Réduction du retard de calcul dans la méthode OLA

- L'algorithme OLA (Overlapp and Add) est classiquement utilisé pour le filtrage adaptatif dans le domaine des fréquences. Cette méthode introduit cependant un retard, qui peut nuire à son utilisation dans certaines applications temps réel. Dans cet article, nous proposons de réorganiser le calcul de la Transformation de Fourier Rapide permettant de réduire ce retard. Nous indiquons aussi une solution pour paralléliser la succession de deux opérations Transformation de Fourier Rapide et Transformation inverse. L'évaluation du gain fourni par ces modifications nouvelles est aussi donnée

State of the art of machine learning models in energy systems: A systematic review

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability

System Interface for an Integrated Intelligent Safety System (ISS) for Vehicle Applications

This paper deals with the interface-relevant activity of a vehicle integrated intelligent safety system (ISS) that includes an airbag deployment decision system (ADDS) and a tire pressure monitoring system (TPMS). A program is developed in LabWindows/CVI, using C for prototype implementation. The prototype is primarily concerned with the interconnection between hardware objects such as a load cell, web camera, accelerometer, TPM tire module and receiver module, DAQ card, CPU card and a touch screen. Several safety subsystems, including image processing, weight sensing and crash detection systems, are integrated, and their outputs are combined to yield intelligent decisions regarding airbag deployment. The integrated safety system also monitors tire pressure and temperature. Testing and experimentation with this ISS suggests that the system is unique, robust, intelligent, and appropriate for in-vehicle applications

DepartmentofMeasurementandInstrumentEngineering,

Block-orientedsignalprocessingtechniqueshaveexcep-tionalroleduetotheavailabilityoffastalgorithms.How-ever,iflargerdatasegmentsaretobeevaluatedinreal-time, thedelaycausedbytheblock-orientedapproachisnotal-waystolerableespeciallyiftheresponsetimeofoureval-uatingsystemisalsospecied.Thiscanbeexceptionally criticalifthesignalprocessingisrelatedtofeedbackloops. Inthispaperblock-orientedsignalprocessingmethodsare combinedwithrecursiveones.Thiscombinationreduces thedelayproblemcausedbytheblock-orientedfastal-gorithmsandatthesametimekeepsthecomputational complexityonrelativelylowlevel.Possiblythemostorig-inalcomponentofthesuggestedsolutionistheextension ofgivensizesignaltransformer-bankchannels(e.g.DF