville Hautamäki ◽  
Svetlana Cherednichenko ◽  
Ismo Kärkkäinen ◽  
Tomi Kinnunen ◽  
Pasi Fränti
Keyword(s):  
Outlier Removal

A Novel Algorithm for Outlier Removal Based on Density

2010 ◽  
Vol 36 (2) ◽  
pp. 343-346 ◽  
Author(s):  
Yang WANG
Keyword(s):  
Outlier Removal ◽  
Novel Algorithm

Improvement of the adversarial optimization approach for outlier removal in image matching

2013 ◽  
Vol 32 (11) ◽  
pp. 3157-3160
Author(s):  
Zhen-hua XUE ◽  
Ping WANG ◽  
Chu-han ZHANG ◽  
Si-jia CAI
Keyword(s):  
Image Matching ◽  
Optimization Approach ◽  
Outlier Removal

Novel Pre-processing using Outlier Removal in Voice Conversion

2016 ◽  
Author(s):  
Sushant V. Rao ◽  
Nirmesh J Shah ◽  
Hemant A. Patil
Keyword(s):  
Voice Conversion ◽  
Outlier Removal

Compact CMOS circuit for outlier removal

1996 ◽  
Vol 32 (11) ◽  
pp. 991 ◽  
Author(s):  
D.M. Wilson
Keyword(s):  
Cmos Circuit ◽  
Outlier Removal

Iteratively reweighted L1-fitting for model-independent outlier removal and regularization in diffusion MRI

2016 ◽  
Author(s):  
Alexandra Tobisch ◽  
Tony Stocker ◽  
Samuel Groeschel ◽  
Thomas Schultz
Keyword(s):  
Diffusion Mri ◽  
Outlier Removal ◽  
Model Independent

Outlier removal method for the refinement of optical measured displacement field based on critical factor least squares and subdomain division

2021 ◽  
Author(s):  
Bo Wang ◽  
Chen Sun ◽  
Keming Zhang ◽  
Jubing Chen
Keyword(s):  
Least Squares ◽  
Displacement Field ◽  
Critical Factor ◽  
Least Square ◽  
Outlier Removal ◽  
Full Field ◽  
Removal Method ◽  
Strain Estimation ◽  
Data Points ◽  
Measured Displacement

Abstract As a representative type of outlier, the abnormal data in displacement measurement often inevitably occurred in full-field optical metrology and significantly affected the further evaluation, especially when calculating the strain field by differencing the displacement. In this study, an outlier removal method is proposed which can recognize and remove the abnormal data in optically measured displacement field. A iterative critical factor least squares algorithm (CFLS) is developed which distinguishes the distance between the data points and the least square plane to identify the outliers. A successive boundary point algorithm is proposed to divide the measurement domain to improve the applicability and effectiveness of the CFLS algorithm. The feasibility and precision of the proposed method are discussed in detail through simulations and experiments. Results show that the outliers are reliably recognized and the precision of the strain estimation is highly improved by using these methods.

Outlier removal method used in servo control system of simulation table

2006 ◽  
Author(s):  
Hai-Feng Liu ◽  
Hua Zhang ◽  
Xiao-Rong Shi
Keyword(s):  
Control System ◽  
Servo Control ◽  
Outlier Removal ◽  
Removal Method ◽  
Servo Control System

scholarly journals Smart & Green: An Internet-of-Things Framework for Smart Irrigation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 190 ◽  
Author(s):  
Nidia G. S. Campos ◽  
Atslands R. Rocha ◽  
Rubens Gondim ◽  
Ticiana L. Coelho da Silva ◽  
Danielo G. Gomes
Keyword(s):  
Soil Moisture ◽  
Irrigation Management ◽  
Management Plan ◽  
Matric Potential ◽  
Outlier Removal ◽  
Temporal Window ◽  
Soil Moisture Sensors ◽  
Management Scheme ◽  
Smart Agriculture ◽  
Different Sources

Irrigation is one of the most water-intensive agricultural activities in the world, which has been increasing over time. Choosing an optimal irrigation management plan depends on having available data in the monitoring field. A smart agriculture system gathers data from several sources; however, the data are not guaranteed to be free of discrepant values (i.e., outliers), which can damage the precision of irrigation management. Furthermore, data from different sources must fit into the same temporal window required for irrigation management and the data preprocessing must be dynamic and automatic to benefit users of the irrigation management plan. In this paper, we propose the Smart&Green framework to offer services for smart irrigation, such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture. Outlier removal techniques allow for more precise irrigation management. For fields without soil moisture sensors, the prediction model estimates the matric potential using weather, crop, and irrigation information. We apply the predicted matric potential approach to the Van Genutchen model to determine the moisture used in an irrigation management scheme. We can save, on average, between 56.4% and 90% of the irrigation water needed by applying the Zscore, MZscore and Chauvenet outlier removal techniques to the predicted data.

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