scholarly journals Alignment labeling scheme for the reverse bends, instrument sections, and the final focus beam line elements and their supports

1986 ◽  
Author(s):  
H. Friedsam ◽  
W. Oren ◽  
R. Pitthan ◽  
R. Pushor ◽  
R. Ruland
Keyword(s):  
Beam Line ◽  
Focus Beam ◽  
Labeling Scheme ◽  
Line Elements

Design of the Beam Line Elements for Proton Computed Tomography

2007 ◽  
Vol 50 (95) ◽  
pp. 1489 ◽  
Author(s):  
Hyungjoon Ryu ◽  
Boyeol Choi ◽  
Jaiki Lee ◽  
Jongwon Kim
Keyword(s):  
Computed Tomography ◽  
Beam Line ◽  
Proton Computed Tomography ◽  
Line Elements

scholarly journals Present status and first results of the final focus beam line at the KEK Accelerator Test Facility

2010 ◽  
Vol 13 (4) ◽  
Author(s):  
P. Bambade ◽  
M. Alabau Pons ◽  
J. Amann ◽  
D. Angal-Kalinin ◽  
R. Apsimon ◽  
...  
Keyword(s):  
Present Status ◽  
Beam Line ◽  
Test Facility ◽  
Focus Beam ◽  
First Results

scholarly journals First beam waist measurements in the final focus beam line at the KEK Accelerator Test Facility

2010 ◽  
Vol 13 (9) ◽  
Author(s):  
Sha Bai ◽  
Alexander Aryshev ◽  
Philip Bambade ◽  
Doug Mc Cormick ◽  
Benoit Bolzon ◽  
...  
Keyword(s):  
Beam Waist ◽  
Beam Line ◽  
Test Facility ◽  
Focus Beam

A DOUBLE CRYSTAL MONOCHROMATOR FOR SOFT X-RAY BEAM LINE OF UVSOR

1986 ◽  
Vol 47 (C8) ◽  
pp. C8-135-C8-137
Author(s):  
T. MURATA ◽  
T. MATSUKAWA ◽  
M. MORI ◽  
M. OBASHI ◽  
S.-I. NAO-E ◽  
...  
Keyword(s):  
Beam Line ◽  
Double Crystal ◽  
X Ray ◽  
Double Crystal Monochromator

Decision Support System for Evaluating Impact of Product Carbon Labeling Scheme

2014 ◽  
Vol 1 (1) ◽  
pp. 89 ◽  
Author(s):  
Dong-ling Xu ◽  
Chris Foster ◽  
Ying Hu ◽  
Jian-bo Yang
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Labeling Scheme ◽  
Carbon Labeling

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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