6,744 research outputs found
Technical Design Report for the PANDA Solenoid and Dipole Spectrometer Magnets
This document is the Technical Design Report covering the two large
spectrometer magnets of the PANDA detector set-up. It shows the conceptual
design of the magnets and their anticipated performance. It precedes the tender
and procurement of the magnets and, hence, is subject to possible modifications
arising during this process.Comment: 10 pages, 14MB, accepted by FAIR STI in May 2009, editors: Inti
Lehmann (chair), Andrea Bersani, Yuri Lobanov, Jost Luehning, Jerzy Smyrski,
Technical Coordiantor: Lars Schmitt, Bernd Lewandowski (deputy),
Spokespersons: Ulrich Wiedner, Paola Gianotti (deputy
Technical Design Report for the PANDA Solenoid and Dipole Spectrometer Magnets
This document is the Technical Design Report covering the two large spectrometer magnets of the PANDA detector set-up. It
shows the conceptual design of the magnets and their anticipated performance. It precedes the tender and procurement of the magnets and, hence, is subject to possible
modifications arising during this process
Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
We present a simulation-based study using deep convolutional neural networks
(DCNNs) to identify neutrino interaction vertices in the MINERvA passive
targets region, and illustrate the application of domain adversarial neural
networks (DANNs) in this context. DANNs are designed to be trained in one
domain (simulated data) but tested in a second domain (physics data) and
utilize unlabeled data from the second domain so that during training only
features which are unable to discriminate between the domains are promoted.
MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at
Fermilab. -dependent cross sections are an important part of the physics
program, and these measurements require vertex finding in complicated events.
To illustrate the impact of the DANN we used a modified set of simulation in
place of physics data during the training of the DANN and then used the label
of the modified simulation during the evaluation of the DANN. We find that deep
learning based methods offer significant advantages over our prior track-based
reconstruction for the task of vertex finding, and that DANNs are able to
improve the performance of deep networks by leveraging available unlabeled data
and by mitigating network performance degradation rooted in biases in the
physics models used for training.Comment: 41 page
Low-cost technologies used in corrosion monitoring
Globally, corrosion is the costliest cause of the deterioration of metallic and concrete structures, leading to significant financial losses and unexpected loss of life. Therefore, corrosion monitoring is vital to the assessment of structuresâ residual performance and for the identification of pathologies in early stages for the predictive maintenance of facilities. However, the high price tag on available corrosion monitoring systems leads to their exclusive use for structural health monitoring applications, especially for atmospheric corrosion detection in civil structures. In this paper a systematic literature review is provided on the state-of-the-art electrochemical methods and physical methods used so far for corrosion monitoring compatible with low-cost sensors and data acquisition devices for metallic and concrete structures. In addition, special attention is paid to the use of these devices for corrosion monitoring and detection for in situ applications in different industries. This analysis demonstrates the possible applications of low-cost sensors in the corrosion monitoring sector. In addition, this study provides scholars with preferred techniques and the most common microcontrollers, such as Arduino, to overcome the corrosion monitoring difficulties in the construction industry.The authors are indebted to the projects PID2021â126405OBâC31 and PID2021â126405OBâC32 funded by FEDER fundsâA Way to Make Europe and Spanish Ministry of Economy and Comâpetitiveness MICIN/AEI/10.13039/501100011033/, project PID2019â106555RBâI00 and project IDEAS 2.14 from Ports 4.0. It should also be noted that funding for this research was provided for Seyedâmilad Komarizadehasl by the European Social Fund and the Spanish Agencia Estatal de InvestiâgaciĂłn del Ministerio de Ciencia InnovaciĂłn y Universidades, grant (PRE2018â083238).Peer ReviewedPostprint (published version
SHMD \u272024 â Book of abstracts
Book of abstracts of the 17th International Symposium of the Croatian Metallurgical Society - SHMD \u272024 - Materials and metallurgy, Zagreb, Croatia, April 18-19 2024
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