104 research outputs found
An Innovative Hybrid Neuro-wavelet Method for Reconstruction of Missing Data in Astronomical Photometric Surveys
No full textThe investigation of solar-like oscillations for probing the star interiors has encountered a tremendous growth in the last decade. For ground based observations the most important difficulties in properly identifying the true oscillation frequencies of the stars are produced by the gaps in the observation time-series and the presence of atmospheric plus the intrinsic stellar granulation noise, unavoidable also in the case of space observations. In this paper an innovative neuro-wavelet method for the reconstruction of missing data from photometric signals is presented. The prediction of missing data was done by using a composite neuro-wavelet reconstruction system composed by two neural networks separately trained. The combination of these two neural networks obtains a ”forward and backward” reconstruction. This technique was able to provide reconstructed data with an error greatly lower than the absolute a priori measurement error. The reconstructed signal frequency spectrum matched the expected spectrum with high accuracy
3D GPR Model in the Military District of San Giacomo Degli Spagnoli (Palermo)
No full textThe georadar method was used to try to find some anthropic structures in a large square inside the Carabinieri barracks in the former military complex of San Giacomo degli Spagnoli in Palermo (Italy). These investigations are part of a broader context of a study of the entire area. The purpose of the investigations is to try to understand if under the ground there are the remains of an ancient horse passage that connected the Royal Palace of Palermo with the sea gate of the city. Furthermore, in the Middle Ages, on the site of the present square, there were most likely two churches, which no longer exist, as evidenced by numerous historical testimonies. One of the two, San Giacomo la Mazara, is known to have was placed right in front of the church of San Paolo, the subject of previous investigations. The investigations carried out on the main square of the military district allowed us to reconstruct a 3D georadar model in which numerous anomalies are highlighted. Some superficial anomalies have been attributed to the presence of sub-services, the deeper ones could be caused by the remains of the medieval underground way or those of the no longer existing medieval churches, but identifying their true nature requires further investigations and archaeological tests
Some Remarks on the Application of RNN and PRNN for the Charge-Discharge Simulation of Advanced Lithium-Ions Battery Energy Storage
No full textIn this paper is reported a critical review, experiences and results about state of charge (SOC) and voltage prediction of Lithium-ions batteries obtained by recurrent neural network (RNN) and pipelined recurrent neural network (PRNN) based simulation. These soft computing technologies will be here presented, utilized and implemented to obtain the typical charge characteristics and the charge/discharge simulation procedure of a commercial solid-polymer technology based cell. Simulations are compared with experimental data manufacturers
Advanced and Adaptive Dispatch for Smart Grids by means of Predictive Models
No full textAbstract—Integrated generation systems are increasingly con- sidered suitable to supply remote areas, less developed coun- tries, and small isolated communities with power. The energy management investigated in this paper concerns a smart grid encompassing a photovoltaic park. We propose a novel cloud- distributed solution to determine the best energy dispatch, i.e. where energy is going to be used and whether to change the operating points for some consumption devices. Neural networks have been used to predict both energy production and consump- tion, making it possible to strategically set the activation time of loading devices and to minimize energy flow changes. Moreover, cloud computing resources make it possible to have fast and distributed computation on the big amount of data gauging power production and consumption
An advanced neural network based solution to enforce dispatch continuity in smart grids
No full textIn energy generation systems including a photovoltaic park, fluctuations are the norm: both production and demand levels can vary on hourly basis. Hence, energy management and dispatching systems have to cope with the possibility of inadequate production while satisfying as much as possible user demands. We put forward a management solution that models the behaviour of each production plant and consumption device, and determines energy allocation. For this, gathered data are wavelet transformed to let us retain only the useful characteristics of data on both large and small scales of the signal. Models are handled by several neural networks which perform predictions in advance of 48-hour, with a granularity of half an hour. Moreover, according to realtime user demands, the management solution determines energy flows between production plants and consumption devices. Therefore, while in some cases it might be necessary to postpone the activation of some consumption devices, in others we can take advantage of a production surplus. Thanks to the proposed solution proper actuators can be programmed beforehand to improve the fairness to users, and use peaks of energy production, thus reducing green energy shortage, and extra costs
A spiking neural network-based long-term prediction system for biogas production
