1,720,981 research outputs found
A new approach for Lead-Acid batteries modeling by local cosine
No full textIn this paper a new approach based on the Local Cosine Bases is proposed in order to obtain an easy and improved Lead-Acid battery modeling so avoiding the training process of RNN and the need of big amount of relative data training sets. The wavelet packet analysis give us a tools to achieve major improvements on data discrimination and analysis. In particular the Local Cosine Bases transform allows us to sensitively reduce the number of significant coefficients, it is useful to synthesize a complex signal with an high degree of approximation of the original signal
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
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
Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach
No full textThe studies about the Sun rise a strong interest regarding modifications caused by the solar activity on the Earth. For almost a century in literature was discussed the problem of forecasting and analysis of the space weather, which in his definition covers both the near-earth space and the biospheric affection clue to the environmental interaction with the Sun. In particular in the last years increased the attention for magnetospheric response in conjunction with the technological infrastructure and the biosphere itself. This to prevent i.e. spacecraft failures or possible treats to human health. Since the main effect of the activity of the Sun is the solar wind, rises the aim to found a correlation between itself and the localized variations induced on the magnetosphere being the purpose to predict long-term variation of the magnetic field from solar wind time series. As recently proposed for solar wind forecasting, an hybrid approach will be here used than joining the wavelet analysis with the prediction capabilities of recurrent neural networks with an adaptive amplitude activation function algorithm in order to avoid the need to standardize or resealing the input signal and to match the exact range of the activation function
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Wavelet recurrent neural network with semi-parametric input data preprocessing for micro-wind power forecasting in integrated generation Systems
No full textWind power penetration is increasing more and more in the modern power system and an accurate wind power forecasting is now required to provide an help to the system operator to consider this renewable source in economic scheduling and other typical tasks of the electrical power system or in smart grid applications. The novelty of this Wavelet Recurrent Neural Network (WRNN) based approach consists on the model construction for micro wind generations. The WRNN does not provides only a prediction for the wavelet coefficients like in other previous studies of the authors in this research area but it is able to reconstruct directly the power signal from band-selected coefficients. The presented approach does not provides only an accurate forecasting model respect to the state of art in the field, but it is also useful for case studies which suffer of a major lack of wind data regarding the geographic site and of accurate and long historical study of the wind speed time series. In fact due to the proposed method of training based on a semiparametric input data preprocessing as Parzen windows then wind power output forecasting is improved
Dual Boundary Element Method for fatigue crack growth: implementation of the Richard’s criterion
Full text linkA new criterion for fatigue crack growth, whose accuracy was previously tested in the literature with the Finite Element Method, is here adopted with a Dual Boundary Element formulation. The fatigue crack growth of an elliptical inclined crack, embedded in a three dimensional cylindrical bar, is analyzed. In this way in addition to the propagation angle estimated by the Sih’s criterion, it is possible to take into account a twist propagation angle. The two propagation criteria are compared in terms of shape of the propagated crack and in terms of SIFs along the crack front. The efficiency of the Dual Boundary Element Method in this study is highlighted
A radial basis function neural network based approach for the model parameters estimation of a photovoltaic module
No full textDesign and development process of solar cells can be greatly enhanced by using accurate models in order to predict accurately their behaviour. The main aim of this paper is to investigate the application of neural network based PV equivalent circuit model to improve the model accuracy and to show the necessity of including the variation of all parameters according the change of the operating conditions. The radial basis function neural network is utilized to predict the electric current, power and equivalent circuit parameters by only using data of irradiation and temperature. A lot of available experimental data were used for training the radial basis function neural network, which employs a backpropagation algorithm. Simulation and experimental validation is reported
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