1,721,041 research outputs found
Hybrid MultiGene Genetic Programming - Artificial neural networks approach for dynamic performance prediction of an aeroengine
No full textDynamic aeroengine models have an important role in the design of real-time control systems. Modelling of aeroengines using dynamic performance simulations is a key step in the design process in order to reduce costs and the development period. A dynamic model can provide a numerical counterpart for the development of control systems and for the study of the engine behaviour in both steady and unsteady scenarios. The latter situation is particularly felt in the military field. The Viper 632-43 engine analysed in this work is a military turbojet, so it was necessary to develop a model that would replicate its behaviour as realistically as possible. The model was built using the Gas turbine Simulation Program (GSP) software and validated both in steady and transient conditions. Once the engine model was validated, different machine learning techniques were used to estimate (data mining) and predict an engine parameter; the Exhaust Gas Temperature (EGT) has been chosen as the key parameter. A MultiGene Genetic Programming (MGGP) technique has been used to derive simple mathematical relationships between different input parameters and the EGT. These, then, can be used to calculate the EGT value of a real Viper 632-43 engine knowing a priori the input parameters and in any operating condition. Finally, the EGT estimated by this algorithm has been added to the dataset used for the one-step-ahead EGT prediction by Artificial Neural Network (ANN). A time-series ANN was used for the EGT prediction, i.e. the Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network. This network recognizes the input data as a real time series and is therefore able to predict the output in the next time step. It was chosen to use, as forecasting method, the one-step-ahead technique which allows to predict the EGT in the immediately next time step
Data regarding dynamic performance predictions of an aeroengine
Full text linkThe design of aeroengine real-time control systems needs the implementation of machine learning based techniques. The lack of in-flight aeroengine performance data is a limit for the researchers interested in the development of these prediction algorithms. Dynamic aeroengine models can be used to overcome this lack. This data article presents data regarding the performance of a turbojet that were predicted by the dynamic engine model that was built using the Gas turbine Simulation Program (GSP) software. The data were also used to implement an Artificial Neural Network (ANN) that predicts the in-flight aeroengine performance, such as the Exhaust Gas Temperature (EGT). The Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network was used. The neural network predictions have been also given as dataset of the present article. The data presented here are related to the article entitled “MultiGene Genetic Programming - Artificial Neural Networks approach for dynamic performance prediction of an aeroengine” [1]
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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