Search results for "Linear"
showing 10 items of 7165 documents
Feedback linearization control of wind turbine equipped with doubly fed induction generator
2017
This paper focuses on several control techniques of a wind turbine of rated power of about 1 MW. In particular, a wind generator equipped with an asynchronous doubly-fed induction machine has been considered and its dynamic model in MATLAB/SIMULINK environment has been implemented. Starting from this model the feedback linearization control has been derived, and several simulations have been carried out, with the aim of compare its dynamic performances with the classical field oriented control, and with the V/f control. The results allow us to conclude that a DFIG controlled by a feedback linearization technique ensures better dynamic performance.
Designing an innovative system for sea wave utilization
2018
For the sea wave energy exploitation, the department of Energy of Palermo University is investigating a new approach based on the mechanical motion conversion from a linear motion into a unidirectional rotary motion. This mechanical output can be used to run alternators, producing electrical power with a very simplified energy conversion chain. First preliminary tests have been realized in laboratory using a small-scale prototype.
An Estimative Model of Automated Valuation Method in Italy
2017
The Automated Valuation Method (AVM) is a computer software program that analyzes data using an automated process. It is related to the process of appraising an universe of real estate properties, using common data and standard appraisal methodologies. Generally, the AVM is based on quantitative models (statistical, mathematical, econometric, etc.), related to the valuation of the properties gathered in homogeneous groups (by use and location) for which are collected samples of market data. The real estate data are collected regularly and systematically. Within the AVM, the proposed valuation scheme is an uniequational model to value properties in terms of widespread availability of sample …
Optimizing Kernel Ridge Regression for Remote Sensing Problems
2018
Kernel methods have been very successful in remote sensing problems because of their ability to deal with high dimensional non-linear data. However, they are computationally expensive to train when a large amount of samples are used. In this context, while the amount of available remote sensing data has constantly increased, the size of training sets in kernel methods is usually restricted to few thousand samples. In this work, we modified the kernel ridge regression (KRR) training procedure to deal with large scale datasets. In addition, the basis functions in the reproducing kernel Hilbert space are defined as parameters to be also optimized during the training process. This extends the n…
Masonry Compressive Strength Prediction Using Artificial Neural Networks
2019
The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of m…
Large-scale random features for kernel regression
2015
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical retrieval of bio-geo-physical parameters. The RKS method allows to approximate a kernel matrix with a set of random bases sampled from the Fourier domain. We extend their use to other bases, such as wavelets, stumps, and Walsh expansions. We show that kernel regression is now possible for data…
Revisitation of Nonorthogonal Spin Adaptation in Coupled Cluster Theory.
2015
The benefits of what is alternatively called a nonorthogonally spin-adapted, spin-free, or orbital representation of the coupled cluster equations is discussed relative to orthogonally spin-adapted, spin-orbital, and spin-integrated theories. In particular, specific linear combinations of the orbital cluster amplitudes, denoted spin-summed amplitudes, are shown to reduce the number of contractions that must be explicitly performed and to simplify the expressions and their derivation. The computational efficiency of the spin-summed approach is discussed and compared to orthogonally spin-adapted and spin-integrated approaches. The spin-summed approach is shown to have significant computationa…
An abstract inf-sup problem inspired by limit analysis in perfect plasticity and related applications
2021
This paper is concerned with an abstract inf-sup problem generated by a bilinear Lagrangian and convex constraints. We study the conditions that guarantee no gap between the inf-sup and related sup-inf problems. The key assumption introduced in the paper generalizes the well-known Babuška–Brezzi condition. It is based on an inf-sup condition defined for convex cones in function spaces. We also apply a regularization method convenient for solving the inf-sup problem and derive a computable majorant of the critical (inf-sup) value, which can be used in a posteriori error analysis of numerical results. Results obtained for the abstract problem are applied to continuum mechanics. In particular…
Influence Diagnostics for Meta-Analysis of Individual Patient Data Using Generalized Linear Mixed Models
2014
In meta-analysis, generalized linear mixed models (GLMMs) are usually used when heterogeneity is present and individual patient data (IPD) are available, while accepting binary, discrete as well as continuous response variables. In the present paper some measures of influence diagnostics based on log-likelihood are suggested and discussed. A known measure is approximated to get a simpler form, for which the information matrix is no more necessary. The performance of the proposed measure is assessed through a diagnostic analysis on simulated data reproducing a possible meta-analytical context of IPD with influential outliers. The proposed measure is showed to work well and to have a form sim…
Vibrational spectroscopy provides a green tool for multi-component analysis
2010
Abstract Based on the literature published in the past decade, we focus on the possibilities offered by vibrational-spectroscopy-based techniques to make multi-component analysis of samples independently of their physical state. We discuss the main chemometric tools proposed for developing calibration models and solving problems derived from spectroscopic non-idealities (e.g., highly overlapped spectral bands or the presence of spectral non-linearity), and the benefits provided by vibrational-spectroscopy-based multi-component analysis in industry. Our main objective is to show that vibrational spectroscopy provides fast analytical methods that enable non-destructive analysis and permits, i…