Convergence of block spins defined by a random field

1980 ◽  
Vol 22 (6) ◽  
pp. 673-684 ◽  
Author(s):  
Carla C. Neaderhouser
Keyword(s):  
Random Field ◽  
Block Spins

Comparison of random field strengths in (1-x)SrTiO3-xBiFeO3 and (1-x)SrTiO3-xBaTiO3 relaxor ferroelectrics by means of acoustic emission

2020 ◽  
Vol 92 (2) ◽  
pp. 20401
Author(s):  
Evgeniy Dul'kin ◽  
Michael Roth
Keyword(s):  
Acoustic Emission ◽  
Random Field ◽  
Electric Fields ◽  
Bias Field ◽  
Relaxor Ferroelectrics ◽  
External Bias ◽  
Field Strengths

In relaxor (1-x)SrTiO3-xBiFeO3 ferroelectrics ceramics (x = 0.2, 0.3 and 0.4) both intermediate temperatures and Burns temperatures were successfully detected and their behavior were investigated in dependence on an external bias field using an acoustic emission. All these temperatures exhibit a non-trivial behavior, i.e. attain the minima at some threshold fields as a bias field enhances. It is established that the threshold fields decrease as x increases in (1-x)SrTiO3-xBiFeO3, as it previously observed in (1-x)SrTiO3-xBaTiO3 (E. Dul'kin, J. Zhai, M. Roth, Phys. Status Solidi B 252, 2079 (2015)). Based on the data of the threshold fields the mechanisms of arising of random electric fields are discussed and their strengths are compared in both these relaxor ferroelectrics.

On the energy spectra of a random field of internal waves

Tellus ◽  
1972 ◽  
Vol 24 (2) ◽  
pp. 161-163 ◽  
Author(s):  
Jacques C. J. Nihoul
Keyword(s):  
Random Field ◽  
Internal Waves ◽  
Energy Spectra

Open Domain New Word Detection Using Condition Random Field Method

2013 ◽  
Vol 24 (5) ◽  
pp. 1051-1060 ◽  
Author(s):  
Fei CHEN ◽  
Yi-Qun LIU ◽  
Chao WEI ◽  
Yun-Liang ZHANG ◽  
Min ZHANG ◽  
...  
Keyword(s):  
Random Field ◽  
Field Method ◽  
Open Domain ◽  
Word Detection

Fusion Moves for Markov Random Field Optimization

2010 ◽  
Vol 32 (8) ◽  
pp. 1392-1405 ◽  
Author(s):  
Victor Lempitsky ◽  
Carsten Rother ◽  
Stefan Roth ◽  
Andrew Blake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Markov Random

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

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