The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model

Functional Analysis of the Transmembrane Domains of Presenilin 1: PARTICIPATION OF TRANSMEMBRANE DOMAINS 2 AND 6 IN THE FORMATION OF INITIAL SUBSTRATE-BINDING SITE OF γ-SECRETASE*

γ-Secretase is a multimeric membrane protein complex composed of presenilin (PS), nicastrin, Aph-1, and Pen-2, which mediates intramembrane proteolysis of a range of type I transmembrane proteins. We previously analyzed the functional roles of the N-terminal transmembrane domains (TMDs) 1–6 of PS1 in the assembly and proteolytic activity of the γ-secretase using a series of TMD-swap PS1 mutants. Here we applied the TMD-swap method to all the TMDs of PS1 for the structure-function analysis of the proteolytic mechanism of γ-secretase. We found that TMD2- or -6-swapped mutant PS1 failed to bind the helical peptide-based, substrate-mimic γ-secretase inhibitor. Cross-linking experiments revealed that both TMD2 and TMD6 of PS1 locate in proximity to the TMD9, the latter being implicated in the initial substrate binding. Taken together, our data suggest that TMD2 and the luminal side of TMD6 are involved in the formation of the initial substrate-binding site of the γ-secretase complex

Machine Learning and Neural Network for Maintenance Management

A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements provided by the sensor will determine if the panel is healthy, damaged or dirty. A thermographic camera has been used to check the temperature variations and validate the results by the sensor. The study shows that the amount of dirt influences the temperature on the surface and the energy generated. Similarly, faults in photovoltaic cells influence the temperature of the panel. The NDT system is less expensive than traditional thermographic sensors or cameras. Early detection of these problems, together with an optimal maintenance strategy, allows to reduce costs and increase the competitiveness of this renewable energy source