Measurement performance assessment has been carried out for the latest design of the ITER Charge Exchange Recombination Spectroscopy (CXRS) Edge diagnostic system. Several plasma scenarios, covering all expected baseline operation regimes for ITER, were used. Various impurity (He, Be, C, and Ne) concentrations for the system whole spatial range (0.5 < r/a < 1.0) were considered. Statistical errors for the measurements of low-Z impurity temperature, density, and rotation velocity were calculated. Other non-statistical error sources were reviewed, including the presence of wall reflections, effects on the active charge-exchange line shape, calibration, and positioning uncertainties. Minimal impurity concentrations, allowing measurements with required accuracy, were obtained. It was shown that the CXRS Edge system will be able to measure primary plasma parameters with required accuracy, space, and time resolution.
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http://dx.doi.org/10.1063/5.0042029 | DOI Listing |
Rev Sci Instrum
August 2024
Max-Planck-Institut für Plasmaphysik, Greifswald, Germany.
Coherence Imaging Charge Exchange Recombination Spectroscopy (CICERS) is an imaging diagnostic installed in Wendelstein 7-X from which 2D maps of ion temperature (Ti) and impurity density (nZ) are obtained. The improved spatial resolution and coverage, as compared to standard Charge eXchange Recombination Spectroscopy (CXRS), with which these parameters can be assessed, come at the expense of spectral resolution, requiring the development of new strategies to isolate the active charge exchange contribution from passive and Bremsstrahlung radiation. In this work, a new approach based on the modeling of background radiation is presented and applied to the derivation of 2D Ti maps.
View Article and Find Full Text PDFRev Sci Instrum
July 2024
HUN-REN Centre for Energy Research, 1121 Budapest, Hungary.
Measurements of ion temperature profiles are required to assess the energy and particle transport processes in the Wendelstein 7-X stellarator. This device is equipped with a diagnostic alkali beam, which can be utilized to determine local impurity temperatures and densities by Charge Exchange Recombination Spectroscopy (CXRS). It could provide such profiles in the edge plasma, where other diagnostics are less efficient.
View Article and Find Full Text PDFRadiography (Lond)
January 2023
School of Radiography, University College Lillebaelt, Odense, Denmark; Health Sciences Research Centre, UCL University College, Odense, Denmark; Department of Regional Health Research, University of Southern Denmark; Department of Radiology, Kolding, Lillebaelt Hospital, University Hospitals of Southern Denmark, Denmark.
Introduction: This study aimed to test whether Advanced Edge Enhancement (AEE) software could improve the localisation of tubes, catheters or wires, while also affecting the overall image quality in chest x-rays (CXR).
Methods: In total, 50 retrospective CXRs were included. All images were obtained utilising the Canon X-ray system (CANON/Arcoma Precision T3 DR System, Canon Europe, Amsterdam, NL) with a CXDI-810C wireless detector.
Rev Sci Instrum
May 2021
Institution "Project Center ITER", 123182 Moscow, Russia.
Measurement performance assessment has been carried out for the latest design of the ITER Charge Exchange Recombination Spectroscopy (CXRS) Edge diagnostic system. Several plasma scenarios, covering all expected baseline operation regimes for ITER, were used. Various impurity (He, Be, C, and Ne) concentrations for the system whole spatial range (0.
View Article and Find Full Text PDFComput Methods Programs Biomed
October 2019
School of Biomedical Engineering, Southern Medical University, 1023-1063 Shatai South Road, Baiyun District, 510515, Guangzhou, China. Electronic address:
Background And Objective: In chest radiographs (CXRs), all bones and soft tissues are overlapping with each other, which raises issues for radiologists to read and interpret CXRs. Delineating the ribs and clavicles is helpful for suppressing them from chest radiographs so that their effects can be reduced for chest radiography analysis. However, delineating ribs and clavicles automatically is difficult by methods without deep learning models.
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