scholarly journals An empirical determination of proton auroral far ultraviolet emission efficiencies using a new nonclimatological proton flux extrapolation method

2012 ◽  
Vol 117 (A11) ◽  
pp. n/a-n/a ◽  
Author(s):  
H. K. Knight ◽  
D. J. Strickland ◽  
J. Correira ◽  
J. H. Hecht ◽  
P. R. Straus
Keyword(s):  
Proton Flux ◽  
Extrapolation Method ◽  
Ultraviolet Emission ◽  
Empirical Determination ◽  
Far Ultraviolet

High-resolution far ultraviolet emission spectra of electron-excited molecular deuterium

1999 ◽  
Vol 32 (15) ◽  
pp. 3813-3838 ◽  
Author(s):  
H Abgrall ◽  
E Roueff ◽  
Xianming Liu ◽  
D E Shemansky ◽  
G K James
Keyword(s):  
High Resolution ◽  
Emission Spectra ◽  
Ultraviolet Emission ◽  
Far Ultraviolet

scholarly journals Combine-Net: An Improved Filter Pruning Algorithm

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 264
Author(s):  
Jinghan Wang ◽  
Guangyue Li ◽  
Wenzhao Zhang
Keyword(s):  
Structured Model ◽  
Compression Algorithms ◽  
Pruning Algorithm ◽  
Model Compression ◽  
The Neural Network ◽  
Empirical Determination ◽  
Knowledge Distillation ◽  
Resource Constrained Devices ◽  
Constrained Devices

The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. Among them, structured model pruning is widely utilized because of its versatility. Structured pruning prunes the neural network itself and discards some relatively unimportant structures to compress the model’s size. However, in the previous pruning work, problems such as evaluation errors of networks, empirical determination of pruning rate, and low retraining efficiency remain. Therefore, we propose an accurate, objective, and efficient pruning algorithm—Combine-Net, introducing Adaptive BN to eliminate evaluation errors, the Kneedle algorithm to determine the pruning rate objectively, and knowledge distillation to improve the efficiency of retraining. Results show that, without precision loss, Combine-Net achieves 95% parameter compression and 83% computation compression on VGG16 on CIFAR10, 71% of parameter compression and 41% computation compression on ResNet50 on CIFAR100. Experiments on different datasets and models have proved that Combine-Net can efficiently compress the neural network’s parameters and computation.

Semi-empirical determination of the long-range molecular states dissociating to C(3PJ)+O(3PJ′)

1989 ◽  
Vol 134 (1) ◽  
pp. 7-18 ◽  
Author(s):  
B. Bussery ◽  
M.E. Rosenkrantz ◽  
D.D. Konowalow ◽  
M. Aubert-frécon
Keyword(s):  
Long Range ◽  
Empirical Determination ◽  
Semi Empirical

Load Spectrum Compilation Based on Nonparametric Statistical Extrapolation

2013 ◽  
Vol 694-697 ◽  
pp. 271-277 ◽  
Author(s):  
Zhi Qiang Xu ◽  
Hong Jian Wang ◽  
Ming Yao Yao
Keyword(s):  
Kernel Function ◽  
Time History ◽  
Critical Issue ◽  
Extrapolation Method ◽  
Load History ◽  
Load Spectrum ◽  
Load Characteristics ◽  
Nonparametric Statistical ◽  
Function Shape

Considering the special load characteristics of the wheel loader, thispaper focus on compiling the load spectrum of the transmission of the wheelloader using the nonparametric statistical extrapolation method (NSEM). In thisprocess, the determination of the kernel function shape is the critical issue,which has been discussed in detail. Before extrapolating the sample loadspectrum, the signal denoising of the field-tested time-history load signals isperformed. After that, the sample load cycles are obtained using the rainflowcounting method and the corresponding kernel function shape is determined. Thenthe NSEM of rainflow matrix is proposed, by which the whole-life load spectrumis estimated. The proposed extrapolation method can well realize the estimationof the load cycles that do not appear in sample load cycles but may exist inthe whole-life load history.

scholarly journals Empirical determination of the shape of dust attenuation curves in star-forming galaxies

2011 ◽  
Vol 417 (3) ◽  
pp. 1760-1786 ◽  
Author(s):  
Vivienne Wild ◽  
Stéphane Charlot ◽  
Jarle Brinchmann ◽  
Timothy Heckman ◽  
Oliver Vince ◽  
...  
Keyword(s):  
Star Forming ◽  
Empirical Determination ◽  
Dust Attenuation

scholarly journals Determination of Proton Flux and Conductance at pH 6.8 through Single Fo Sectors from Escherichia coli

2004 ◽  
Vol 87 (5) ◽  
pp. 3594-3599 ◽  
Author(s):  
Michael J. Franklin ◽  
William S.A. Brusilow ◽  
Dixon J. Woodbury
Keyword(s):  
Escherichia Coli ◽  
Proton Flux
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