Search results for " Modelling"
showing 10 items of 1055 documents
High Frequency Model of PV Systems for the Evaluation of Ground Currents
2012
A high frequency model of a photovoltaic (PV) plant is developed and analysed to investigate the common mode (CM) currents circulating through the ground connections of the plant. The modelling method is based on the measurement of the impedance frequency response of photovoltaic module and on a high frequency representation of the power conversion unit. An overall lumped parameters circuit model is obtained and then implemented in PSpice. The CM leakage currents are evaluated by simulation.
Evaluation of ground currents in a PV system with high frequency modeling
2018
In this work a high frequency model of a photovoltaic (PV) plant is identified and implemented aiming to investigate the common mode (CM) currents circulating through the ground connections of the plant. From the identification of the impedance obtained by frequency response of photovoltaic module and by a suitable of the power conversion unit, an equivalent high frequency circuit representation has been developed. The lumped parameters circuit model is implemented in PSpice environment to obtain the CM leakage currents. Harmonics at frequencies multiple of the switching frequency and a strong resonance at about 12 MHz are detected.
A computational aeroelastic framework based on high-order structural models and high-fidelity aerodynamics
2023
A computational framework for high-fidelity static aeroelastic analysis is presented. Aeroelastic analysis traditionally employs a beam stick representation for the structure and potential, inviscid and irrotational flow assumptions for the aerodynamics. The unique contribution of this work is the introduction of a high-order structural formulation coupled with a high-fidelity method for the aerodynamics. In more details, the Carrera Unified Formulation coupled with the Finite Element Method is implemented to model geometrically complex composite, laminated structures as equivalent bi-dimensional plates. The open-source software SU2 is then used for the solution of the aerodynamic fields. T…
Using an Adaptive Network-based Fuzzy Inference System to Estimate the Vertical Force in Single Point Incremental Forming
2019
Manufacturing processes are usually complex ones, involving a significant number of parameters. Unconventional manufacturing processes, such as incremental forming is even more complex, and the establishment of some analytical relationships between parameters is difficult, largely due to the nonlinearities in the process. To overcome this drawback, artificial intelligence techniques were used to build empirical models from experimental data sets acquired from the manufacturing processes. The approach proposed in this work used an adaptive network-based fuzzy inference system to extract the value of technological force on Z-axis, which appears during incremental forming, considering a set of…
On the first- and second-order statistics of the capacity of N*Nakagami-m channels for applications in cooperative networks
2012
This article deals with the derivation and analysis of the statistical properties of the instantaneous channel capacitya of N*Nakagami-m channels, which has been recently introduced as a suitable stochastic model for multihop fading channels. We have derived exact analytical expressions for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades (ADF) of the instantaneous channel capacity of N*Nakagami-m channels. For large number of hops, we have studied the first-order statistics of the instantaneous channel capacity by assuming that the fading amplitude of the channel can approximately be modeled as a lognor…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…
ORGANIZED LEARNING MODELS (PURSUER CONTROL OPTIMISATION)
1983
Abstract The concept of Organized Learning is defined, and some random models are presented. For Not Transferable Learning, it is necessary to start from an instantaneous learning; by a discrete way, we must form a stochastic model considering the probability of each path; with a continue aproximation, we can study the evolution of the internal state through to consider the relative and absolute probabilities, by means of differential equations systems. For Transferable Learning, the instantaneous learning give us directly the System evolution. So, the Algoritmes for the different models are compared.
Combining Supervised and Unsupervised Learning to Discover Emotional Classes
2017
Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation t…
Predictive models for energy saving in Wireless Sensor Networks
2011
ICT devices nowadays cannot disregard optimizations toward energy sustainability. Wireless Sensor Networks, in particular, are a representative class of a technology where special care must be given to energy saving, due to the typical scarcity and non-renewability of their energy sources, in order to enhance network lifetime. In our work we propose a novel approach that aims to adaptively control the sampling rate of wireless sensor nodes using prediction models, so that environmental phenomena can be consistently modeled while reducing the required amount of transmissions; the approach is tested on data available from a public dataset.
English writing instruction in Norwegian upper secondary schools
2015
AbstractThis article presents a study of current English writing instruction practices in a selection of Norwegian upper secondary schools and discusses how this draws upon ideas within genre-pedagogy. The data comprises individual and focus-group interviews, observation reports and some teaching material. The study shows that English teachers focus on teaching genre requirements and adjustment of language to task and context. However, despite agreeing on the importance of teaching how to write specific text-types and to adjust to the situation at hand, there seems to be different opinions about how detailed instruction should be. Some teachers fear that too explicit instruction may hinder …