Search results for " method"
showing 10 items of 10455 documents
A Review of Kernel Methods in Remote Sensing Data Analysis
2011
Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastruc…
Classical Training Methods
2006
This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.
Multiple criteria assessment of methods for forecasting building thermal energy demand
2020
Abstract Nowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of s…
Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy
2022
AbstractIt is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper differ…
Semi-Supervised Support Vector Biophysical Parameter Estimation
2008
Two kernel-based methods for semi-supervised regression are presented. The methods rely on building a graph or hypergraph Laplacian with both the labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). The semi-supervised SVR methods are sucessfully tested in LAI estimation and ocean chlorophyll concentration prediction from remotely sensed images.
Neural network prediction in a system for optimizing simulations
2002
Neural networks have been widely used for both prediction and classification. Back-propagation is commonly used for training neural networks, although the limitations associated with this technique are well documented. Global search techniques such as simulated annealing, genetic algorithms and tabu search have also been used for this purpose. The developers of these training methods, however, have focused on accuracy rather than training speed in order to assess the merit of new proposals. While speed is not important in settings where training can be done off-line, the situation changes when the neural network must be trained and used on-line. This is the situation when a neural network i…
An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations
2019
A novel measurement approach for power-flow analysis in medium-voltage (MV) networks, based on load power measurements at low-voltage level in each secondary substation (SS) and only one voltage measurement at the MV level at primary substation busbars, was proposed by the authors in previous works. In this paper, the method is improved to cover the case of temporary unavailability of load power measurements in some SSs. In particular, a new load power estimation method based on artificial neural networks (ANNs) is proposed. The method uses historical data to train the ANNs and the real-time available measurements to obtain the load estimations. The load-flow algorithm is applied with the e…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Testing the shape-similarity hypothesis between partide-size distribution and water retention for sicilian soils
2012
Application of the Arya and Paris (AP) model to estimate the soil water retention curve requires a detailed description of the particlesize distribution (PSD) but limited experimental PSD data are generally determined by the conventional sieve-hydrometer (SH) method. Detailed PSDs can be obtained by fitting a continuous model to SH data or performing measurements by the laser diffraction (LD) method. The AP model was applied to 40 Sicilian soils for which the PSD was measured by both the SH and LD methods. The scale factor was set equal to 1.38 (procedure AP1) or estimated by a logistical model with parameters gathered from literature (procedure AP2). For both SH and LD data, procedure AP2 …
Enzyme replacement therapy for mucopolysaccharidosis VI: evaluation of long-term pulmonary function in patients treated with recombinant human N-acet…
2010
Pulmonary function is impaired in untreated mucopolysaccharidosis type VI (MPS VI). Pulmonary function was studied in patients during long-term enzyme replacement therapy (ERT) with recombinant human arylsulfatase B (rhASB; rhN-acetylgalactosamine 4-sulfatase). Pulmonary function tests prior to and for up to 240 weeks of weekly infusions of rhASB at 1 mg/kg were completed in 56 patients during Phase 1/2, Phase 2, Phase 3 and Phase 3 Extension trials of rhASB and the Survey Study. Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and, in a subset of patients, maximum voluntary ventilation (MVV), were analyzed as absolute volume in liters. FEV1 and FVC showed little change f…