S. Dormido-Canto

2.6k total citations
119 papers, 1.4k citations indexed

About

S. Dormido-Canto is a scholar working on Control and Systems Engineering, Nuclear and High Energy Physics and Artificial Intelligence. According to data from OpenAlex, S. Dormido-Canto has authored 119 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Control and Systems Engineering, 32 papers in Nuclear and High Energy Physics and 30 papers in Artificial Intelligence. Recurrent topics in S. Dormido-Canto's work include Magnetic confinement fusion research (31 papers), Anomaly Detection Techniques and Applications (20 papers) and Experimental Learning in Engineering (16 papers). S. Dormido-Canto is often cited by papers focused on Magnetic confinement fusion research (31 papers), Anomaly Detection Techniques and Applications (20 papers) and Experimental Learning in Engineering (16 papers). S. Dormido-Canto collaborates with scholars based in Spain, Chile and Italy. S. Dormido-Canto's co-authors include Gonzalo Farías, S. Dormido, J. Vega, Ernesto Fábregas, Raquel Dormido, A. Murari, Natividad Duro, Francisco Esquembre, J. Sánchez and Héctor Vargas and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Industrial Electronics and Expert Systems with Applications.

In The Last Decade

S. Dormido-Canto

112 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Dormido-Canto Spain 19 395 339 304 276 190 119 1.4k
J. Sánchez Spain 28 971 2.5× 1.2k 3.4× 584 1.9× 88 0.3× 735 3.9× 212 3.1k
Pekka Toivanen Finland 17 126 0.3× 163 0.5× 144 0.5× 177 0.6× 278 1.5× 81 1.1k
James L. Melsa United States 14 133 0.3× 670 2.0× 16 0.1× 483 1.8× 259 1.4× 56 1.9k
Jianmin Li China 24 158 0.4× 39 0.1× 103 0.3× 368 1.3× 133 0.7× 103 1.8k
Ling Zhang China 17 30 0.1× 144 0.4× 63 0.2× 360 1.3× 225 1.2× 134 1.2k
Sushil J. Louis United States 24 24 0.1× 124 0.4× 83 0.3× 990 3.6× 114 0.6× 135 1.9k
Yuhao Zhu United States 24 35 0.1× 52 0.2× 139 0.5× 238 0.9× 653 3.4× 107 1.8k
Mark E. Oxley United States 18 169 0.4× 172 0.5× 9 0.0× 611 2.2× 214 1.1× 115 1.7k
Shiu Yin Yuen Hong Kong 22 37 0.1× 66 0.2× 56 0.2× 569 2.1× 289 1.5× 104 1.4k

Countries citing papers authored by S. Dormido-Canto

Since Specialization
Citations

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

Fields of papers citing papers by S. Dormido-Canto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Dormido-Canto. 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 S. Dormido-Canto. The network helps show where S. Dormido-Canto may publish in the future.

Co-authorship network of co-authors of S. Dormido-Canto

This figure shows the co-authorship network connecting the top 25 collaborators of S. Dormido-Canto. A scholar is included among the top collaborators of S. Dormido-Canto 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 S. Dormido-Canto. S. Dormido-Canto 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.
Correa, Ricardo, Gonzalo Farías, Ernesto Fábregas, et al.. (2024). Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic. Sensors. 24(9). 2764–2764. 1 indexed citations
2.
Vega, J., et al.. (2024). Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time. Nuclear Fusion. 64(4). 46010–46010. 3 indexed citations
3.
Dormido-Canto, S., et al.. (2024). Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model. Algorithms. 17(10). 445–445. 1 indexed citations
4.
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
5.
Farías, Gonzalo, et al.. (2024). Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution. Energies. 17(11). 2709–2709. 1 indexed citations
6.
Garcia, Gonzalo, et al.. (2023). Development and Control of a Real Spherical Robot. Sensors. 23(8). 3895–3895. 7 indexed citations
7.
Dormido-Canto, S., et al.. (2023). A novel feature engineering approach for high-frequency financial data. Engineering Applications of Artificial Intelligence. 125. 106705–106705. 5 indexed citations
8.
Fábregas, Ernesto, et al.. (2022). Modeling and Control of a Spherical Robot in the CoppeliaSim Simulator. Sensors. 22(16). 6020–6020. 9 indexed citations
9.
Fábregas, Ernesto, et al.. (2022). Deep learning exoplanets detection by combining real and synthetic data. PLoS ONE. 17(5). e0268199–e0268199. 6 indexed citations
10.
Murari, A., E. Peluso, T. Craciunescu, et al.. (2021). Frontiers in data analysis methods: from causality detection to data driven experimental design. Plasma Physics and Controlled Fusion. 64(2). 24002–24002. 1 indexed citations
11.
Farías, Gonzalo, et al.. (2020). Reinforcement Learning for Position Control Problem of a Mobile Robot. IEEE Access. 8. 152941–152951. 19 indexed citations
12.
Ruíz, M., et al.. (2019). OpenCL Implementation of an Adaptive Disruption Predictor Based on a Probabilistic Venn Classifier. IEEE Transactions on Nuclear Science. 66(7). 1007–1013. 3 indexed citations
13.
Fábregas, Ernesto, Gonzalo Farías, Ernesto Aranda‐Escolástico, et al.. (2019). Simulation and Experimental Results of a New Control Strategy For Point Stabilization of Nonholonomic Mobile Robots. IEEE Transactions on Industrial Electronics. 67(8). 6679–6687. 28 indexed citations
14.
Moreno, R., et al.. (2015). Advanced Disruption Predictor Based On The Locked Mode Signal: Application To Jet. 28. 9 indexed citations
15.
Moreno-Salinas, David, Dictino Chaos, Joaquín Aranda Almansa, et al.. (2014). Application of an Aeronautic Control for Ship Path Following. Journal of maritime research. 6(2). 71–82. 1 indexed citations
16.
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
17.
Sánchez, J., S. Dormido-Canto, Gonzalo Farías, & S. Dormido. (2011). Understanding automatic control concepts by playing games. International journal of engineering education. 27(3). 528–534. 6 indexed citations
18.
Dormido-Canto, S., J. Sánchez, & S. Dormido. (2007). A new control laboratory using parallel programming. International journal of engineering education. 24(6). 1170–1179.
19.
Almansa, Joaquín Aranda, Raúl Santiago Muñoz Aguilar, S. Dormido-Canto, & S. Dormido. (2005). An analysis of models identification methods for high speed crafts. Journal of maritime research. 2(1). 51–67. 1 indexed citations
20.
Santos, Matilde, et al.. (2004). Determinación de parámetros de la transfomada Wavelet para la clasificación de señales del diagnóstico scattering Thomson. 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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