Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Experimental Investigation of Stretch Blow Molding, Part 1: Instrumentation in an Industrial Environment

In spite of intensive research, computational modeling of the injection stretch blow molding (ISBM) still cannot match the accuracy of other polymer processes such as injection molding. There is a lack of understanding of the interdependence among the machine parameters set up by the operators, process parameters, material behavior, and the resulting final thickness distribution and performance of the molded product. The work presented in this paper describes a set of instrumentation tools developed for investigation of the ISBM process in an industrial setting. Results are presented showing the pressure and air temperature evolution inside the mold, the stretch rod force and displacement history, and the moment of contact of the polymer with seven discrete locations on the mold.

Endothelial Progenitors as Tools to Study Vascular Disease

Endothelial progenitor cells (EPCs) have great clinical value because they can be used as diagnostic biomarkers and as a cellular therapy for promoting vascular repair of ischaemic tissues. However, EPCs also have an additional research value in vascular disease modelling to interrogate human disease mechanisms. The term EPC is used to describe a diverse variety of cells, and we have identified a specific EPC subtype called outgrowth endothelial cell (OEC) as the best candidate for vascular disease modelling because of its high-proliferative potential and unambiguous endothelial commitment. OECs are isolated from human blood and can be exposed to pathologic conditions (forward approach) or be isolated from patients (reverse approach) in order to study vascular human disease. The use of OECs for modelling vascular disease will contribute greatly to improving our understanding of endothelial pathogenesis, which will potentially lead to the discovery of novel therapeutic strategies for vascular diseases.