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Service-Oriented Architecture for Human-Centric Information Fusion
Published in David L. Hall, Chee-Yee Chong, James Llinas, Martin Liggins, Distributed Data Fusion for Network-Centric Operations, 2013
Other OGC standards for SWE includeObservations and Measurements (O&M)Sensor Model Language (SensorML)Sensor Observation Service (SOS)Sensor Alert Service (SAS)Web Notification Services (WNS)
Geotechnical data standardization and management to support BIM for underground infrastructures and tunnels
Published in Daniele Peila, Giulia Viggiani, Tarcisio Celestino, Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art, 2020
M. Beaufils, S. Grellet, B. Le Hello, J. Lorentz, M. Beaudouin, J. Castro Moreno
Beside conceptual models, OGC introduces standard interfaces that enable to retrieve data and expose them on the web. Most famous standards from OGC is the Web Mapping Service (WMS) that enable to provide a map (image) representation from several GIS vector or raster data formats. Yet other OGC standards enable to expose data: Web Feature Service (WFS) to provide discrete geospatial objects, Web Coverage Service (WCS) to provide coverage, Sensor Observation Service (SOS) and Sensor Things API to provide observations and measurements.
Geotechnical data standardization and management to support BIM for underground infrastructures and tunnels
Published in Daniele Peila, Giulia Viggiani, Tarcisio Celestino, Tunnels and Underground Cities: Engineering and Innovation meet Archaeology, Architecture and Art, 2019
M. Beaufils, S. Grellet, B. Le Hello, J. Lorentz, M. Beaudouin, J. Castro Moreno
Beside conceptual models, OGC introduces standard interfaces that enable to retrieve data and expose them on the web. Most famous standards from OGC is the Web Mapping Service (WMS) that enable to provide a map (image) representation from several GIS vector or raster data formats. Yet other OGC standards enable to expose data: Web Feature Service (WFS) to provide discrete geospatial objects, Web Coverage Service (WCS) to provide coverage, Sensor Observation Service (SOS) and Sensor Things API to provide observations and measurements.
Kriging and dimension reduction techniques for delamination detection in composites using electrical resistance tomography
Published in Engineering Optimization, 2023
Paulina Díaz-Montiel, Luis Escalona-Galvis, Satchi Venkataraman
Principal components analysis (PCA) is a technique used for model reduction. PCA develops an orthogonal set of basic vectors from a system observation matrix R, where R () is the matrix of M observations (resistance measurements) from N different damage cases. These basis vectors are the eigenvectors of the covariance matrix of the system responses, namely . Any observation can be expressed as a linear combination of these eigenvectors, also referred as the principal components. The information contained in each principal component to describe the system of observations is proportional to their corresponding eigenvalue. In any physical system, it is possible to reduce the number of retained eigenvectors (principal components) to significantly less than N.
Strong convergence of two algorithms for the split feasibility problem in Banach spaces
Published in Optimization, 2018
Compressed sensing is a very active domain of research and applications, based on the fact that an N-sample signal x with exactly m non-zero components can be recovered from measurements as long as the number of measurements is smaller than the number of signal samples and at the same time much larger than the sparsity level of x. More specifically, compressed sensing can be formulated as inverting the equation system where is the data to be recovered, is the vector of noisy observations or measurements, and ϵ represents the noise, is a bounded linear observation operator, often ill-conditioned because it models a process with loss of information. A powerful approach for problem (14) consists in considering a solution x represented by a sparse expansion, that is, represented by a series expansion with respect to an orthonormal basis that has only a small number of large coefficients. A successful model for solving (14) is the following convex constraint minimization problem: for some nonnegative real number t (cf. [23]). It is readily seen that problem (15) is a particular case of the SFP where and , and thus can be solved by our method. In this case, is the projection onto the closed ball in .