Roberto Schirru

2.4k total citations
99 papers, 1.8k citations indexed

About

Roberto Schirru is a scholar working on Artificial Intelligence, Control and Systems Engineering and Aerospace Engineering. According to data from OpenAlex, Roberto Schirru has authored 99 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 35 papers in Control and Systems Engineering and 32 papers in Aerospace Engineering. Recurrent topics in Roberto Schirru's work include Fault Detection and Control Systems (35 papers), Nuclear reactor physics and engineering (25 papers) and Evolutionary Algorithms and Applications (16 papers). Roberto Schirru is often cited by papers focused on Fault Detection and Control Systems (35 papers), Nuclear reactor physics and engineering (25 papers) and Evolutionary Algorithms and Applications (16 papers). Roberto Schirru collaborates with scholars based in Brazil, Switzerland and Italy. Roberto Schirru's co-authors include Cláudio M.N.A. Pereira, César Marques Salgado, Luís Eduardo Barreira Brandão, Anderson Alvarenga de Moura Meneses, Pauli Adriano de Almada Garcia, Aquilino Senra Martinez, C.C. Conti, Cláubia Pereira, Ademir Xavier da Silva and William Luna Salgado and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Energy and Applied Soft Computing.

In The Last Decade

Roberto Schirru

92 papers receiving 1.7k citations

Peers

Roberto Schirru
Cláudio M.N.A. Pereira Brazil
Geoffrey T. Parks United Kingdom
Jiejin Cai China
Minjun Peng China
B.R. Upadhyaya United States
Yong-kuo Liu China
Man Gyun Na South Korea
Mohammad Mohammadi Iran
J. Wesley Hines United States
Miltiadis Alamaniotis United States
Cláudio M.N.A. Pereira Brazil
Roberto Schirru
Citations per year, relative to Roberto Schirru Roberto Schirru (= 1×) peers Cláudio M.N.A. Pereira

Countries citing papers authored by Roberto Schirru

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Schirru

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roberto Schirru

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Schirru. A scholar is included among the top collaborators of Roberto Schirru 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 Roberto Schirru. Roberto Schirru 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.
Dam, Roos Sophia de Freitas, William Luna Salgado, C.C. Conti, Roberto Schirru, & César Marques Salgado. (2024). Volume fraction detection in multiphase systems using neutron activation analysis and artificial neural network. Applied Radiation and Isotopes. 214. 111504–111504. 1 indexed citations
2.
Schirru, Roberto, et al.. (2024). Optimized modular nuclear reactor project utilizing artificial intelligence: Seed-blanket concept. Nuclear Engineering and Design. 423. 113187–113187. 8 indexed citations
3.
4.
Schirru, Roberto, et al.. (2024). Development of a deep neural network and a PSO algorithm to predict ore hardness using X-ray diffraction and atomic emission spectroscopy. Minerals Engineering. 213. 108760–108760. 1 indexed citations
5.
6.
Meneses, Anderson Alvarenga de Moura, et al.. (2024). A GPU-accelerated linear system solution for the Galerkin finite element method applied to neutron diffusion equation. Nuclear Engineering and Design. 421. 113103–113103.
7.
Pereira, Cláudio M.N.A., et al.. (2023). Prediction of LOCA’s break size and location based on random forest and Multi Tasking Deep Neural Network. Nuclear Engineering and Design. 415. 112711–112711. 4 indexed citations
8.
Dam, Roos Sophia de Freitas, et al.. (2023). A comparative study of a traditional localization algorithm and a deep learning model for radioactive particle tracking application. Applied Radiation and Isotopes. 205. 111156–111156. 2 indexed citations
9.
Schirru, Roberto, et al.. (2022). Deep neural networks for estimation of temperature values for thermal ageing evaluation of nuclear power plant equipment. Progress in Nuclear Energy. 156. 104542–104542. 8 indexed citations
10.
Salgado, César Marques, et al.. (2021). Development of a deep rectifier neural network for fluid volume fraction prediction in multiphase flows by gamma-ray densitometry. Radiation Physics and Chemistry. 189. 109708–109708. 10 indexed citations
11.
Dam, Roos Sophia de Freitas, William Luna Salgado, Roberto Schirru, & César Marques Salgado. (2021). Application of radioactive particle tracking and an artificial neural network to calculating the flow rate in a two-phase (oil–water) stratified flow regime. Applied Radiation and Isotopes. 180. 110061–110061. 13 indexed citations
12.
Dam, Roos Sophia de Freitas, et al.. (2020). Optimization of radioactive particle tracking methodology in a single-phase flow using MCNP6 code and artificial intelligence methods. Flow Measurement and Instrumentation. 78. 101862–101862. 6 indexed citations
13.
Salgado, William Luna, et al.. (2020). Determination of eccentric deposition thickness on offshore horizontal pipes by gamma-ray densitometry and artificial intelligence technique. Applied Radiation and Isotopes. 165. 109221–109221. 5 indexed citations
14.
Schirru, Roberto, et al.. (2018). Particle Swarm Optimization Applied to the Nuclear Fuel Bundle Spacer Grid Spring Design. Nuclear Technology. 205(5). 637–645. 1 indexed citations
15.
Schirru, Roberto, et al.. (2017). A Real Time Expert System For Decision Support In Nuclear Power Plants. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
16.
Schirru, Roberto, et al.. (2016). A new methodology for diagnosis system with ‘Don’t Know’ response for Nuclear Power Plant. Annals of Nuclear Energy. 100. 91–97. 12 indexed citations
17.
Schirru, Roberto, et al.. (2014). QDPSO and Minkowski Distance Applied to Transient Diagnosis System.. 611–616. 3 indexed citations
18.
Meneses, Anderson Alvarenga de Moura, Paola M. V. Rancoita, Tom Schaul, et al.. (2010). Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 621(1-3). 662–669. 5 indexed citations
19.
Pictet, Olivier V., et al.. (1998). Using Genetic Algorithms For Robust Optimization In Financial Applications. SSRN Electronic Journal. 22 indexed citations
20.
Pereira, Cláudio M.N.A., Roberto Schirru, & Aquilino Senra Martinez. (1970). Learning An Optimized Classification SystemFrom A Data Base Of Time Series Patterns UsingGenetic Algorithms. WIT transactions on information and communication technologies. 22. 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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