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Page 1056 of American Society of Civil Engineers. Collected Journals Vol. 117, Issue EM5
[page]
1991
American Society of Civil Engineers. Collected Journals
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regardless of the spectral description one may utilize a low AR model. ...
The simulated records exhibit an excellent agreement with the prescribed probabilistic characteristics, e.g., spectral density and correlation functions. ...
Vision based fire detection
2004
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
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In this paper, we present spectral, spatial and temporal models of fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of fire pixels. ...
The spatial model captures the spatial structure within a fire region. ...
A region that corresponds to fire can be captured in terms of (1) spectral characteristics of the pixels in the region, and (2) the spatial structure defined by their spectral variation within the region ...
doi:10.1109/icpr.2004.1333722
dblp:conf/icpr/LiuA04
fatcat:hqddr3it3nd6hntwer5bewe35u
On Space-Time Resolution of Inflow Representations for Wind Turbine Loads Analysis
2012
Energies
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Using Fourier-based stochastic simulation of inflow turbulence, we first investigate loads for a utility-scale turbine in the neutral atmospheric boundary layer. ...
Efficient spatial and temporal resolution of simulated inflow wind fields is important in order to represent wind turbine dynamics and derive load statistics for design. ...
Nguyen and supporting calculations carried out by him that helped in interpretation of some of the results presented. ...
doi:10.3390/en5072071
fatcat:sxxw3cprbfaojg425huazqkw5m
Page 269 of American Society of Civil Engineers. Collected Journals Vol. 116, Issue EM2
[page]
1990
American Society of Civil Engineers. Collected Journals
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The spectral representation method enables one to simulate stochastic fields and generate their sample functions, resulting in highly accurate spatial sta- tistics because of the periodicity and orthogonality ...
In general, the simulation and generation of sample functions of stochastic fields can be performed by means of: (1) Spectral representation; (2) ARMA (auto-regressive moving average) modeling; and (3) ...
Stochastic response of suspension bridges for various spatial variability models
2016
Steel and composite structures
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The results also show the consistency of the spatial variability models, which have different characteristics, considered in this study. ...
Because each of these models has its own characteristics, it is intended to determine the sensitivity of a suspension bridge due to these losses of coherency models which represent the spatial variability ...
These models are proposed by Harichandran and Vanmarcke (1986) , Abrahamson (1993) , Hindy and Novak (1980) and Uscinski (1977) . Each of these models has its own characteristics. ...
doi:10.12989/scs.2016.22.5.1001
fatcat:ur7k7fa7afc63hu45l2jezndnq
Fully Nonstationary Spatially Variable Ground Motion Simulations Based on a Time-Varying Power Spectrum Model
2014
Mathematical Problems in Engineering
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In the first example, the nonstationary spatially variable ground motion that satisfies the time-frequency characteristics and response characteristics of the original ground motion is simulated by identifying ...
Kameda time-varying power spectrum model. ...
The cross-power spectral density matrix of the spatial stochastic process ( ) is Hermitian and positive definite. ...
doi:10.1155/2014/293182
fatcat:goopyx3phnds5dzbh3xochaefe
A Boosting-Based Spatial-Spectral Model for Stroke Patients' EEG Analysis in Rehabilitation Training
2016
IEEE transactions on neural systems and rehabilitation engineering
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In the proposed method, the spatial-spectral configurations are divided into multiple preconditions, and a new heuristic supervisor of stochastic gradient boost strategy is utilized to train weak classifiers ...
In addition, the spatial patterns (spatial weights) and spectral patterns (bandpass filters) determined by the algorithm can also be used for further analysis of the data, e.g., for brain source localization ...
during rehabilitation, and CSSBP was utilized to extract the most contributed channels
Spatial weight Spectral weight CSSBP Spatial-Spectral Weights group and frequency bands of each subject. ...
doi:10.1109/tnsre.2015.2466079
pmid:26302519
fatcat:mnmqzrikw5dvpfvv2spi23mciy
Euler–Poisson Schemes for Lévy Processes
[chapter]
2017
Trends in Mathematics
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Applications of the stochastic maximum principle to non-concave Hamiltonian and recursive utility maximization are also discussed. ...
A general sufficient maximum principle for optimal control for a system driven by a Markov regime-switching forward and backward jumpdiffusion model is developed. ...
