Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals

2019 ◽  
Vol 572 ◽  
pp. 75-95 ◽  
Author(s):  
Hossein Bonakdari ◽  
Amir Hossein Zaji ◽  
Andrew D. Binns ◽  
Bahram Gharabaghi
Keyword(s):  
Markov Chains ◽  
Uncertainty Analysis ◽  
Analysis Techniques

scholarly journals Perturbed Markov chains

2003 ◽  
Vol 40 (1) ◽  
pp. 107-122 ◽  
Author(s):  
Eilon Solan ◽  
Nicolas Vieille
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Transition Matrix ◽  
Matrix Analysis ◽  
Transition Matrices ◽  
Analysis Techniques ◽  
Graph Theoretic ◽  
The Matrix ◽  
Finite State ◽  
Finite State Space

We study irreducible time-homogenous Markov chains with finite state space in discrete time. We obtain results on the sensitivity of the stationary distribution and other statistical quantities with respect to perturbations of the transition matrix. We define a new closeness relation between transition matrices, and use graph-theoretic techniques, in contrast with the matrix analysis techniques previously used.

Perturbed Markov chains

2003 ◽  
Vol 40 (01) ◽  
pp. 107-122 ◽  
Author(s):  
Eilon Solan ◽  
Nicolas Vieille
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Transition Matrix ◽  
Matrix Analysis ◽  
Transition Matrices ◽  
Analysis Techniques ◽  
Graph Theoretic ◽  
The Matrix ◽  
Finite State ◽  
Finite State Space

We study irreducible time-homogenous Markov chains with finite state space in discrete time. We obtain results on the sensitivity of the stationary distribution and other statistical quantities with respect to perturbations of the transition matrix. We define a new closeness relation between transition matrices, and use graph-theoretic techniques, in contrast with the matrix analysis techniques previously used.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Dissociated Σ=27 boundaries in silicon

1988 ◽  
Vol 46 ◽  
pp. 624-625
Author(s):  
A. Garg ◽  
W.A.T. Clark ◽  
J.P. Hirth
Keyword(s):  
Electron Diffraction ◽  
Local Minimum ◽  
Boundary Energy ◽  
Coincidence Site Lattice ◽  
Boundary Plane ◽  
Low Energy ◽  
Theoretical Understanding ◽  
Site Lattice ◽  
Kikuchi Pattern ◽  
Analysis Techniques

In the last twenty years, a significant amount of work has been done in the theoretical understanding of grain boundaries. The various proposed grain boundary models suggest the existence of coincidence site lattice (CSL) boundaries at specific misorientations where a periodic structure representing a local minimum of energy exists between the two crystals. In general, the boundary energy depends not only upon the density of CSL sites but also upon the boundary plane, so that different facets of the same boundary have different energy. Here we describe TEM observations of the dissociation of a Σ=27 boundary in silicon in order to reduce its surface energy and attain a low energy configuration.The boundary was identified as near CSL Σ=27 {255} having a misorientation of (38.7±0.2)°/[011] by standard Kikuchi pattern, electron diffraction and trace analysis techniques. Although the boundary appeared planar, in the TEM it was found to be dissociated in some regions into a Σ=3 {111} and a Σ=9 {122} boundary, as shown in Fig. 1.

Material analysis techniques using the ion mill

1996 ◽  
Vol 54 ◽  
pp. 1034-1035
Author(s):  
J. P. Benedict ◽  
R. M. Anderson ◽  
S. J. Klepeis
Keyword(s):  
Polished Surface ◽  
Surface Material ◽  
Analysis Techniques ◽  
Material Analysis ◽  
Optical Inspection ◽  
Electron Microscopy Analysis ◽  
Microscopy Analysis ◽  
Argon Ions ◽  
The Impact ◽  
Scanning Electron

Ion mills equipped with flood guns can perform two important functions in material analysis; they can either remove material or deposit material. The ion mill holder shown in Fig. 1 is used to remove material from the polished surface of a sample for further optical inspection or SEM ( Scanning Electron Microscopy ) analysis. The sample is attached to a pohshing stud type SEM mount and placed in the ion mill holder with the polished surface of the sample pointing straight up, as shown in Fig 2. As the holder is rotating in the ion mill, Argon ions from the flood gun are directed down at the top of the sample. The impact of Argon ions against the surface of the sample causes some of the surface material to leave the sample at a material dependent, nonuniform rate. As a result, the polished surface will begin to develop topography during milling as fast sputtering materials leave behind depressions in the polished surface.

Developmental Sentence Scoring

1984 ◽  
Vol 15 (3) ◽  
pp. 154-168 ◽  
Author(s):  
Mary Ann Lively
Keyword(s):  
Expressive Language ◽  
Analysis Techniques ◽  
Language Analysis ◽  
Scoring Errors

Developmental Sentence Scoring (DSS) is a useful procedure for quantifying thegrammatic structure of children's expressive language. Like most language analysis techniques, however, DSS requires considerable study and practice to use it correctly and efficiently. Clinicians learning DSS tend to make many scoring errors at first and they display similar confusions and mistakes. This article identifies some of these common "problem" areas and provides scoring examples to assist clinicians in learning the DSS procedure.

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