scholarly journals Development and beam commissioning of a continuous-wave window-type radio-frequency quadrupole

2019 ◽  
Vol 22 (3) ◽  
Author(s):  
P. P. Gan ◽  
K. Zhu ◽  
Q. Fu ◽  
H. P. Li ◽  
M. J. Easton ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Radio Frequency Quadrupole ◽  
Beam Commissioning

Beam commissioning and analysis of a continuous-wave window-type deuteron radio-frequency quadrupole

2020 ◽  
Vol 29 (02) ◽  
pp. 1950111
Author(s):  
Qiuyun Tan ◽  
Kun Zhu ◽  
Pingping Gan ◽  
Qi Fu ◽  
Haipeng Li ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Ion Beam ◽  
Radiation Risk ◽  
Deuteron Beam ◽  
Ion Source ◽  
Advanced Technology ◽  
High Current ◽  
Radio Frequency Quadrupole ◽  
Beam Commissioning

As high-intensity beams are required for various applications, high-power, high-current, continuous-wave (CW) radio-frequency quadrupole (RFQ) accelerators have become a research focus in recent years and also a direction for development in the future. To master and accumulate the advanced technology in design, fabrication and operation of high-current CW RFQs, the RFQ group at Peking University has built a window-type CW RFQ, operating at 162.5[Formula: see text]MHz, to accelerate a 50-mA deuteron beam from 50[Formula: see text]keV to 1[Formula: see text]MeV. It is the first relatively high-frequency window-type CW RFQ in the world. A [Formula: see text] ion beam extracted from an electron cyclotron resonance (ECR) ion source was used for the beam commissioning because deuteron beam acceleration will produce a serious radiation risk. We compared and analyzed the measurement results obtained during the beam commissioning with simulations. The data show good consistency in many respects. For the discrepancies, we explain the issues in detail. We achieved stable and robust acceleration of about 1.5[Formula: see text]mA CW [Formula: see text] for 1[Formula: see text]h. Finally, we discuss the differences between [Formula: see text] ion beam acceleration and deuteron beam acceleration.

scholarly journals Design and multiphysics analysis of a 176 MHz continuous-wave radio-frequency quadrupole

2014 ◽  
Vol 17 (7) ◽  
Author(s):  
S. V. Kutsaev ◽  
B. Mustapha ◽  
P. N. Ostroumov ◽  
A. Barcikowski ◽  
D. Schrage ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Radio Frequency Quadrupole ◽  
Wave Radio

scholarly journals Development and beam test of a continuous wave radio frequency quadrupole accelerator

2012 ◽  
Vol 15 (11) ◽  
Author(s):  
P. N. Ostroumov ◽  
B. Mustapha ◽  
A. Barcikowski ◽  
C. Dickerson ◽  
A. A. Kolomiets ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Continuous Wave ◽  
Beam Test ◽  
Radio Frequency Quadrupole ◽  
Wave Radio

Proton injector operational results on a high-power continuous-wave radio-frequency quadrupole accelerator

2000 ◽  
Vol 71 (2) ◽  
pp. 767-770 ◽  
Author(s):  
Joseph D. Sherman ◽  
Gerald O. Bolme ◽  
Lash D. Hansborough ◽  
Thomas W. Hardek ◽  
Debora M. Kerstiens ◽  
...  
Keyword(s):  
Radio Frequency ◽  
High Power ◽  
Continuous Wave ◽  
Radio Frequency Quadrupole ◽  
Wave Radio ◽  
Proton Injector

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

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