Local estimation of the global horizontal irradiance using an all-sky camera
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The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...
A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...
This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...
Self-sensing allows to use a Dielectric Elastomer Actuator (DEA) simultaneously as an actuator and sensor, without the need of external sensors. DEAs are composed of a dielectric elastomer that is sandwiched between two electrodes. If a voltage difference ...
Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...
The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, ...
Three-dimensional (3D) knee angle measurement is one of the key measures in human gait analysis. Inertial sensor capable of measuring joint motion under unconstrained conditions is a practical tool for clinical evaluation and rehabilitation. An inertial me ...
Purpose: To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data. Methods: Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k ...
We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...
We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...