No full textEfficient energy production from biomass is a central rarch issue in the context of clean alternative energy resource. In this work we propose a novel model based on spiking neural networks cubes in order to model the chemical processes that goes on in a digestor for the production of usable biogas. For the implementation of the predictive structure, we have used the NeuCube computational framework. The goals of the proposed model were: develop a tool for real applications (low-cost and efficient), generalise the data when the system presents high sensitivity to small differences on the initial conditions, take in account the “multi-scale” temporal dynamics of the chemical processes occurring in the digestor, since the variations present in the early stages of the processes are very quick, whereas in the later stages are slower. By using the first ten days of observation the implemented system has been proven able to predict the evolution of the chemical process up to the 100th day obtaining an high degree of accuracy with respect to the experimental data measured in laboratory. This is due to the fact that the spiking neural networks have shown to be able to modeling complex information processes and then it has been shown that spiking neurons are able to handle patterns of activity that spans different time scales. Thanks to such properties, our system is able to capture the multi-scale trend of the time series associated to the early-stage evolutions, as well as their interaction, which are crucial in the point of view of the information content to obtain a good long-term prediction
Organic solar cells defects detection by means of an elliptical basis neural network and a new feature extraction technique
No full textThe study proposed in this paper devises to develop a new methodology based on elliptical basisneural network (EBNN) and on a new feature extraction technique in order to recognize theorganic solar cells (OSCs) defects. The feature extraction procedure has been obtained by usingthe co-occurrence matrices and the SVD decomposition applied to atomic microscope forceimagery. The polymer-based OSCs used for this work have been produced at the optoelectronicorganic semiconductor devices laboratory at Ben Gurion University of the Negev. The testsperformed show that with our approach it is possible to obtain a correct classification percentageof 95.4% proving that the proposed feature extraction technique based on the co-occurrenceMatrix and the SVD decomposition is very effective in the detection of different types of OSC surface defects
A New Iterative FIR Filter Design Approach using a Gaussian Approximation
No full textThe paper presents a novel iterative methodology for the design of FIR filters based on an approssimation of the desired filter frequency response using a Gabor system generated by the Gaussian function. The proposed method exhibits simplicity of implementation, comparable to that of window-based design methods and ensures accuracy in the fulfillment of design requirements, comparable to the one achieved by the Parks- McClellans method. Furthermore, two other advantages of this method are: closed-form formula for the tap coefficients of the filter and the smooth, monotonically decreasing behaviour of the frequency response from DC to infinite frequency
Determinants of business angels investments: an empirical analysis of the italian informal venture capital market product=determinants-of-business-angels-investments-an-empirical-analysis-of-the-italian-informal-venture-capital-market
No full textThe aim of this study is to analyze the returns of business angels’ investments and their determinants. In this research the author wants to investigate the relationship existing between the performance of business angels investments and a series of explanatory variables widely used in the literature dealing with formal venture capital investments. Thanks to the data provided by surveyed business angels about their exits, it has been possible to build a dataset containing the details of about 90 disinvestments made in Italy during the 2007-2010 time period. This study shows that the most important features business angels look for when financing new firms is the management team, followed by the potential growth of the market. - See more at: http://dl4.globalstf.org/?wpsc-product=determinants-of-business-angels-investments-an-empirical-analysis-of-the-italian-informal-venture-capital-market#sthash.8tNaCe5V.dpu
What drives the returns of business angels’ investments? An empirical analysis of the Italian informal venture capital market
No full textThe aim of this study is to analyze the returns of business angels’ investments and their determinants. In this research the author wants to investigate the relationship existing between the performance of business angels’ investments and a series of explanatory variables widely used in the literature dealing with formal venture capital investments. Thanks to the data provided by surveyed business angels about their exits, it has been possible to build a dataset containing the details of about 90 disinvestments made in Italy during the 2007-2010 time periods. This study shows that the most important features business angels look for when financing new firms is the management team, followed by the potential growth of the market. Furthermore, the exit strategy and the industry financed have a significant impact on the IRR of angels’ investment
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