The other factor is an OU process driven by a pure jump Lévy process and models the characteristic spikes observed in such markets. ...
doi:10.1007/978-3-319-51753-7_18
fatcat:7cknhlndwvctdnjnzpefbzs4dm
Spatial sensitivity of seismic hazard results to different background seismic activity and temporal earthquake occurrence models
2011
Soil Dynamics and Earthquake Engineering
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For the contribution of faults, through characteristic earthquakes, both the memoryless Poisson and the time dependent renewal models are utilized. ...
Best estimate seismic hazard maps for PGA and Spectral Accelerations (SA) at 0.2 and 1.0 s are obtained by using the logic tree method. ...
For the contribution of faults, through characteristic earthquakes, both the memoryless Poisson and the time dependent renewal models are utilized. ...
doi:10.1016/j.soildyn.2011.03.009
fatcat:soejp5ynxvcipl4t4n7brqfo2i
Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads
2017
Energies
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To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with ...
We report on stochastic (mean and turbulence) parameters and features estimated from the LES free-stream and waked wind velocity fields; we also describe attempts to understand spatial and temporal features ...
A Stochastic Wake Model We discuss the development of an engineering wake model using a stochastic approach that will utilize statistical and spectral information conveyed through the mean and variance ...
doi:10.3390/en11010053
fatcat:pxatzonhzzdkfok2tlxwuyyvm4
Effect of surface layer stochasticity on seismic ground motion coherence and strain estimates
1997
Soil Dynamics and Earthquake Engineering
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The significance of stochasticity in the characteristics of the surface layers of a site to the resulting spatial variation of seismic ground motions and the seismic ground strains is investigated. ...
In the absence of spatially recorded seismic data at a site, the approach can be utilized for the description of the spatial variation of the motions in the seismic response analysis of buried and above-ground ...
Thus, stochasticity in the soil characteristics ought to be incorporated in spatial variability models. ...
doi:10.1016/s0267-7261(97)00019-5
fatcat:ckx4vv2tljhvjjt6jeyhkse4mi
PATCH-WISE HYPERSPECTRAL IMAGE CLASSIFICATION USING COMPOSITE 3D-2D CONVOLUTIONAL NEURAL NETWORK FEATURE HIERARCHY
2022
International Research Journal of Modernization in Engineering Technology and Science
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The proposed method exploits both the spectral and spatial features of the images very well, while the 2D convolutional layer aids in the spatial feature extraction. ...
However, using only a 2D or 3D convolutional neural network necessarily involves high computational complexity and does not effectively exploit spectral spatial features. ...
In this paper, we aim to overcome the short comings of the previous methods and combine the advantages of both 2D and 3D CNN into composite model to exploit both spectral and spatial feature to their maximum ...
doi:10.56726/irjmets30187
fatcat:hiylw3rxszcx5a7dc3fcwmil74
What makes a small-world network? Leveraging machine learning for the robust prediction and classification of networks
[article]
2024
arXiv
pre-print
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We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. ...
Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures, and forming real-world networks ...
, small world, scale free, spatial, and stochastic block model predictions. ...
arXiv:2403.13215v1
fatcat:x6j6gpwaafdkdiljbcksy55lma
A kernel-based spectral model for non-Gaussian spatio-temporal processes
2002
Statistical Modelling
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equations and their utility in modeling dynamical processes (e.g., Kot et al. 1996) . ...
Third, it scales to high dimensional problems, since it uses spectral decompositions of the spatial kernels and the state process. ...
Pan for providing the cloud data and anonymous reviewers for helpful suggestions on an early draft. ...
doi:10.1191/1471082x02st036oa
fatcat:r3h3az424reevdztqsj2tnlqja
Stochastic coupled simulation of strong motion and tsunami for the 2011 Tohoku, Japan earthquake
2016
Stochastic environmental research and risk assessment (Print)
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This study conducts coupled simulation of strong motion and tsunami using stochastically generated earthquake source models. It is focused upon the 2011 Tohoku, Japan earthquake. ...
Key objectives of this research are: (i) to investigate the sensitivity of strong motion and tsunami hazard parameters to asperities and strong motion generation areas, and (ii) to quantify the spatial ...
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s00477-016-1352-1
pmid:32009849
pmcid:PMC6959406
fatcat:lgyx4gebt5fj7jhyckayvkq5ee
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