Browsing by Keyword "Principal component analysis"
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Item Bearing assessment tool for longitudinal bridge performance(2020-11-01) Garcia-Sanchez, David; Fernandez-Navamuel, Ana; Sánchez, Diego Zamora; Alvear, Daniel; Pardo, David; Tecnalia Research & Innovation; E&I SEGURAS Y RESILIENTESThis work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management.Item Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation: Combined PCA-based loss function for polyp segmentation(2020-08) Sánchez-Peralta, Luisa F.; Picón, Artzai; Antequera-Barroso, Juan Antonio; Ortega-Morán, Juan Francisco; Sánchez-Margallo, Francisco M.; Pagador, J. Blas; COMPUTER_VISIONColorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.