Extraction of the structural environment of mutual system information agreement in multicomplex systems

2014 ◽  
Vol 75 (8) ◽  
pp. 1471-1478 ◽  
Author(s):  
I. V. Asharina ◽  
A. V. Lobanov
Keyword(s):  
Mutual System ◽  
Structural Environment ◽  
System Information

THE STRUCTURAL ENVIRONMENT OF Te DOPANTS IN GaAs USING EXAFS IN FLUORESCENCE MODE

1986 ◽  
Vol 47 (C8) ◽  
pp. C8-901-C8-904 ◽  
Author(s):  
G. N. GREAVES ◽  
P. J. HALFPENNY ◽  
G. M. LAMBLE ◽  
K. J. ROBERTS
Keyword(s):  
Structural Environment

System Information Model of Obtaining and Using Data of Remote Sensing the Earth

2008 ◽  
Vol 40 (11) ◽  
pp. 46-56
Author(s):  
Ludmila I. Samoilenko ◽  
Sergey A. Baulin ◽  
Tatyana V. Ilyenko ◽  
Margarita A. Kirnosova ◽  
Ludmila N. Kolos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Model ◽  
The Earth ◽  
System Information ◽  
Using Data

Formalization of Functions of the Prospective Program System for Protection of Automated System Information

2004 ◽  
Vol 62 (1-6) ◽  
pp. 81-91
Author(s):  
Ya. E. Lvovich ◽  
V. I. Sumin ◽  
I. I. Zastrozhnov ◽  
E. A. Rogozin ◽  
A. S. Dubrovin
Keyword(s):  
Automated System ◽  
Program System ◽  
System Information

scholarly journals A holistic seismotectonic model of Delhi region

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brijesh K. Bansal ◽  
Kapil Mohan ◽  
Mithila Verma ◽  
Anup K. Sutar
Keyword(s):  
Strain Energy ◽  
Fault Plane ◽  
Near Field ◽  
Energy Estimates ◽  
The West ◽  
Fault Plane Solutions ◽  
Northern India ◽  
Structural Environment ◽  
Reverse Faulting ◽  
Using Data

AbstractDelhi region in northern India experiences frequent shaking due to both far-field and near-field earthquakes from the Himalayan and local sources, respectively. The recent M3.5 and M3.4 earthquakes of 12th April 2020 and 10th May 2020 respectively in northeast Delhi and M4.4 earthquake of 29th May 2020 near Rohtak (~ 50 km west of Delhi), followed by more than a dozen aftershocks, created panic in this densely populated habitat. The past seismic history and the current activity emphasize the need to revisit the subsurface structural setting and its association with the seismicity of the region. Fault plane solutions are determined using data collected from a dense network in Delhi region. The strain energy released in the last two decades is also estimated to understand the subsurface structural environment. Based on fault plane solutions, together with information obtained from strain energy estimates and the available geophysical and geological studies, it is inferred that the Delhi region is sitting on two contrasting structural environments: reverse faulting in the west and normal faulting in the east, separated by the NE-SW trending Delhi Hardwar Ridge/Mahendragarh-Dehradun Fault (DHR-MDF). The WNW-ESE trending Delhi Sargoda Ridge (DSR), which intersects DHR-MDF in the west, is inferred as a thrust fault. The transfer of stress from the interaction zone of DHR-MDF and DSR to nearby smaller faults could further contribute to the scattered shallow seismicity in Delhi region.

Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Grienggrai Rajchakit ◽  
Ramalingam Sriraman ◽  
Rajendran Samidurai
Keyword(s):  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Markovian Jump ◽  
Stochastic Perturbations ◽  
Practical Applications ◽  
Markovian Jump Parameters ◽  
Generalized Neural Networks ◽  
System Information ◽  
Dissipativity Analysis

Abstract This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.

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