| 1. |
- Machado Filho, Manoel Alves, et al.
(author)
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Density Functional Theory-Fed Phase Field Model for Semiconductor Nanostructures: The Case of Self-Induced Core-Shell InAlN Nanorods
- 2024
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In: Crystal Growth & Design. - : AMER CHEMICAL SOC. - 1528-7483 .- 1528-7505.
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Journal article (peer-reviewed)abstract
- The self-induced formation of core-shell InAlN nanorods (NRs) is addressed at the mesoscopic scale by density functional theory (DFT)-resulting parameters to develop phase field modeling (PFM). Accounting for the structural, bonding, and electronic features of immiscible semiconductor systems at the nanometer scale, we advance DFT-based procedures for computation of the parameters necessary for PFM simulation runs, namely, interfacial energies and diffusion coefficients. The developed DFT procedures conform to experimental self-induced InAlN NRs' concerning phase-separation, core/shell interface, morphology, and composition. Finally, we infer the prospects for the transferability of the coupled DFT-PFM simulation approach to a wider range of nanostructured semiconductor materials.
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| 2. |
- Moreira, André R., et al.
(author)
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Classification of Oil Rigs in SAR Images Using RPCA-Based Preprocessing
- 2024
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In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783800762873 ; , s. 432-437
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Conference paper (peer-reviewed)abstract
- This paper uses a signal separation method called Robust Principal Component Analysis (RPCA) as a pre-processing technique to improve the classification of oil rigs in Synthetic Aperture Radar (SAR) images. After the pre-processing method, features are extracted from the images using the VGG-16 convolutional neural network. These features guide classification through Support Vector Machine (SVM), Neural Networks, and Logistic Regression algorithms. The experiments used SAR images from the Sentinel-1 system, C-band, and VH polarization. Early results highlight that preprocessing improves classification accuracy compared to conventional methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.
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| 3. |
- Murari, A., et al.
(author)
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A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors
- 2024
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In: Nature Communications. - 2041-1723 .- 2041-1723. ; 15:1
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Journal article (peer-reviewed)abstract
- The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.
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