Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported

Lessening stress and anxiety-related behaviors by means of AI-driven drones for aromatherapy

Stress and anxiety are part of the human mental process which is often unavoidably yield by circumstances and situations such as waiting for a flight at the airport gate, hanging around before an exam,or while in an hospital waiting room. In this work we devise a decision system for a robotic aroma diffusion device designed to lessen stress and anxiety-related behaviors. The robot is intended as designed for deployments in closed environments that resembles the aspect and structure of a waiting room with different chairs where people sit and wait. The robot can be remotely driven by means of an artificial intelligence based on Radial Basis Function Neural Networks classifiers. The latter is responsible to recognize when stress or anxiety levels are arising so that the diffusion of specific aromas could relax the bystanders. We make use of thermal images to infer the level of stress by means of an ad hoc feature extraction approach. The system is prone to future improvements such as the refinement of the classification process also by means of ac-curate psychometric studies that could be based on standardized tests or derivatives

Cascade feed forward neural network-based model for air pollutants evaluation of single monitoring stations in urban areas

In this paper, air pollutants concentrations for N O2 , N O, N Ox and P M 10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy.

Toward adaptive heuristic video frames capturing and correction in real-time

Multimedia devices are widely used in professional applications as well as personal purposes. The use of computer vision systems enables detection and extraction of important features exposed in images. However constantly increasing demand for this type of video with high quality requires simple however reliable methods. The objective of presented research is to investigate applicability of heuristic method for real-time video frames capturing and correction

Functionalized Carbon Nanoparticle-Based Sensors for Chemical Warfare Agents

Real-time sensing of chemical warfare agents (CWAs) is, today, a crucial topic to prevent lethal effects of a chemical terroristic attack. For this reason, the development of efficient, selective, ..

Available bandwidth estimation in smart VPN bonding technique based on a NARX neural network

Today many applications require a high Quality of Service (QoS) to the network, especially for real time applications like VoIP services, video/audio conferences, video surveillance, high definition video transmission, etc. Besides, there are many application scenarios for which it is essential to guarantee high QoS in high speed mobility context using an Internet Mobile access. However, internet mobile networks are not designed to support the real-time data traffic due to many factors such as resource sharing, traffic congestion, radio link, coverage, etc., which affect the Quality of Experience (QoE). In order to improve the QoS in mobility scenarios, the authors propose a new technique named "Smart VPN Bonding" which is based on aggregation of two or more internet mobile accesses and is able to provide a higher end-to-end available bandwidth due to an adaptive load balancing algorithm. In this paper, in order to dynamically establish the correct load balancing weights of the smart VPN bonder, a neural network approach to predict the main Key Performance Indicators (KPIs) values in a determinate geographical point is proposed

A Multithread Nested Neural Network Architecture to Model Surface Plasmon Polaritons Propagation

Surface Plasmon Polaritons are collective oscillations of electrons occurring at the interface between a metal and a dielectric. The propagation phenomena in plasmonic nanostructures is not fully understood and the interdependence between propagation and metal thickness requires further investigation. We propose an ad-hoc neural network topology assisting the study of the said propagation when several parameters, such as wavelengths, propagation length and metal thickness are considered. This approach is novel and can be considered a first attempt at fully automating such a numerical computation. For the proposed neural network topology, an advanced training procedure has been devised in order to shun the possibility of accumulating errors. The provided results can be useful, e.g., to improve the efficiency of photocells, for photon harvesting, and for improving the accuracy of models for solid state devices

Metodologie ed algoritmi innovativi per la compressione intelligente ed il trattamento di segnali mono e multidimensionali

Dottorato di ricerca in ingegneria elettronica. 12. ciclo. A.a. 1998-1999. Coordinatore Vincenzo Coccorese. Tutore Salvatore CocoConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7 , Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

A New Approach to Heart Sounds Biometric Recognition Based on Gram-PNN

In this paper we introduce a new approach to heart sounds biometric recognition based on Gram polynomials and probabilistic neural networks (PNN). The usage of heart sounds as physiological biometric traits was first introduced in [1], in which the authors proposed and started exploring this idea. Heart sound recognition is based on the analysis of PCG (PhonoCardioGram) sequences. The proposed system presents good performance obtaining an error rate of 13.70 % over a database of 50 people, containing multiple heart sequences per person, each lasting from 20 to 70 seconds