scholarly journals Radiation study of the target station for the EMuS baseline scheme

2020 ◽  
Author(s):  
Guang Zhao
Keyword(s):  
Target Station ◽  
Radiation Study

Indirectly Cooled Radiation-Resistant Magnets for Hadron Target Station at J-PARC

2010 ◽  
Vol 20 (3) ◽  
pp. 344-347 ◽  
Author(s):  
H Takahashi ◽  
K Agari ◽  
E Hirose ◽  
M Ieiri ◽  
M Iio ◽  
...  
Keyword(s):  
Target Station ◽  
Radiation Resistant

The Spallation Neutron Source High Power Target Station Moderator Performance: Calculations and Studies

2003 ◽  
Vol 11 (1-2) ◽  
pp. 83-91 ◽  
Author(s):  
E.B. Iverson ◽  
P.D. Ferguson ◽  
F.X. Gallmeier ◽  
B.D. Murphy
Keyword(s):  
Neutron Source ◽  
High Power ◽  
Spallation Neutron Source ◽  
Target Station ◽  
High Power Target

Code Development and Target Station Design for Chinese Accelerator-Driven System Project

2016 ◽  
Vol 183 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Han-Jie Cai ◽  
Fen Fu ◽  
Jian-Yang Li ◽  
Ya-Ling Zhang ◽  
Xun-Chao Zhang ◽  
...  
Keyword(s):  
Target Station ◽  
Driven System ◽  
Accelerator Driven System ◽  
System Project ◽  
Code Development

scholarly journals An Empirical Mode-Spatial Model for Environmental Data Imputation

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 63 ◽  
Author(s):  
Benjamin Nelsen ◽  
D. Williams ◽  
Gustavious Williams ◽  
Candace Berrett
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Missing Values ◽  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Data Imputation ◽  
Accurate Data ◽  
Target Station ◽  
Periodic Components

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes.

scholarly journals Experimental Muon Source (EMuS) Target Station Shielding Design for "Baby Scheme"

2020 ◽  
Author(s):  
Nitin Yadav ◽  
Yuan Ye ◽  
Guang Zhao ◽  
Nikolaos Vassilopoulos
Keyword(s):  
Target Station ◽  
Shielding Design
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