R. Moreno

808 total citations
19 papers, 430 citations indexed

About

R. Moreno is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Materials Chemistry. According to data from OpenAlex, R. Moreno has authored 19 papers receiving a total of 430 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Nuclear and High Energy Physics, 6 papers in Artificial Intelligence and 6 papers in Materials Chemistry. Recurrent topics in R. Moreno's work include Magnetic confinement fusion research (10 papers), Fusion materials and technologies (5 papers) and Anomaly Detection Techniques and Applications (4 papers). R. Moreno is often cited by papers focused on Magnetic confinement fusion research (10 papers), Fusion materials and technologies (5 papers) and Anomaly Detection Techniques and Applications (4 papers). R. Moreno collaborates with scholars based in Spain, Italy and United Kingdom. R. Moreno's co-authors include A. Murari, J. Vega, S. Dormido-Canto, A. Pereira, David Moreno-Salinas, Joaquín Aranda Almansa, Juan Manuel López, Shmuel Osovski, D. Alves and R. Felton and has published in prestigious journals such as Review of Scientific Instruments, Applied Soft Computing and Engineering Fracture Mechanics.

In The Last Decade

R. Moreno

19 papers receiving 397 citations

Peers

R. Moreno

NHEP

CNC

A. Pereira Spain

G.A. Rattá Spain

Sara Carcangiu Italy

E. Barrera Spain

Yasushi Nauchi Japan

An Sun China

Xu Fang China

Sandeep Madireddy United States

Patrick McDaniel United States

Guoliang Fan China

A. Pereira Spain

R. Moreno

179 ×0.9

NHEP

118 ×0.9

83 ×0.9

54 ×0.7

92 ×1.7

CNC

Citations per year, relative to R. Moreno R. Moreno (= 1×) peers A. Pereira

Countries citing papers authored by R. Moreno

Since Specialization

Citations

This map shows the geographic impact of R. Moreno'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 R. Moreno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites R. Moreno more than expected).

Fields of papers citing papers by R. Moreno

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by R. Moreno. 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 R. Moreno. The network helps show where R. Moreno may publish in the future.

Co-authorship network of co-authors of R. Moreno

This figure shows the co-authorship network connecting the top 25 collaborators of R. Moreno. A scholar is included among the top collaborators of R. Moreno 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 R. Moreno. R. Moreno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

Lei, Bo, et al.. (2023). Exploring the trade-off between performance and annotation complexity in semantic segmentation. Engineering Applications of Artificial Intelligence. 123. 106299–106299. 6 indexed citations

Luengo, Julián, et al.. (2021). A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. Information Fusion. 78. 232–253. 37 indexed citations

Moreno, R., et al.. (2020). Toward quantitative fractography using convolutional neural networks. Engineering Fracture Mechanics. 231. 106992–106992. 36 indexed citations

Moreno, R., David Moreno-Salinas, & Joaquín Aranda Almansa. (2019). Black-Box Marine Vehicle Identification with Regression Techniques for Random Manoeuvres. Electronics. 8(5). 492–492. 22 indexed citations

Moreno-Salinas, David, R. Moreno, A. Pereira, Joaquín Aranda Almansa, & Jesús Manuel de la Cruz García. (2018). Modelling of a surface marine vehicle with kernel ridge regression confidence machine. Applied Soft Computing. 76. 237–250. 47 indexed citations

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

Vega, J., R. Moreno, A. Pereira, S. Dormido-Canto, & A. Murari. (2016). Advanced Disruption Predictor Based On The Locked Mode Signal: Application To Jet. 28–28. 12 indexed citations

Vega, J., R. Moreno, A. Pereira, et al.. (2016). Review of disruption predictors in nuclear fusion: Classical, from scratch and anomaly detection approaches. 6375–6379. 3 indexed citations

Moreno, R., et al.. (2016). Disruption Prediction on JET during the ILW Experimental Campaigns. Fusion Science & Technology. 69(2). 485–494. 12 indexed citations

10.

Moreno, R., et al.. (2015). Advanced Disruption Predictor Based On The Locked Mode Signal: Application To Jet. 28. 9 indexed citations

11.

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

12.

Vega, J., R. Moreno, A. Pereira, et al.. (2015). Disruption precursor detection: Combining the time and frequency domains. 48. 1–8. 16 indexed citations

13.

Moreno, R., et al.. (2014). Automatic location of disruption times in JET. Review of Scientific Instruments. 85(11). 11D826–11D826. 1 indexed citations

14.

Vega, J., A. Murari, S. Dormido-Canto, et al.. (2014). Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks. Nuclear Fusion. 54(12). 123001–123001. 44 indexed citations

15.

Moreno, R., J. Vega, A. Murari, et al.. (2014). Robustness and increased time resolution of JET Advanced Predictor of Disruptions. Plasma Physics and Controlled Fusion. 56(11). 114003–114003. 4 indexed citations

16.

Vega, J., S. Dormido-Canto, Juan Manuel López, et al.. (2013). Results of the JET real-time disruption predictor in the ITER-like wall campaigns. Fusion Engineering and Design. 88(6-8). 1228–1231. 76 indexed citations

17.

Murari, A., J. Vega, M. Gelfusa, et al.. (2013). Clustering based on the geodesic distance on Gaussian manifolds for the automatic classification of disruptions. Nuclear Fusion. 53(3). 33006–33006. 43 indexed citations

18.

Dormido-Canto, S., J. Vega, A. Murari, et al.. (2013). Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER. Nuclear Fusion. 53(11). 113001–113001. 52 indexed citations

19.

Ruíz, M., J. Vega, S. Dormido-Canto, et al.. (2012). Results of the JET real-time disruption predictor in the ITER-like wall campaigns. 1 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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