G.A. Rattá

2.1k total citations
47 papers, 618 citations indexed

About

G.A. Rattá is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, G.A. Rattá has authored 47 papers receiving a total of 618 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Nuclear and High Energy Physics, 13 papers in Artificial Intelligence and 11 papers in Aerospace Engineering. Recurrent topics in G.A. Rattá's work include Magnetic confinement fusion research (23 papers), Anomaly Detection Techniques and Applications (10 papers) and Time Series Analysis and Forecasting (7 papers). G.A. Rattá is often cited by papers focused on Magnetic confinement fusion research (23 papers), Anomaly Detection Techniques and Applications (10 papers) and Time Series Analysis and Forecasting (7 papers). G.A. Rattá collaborates with scholars based in Spain, Italy and United Kingdom. G.A. Rattá's co-authors include J. Vega, A. Murari, G. Vagliasindi, M. Johnson, Enrique E. Mombello, Aida Domínguez-Sáez, Carmen C. Barrios, P.C. de Vries, S. Dormido-Canto and José Antônio Jardini and has published in prestigious journals such as PLoS ONE, American Journal of Obstetrics and Gynecology and Expert Systems with Applications.

In The Last Decade

G.A. Rattá

45 papers receiving 569 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
G.A. Rattá Spain 15 252 162 117 116 114 47 618
A. Pereira Spain 12 179 0.7× 118 0.7× 54 0.5× 83 0.7× 45 0.4× 50 462
R. Moreno Spain 11 190 0.8× 129 0.8× 78 0.7× 90 0.8× 24 0.2× 19 430
Sara Carcangiu Italy 11 104 0.4× 75 0.5× 20 0.2× 77 0.7× 105 0.9× 39 349
G. D’Antona Italy 18 136 0.5× 35 0.2× 40 0.3× 80 0.7× 606 5.3× 94 955
Yasushi Nauchi Japan 10 64 0.3× 67 0.4× 172 1.5× 290 2.5× 87 0.8× 32 590
T.A. Ramstad Norway 19 62 0.2× 83 0.5× 55 0.5× 30 0.3× 350 3.1× 91 1.6k
Chengwen Zhong China 23 18 0.1× 66 0.4× 81 0.7× 351 3.0× 276 2.4× 122 1.8k
Xu Fang China 16 85 0.3× 52 0.3× 8 0.1× 165 1.4× 101 0.9× 51 623
An Sun China 18 46 0.2× 32 0.2× 14 0.1× 57 0.5× 520 4.6× 93 926
J. M. Seixas Brazil 15 55 0.2× 116 0.7× 9 0.1× 19 0.2× 168 1.5× 98 657

Countries citing papers authored by G.A. Rattá

Since Specialization
Citations

This map shows the geographic impact of G.A. Rattá's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by G.A. Rattá with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G.A. Rattá more than expected).

Fields of papers citing papers by G.A. Rattá

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by G.A. Rattá. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by G.A. Rattá. The network helps show where G.A. Rattá may publish in the future.

Co-authorship network of co-authors of G.A. Rattá

This figure shows the co-authorship network connecting the top 25 collaborators of G.A. Rattá. A scholar is included among the top collaborators of G.A. Rattá based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with G.A. Rattá. G.A. Rattá is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Rattá, G.A., et al.. (2024). Advancing MARFE detection in JET’s operational camera videos through Machine Learning techniques. Fusion Engineering and Design. 205. 114534–114534. 2 indexed citations
2.
Rattá, G.A., et al.. (2023). Characterization of physics events in JET preceding disruptions. Fusion Engineering and Design. 189. 113468–113468. 1 indexed citations
3.
Rattá, G.A., J. Vega, A. Murari, D. Gadariya, & Jet Contributors. (2021). PHAD: a phase-oriented disruption prediction strategy for avoidance, prevention, and mitigation in JET. Nuclear Fusion. 61(11). 116055–116055. 15 indexed citations
4.
Murari, A., Riccardo Rossi, E. Peluso, et al.. (2020). On the transfer of adaptive predictors between different devices for both mitigation and prevention of disruptions. Nuclear Fusion. 60(5). 56003–56003. 30 indexed citations
5.
Vega, J., R. Castro, S. Dormido-Canto, G.A. Rattá, & M. Ruíz. (2020). Automatic recognition of plasma relevant events: Implications for ITER. Fusion Engineering and Design. 156. 111638–111638. 1 indexed citations
6.
Rattá, G.A., J. Vega, & A. Murari. (2019). A multidimensional linear model for disruption prediction in JET. Fusion Engineering and Design. 146. 2393–2396. 4 indexed citations
7.
Domínguez-Sáez, Aida, G.A. Rattá, & Carmen C. Barrios. (2018). Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression. Energy. 149. 675–683. 55 indexed citations
8.
Vega, J., M. Ruíz, E. Barrera, et al.. (2018). Real-time implementation with FPGA-based DAQ system of a probabilistic disruption predictor from scratch. Fusion Engineering and Design. 129. 179–182. 2 indexed citations
9.
Pau, A., B. Cannas, G. Sias, et al.. (2017). Advances in the development of DIS_tool and first analysis on TCV disruptions. MPG.PuRe (Max Planck Society). 1 indexed citations
10.
Farías, Gonzalo, S. Dormido-Canto, J. Vega, et al.. (2016). Automatic feature extraction in large fusion databases by using deep learning approach. Fusion Engineering and Design. 112. 979–983. 30 indexed citations
11.
Rattá, G.A., J. Vega, A. Murari, S. Dormido-Canto, & R. Moreno. (2016). Global optimization driven by genetic algorithms for disruption predictors based on APODIS architecture. Fusion Engineering and Design. 112. 1014–1018. 6 indexed citations
12.
Pereira, A., et al.. (2015). Feature selection for disruption prediction from scratch in JET by using genetic algorithms and probabilistic predictors. Fusion Engineering and Design. 96-97. 907–911. 3 indexed citations
13.
Palacio, Montse, Teresa Cobo, G.A. Rattá, et al.. (2012). Performance of an automatic quantitative ultrasound analysis of the fetal lung to predict fetal lung maturity. American Journal of Obstetrics and Gynecology. 207(6). 504.e1–504.e5. 25 indexed citations
14.
15.
Ruíz, M., J. Vega, Guillermo de Arcas, et al.. (2011). Real Time Plasma Disruptions Detection in JET Implemented With the ITMS Platform Using FPGA Based IDAQ. IEEE Transactions on Nuclear Science. 58(4). 1576–1581. 11 indexed citations
16.
Ruíz, M., J. Vega, E. Barrera, et al.. (2010). Test-bed of a real time detection system for L/H and H/L transitions implemented with the ITMS platform. Fusion Engineering and Design. 85(3-4). 360–366. 2 indexed citations
17.
Ruíz, M., J. Vega, G.A. Rattá, et al.. (2010). Real time plasma disruptions detection in JET implemented with the ITMS platform using FPGA based IDAQ. 1–4. 2 indexed citations
18.
Murari, A., J. Vega, D. Mazon, et al.. (2010). Innovative signal processing and data analysis methods on JET for control in the perspective of next-step devices. Nuclear Fusion. 50(5). 55005–55005. 7 indexed citations
19.
Rattá, G.A., et al.. (2008). Feature extraction for improved disruption prediction analysis at JET. Review of Scientific Instruments. 79(10). 10F328–10F328. 19 indexed citations
20.
Mombello, Enrique E., et al.. (1991). Study of internal stresses intransformer windings due to lightning transient phenomena. Electric Power Systems Research. 21(3). 161–172. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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