Search results for "DIMENSION"
showing 10 items of 2766 documents
Domain wall energy in quasi-one-dimensional Fe/W(110) nanostripes
2003
The magnetic susceptibility in Fe/W(110) nanostripes decreases exponentially with increasing temperature according to an Arrhenius law which indicates a quasi-one-dimensional behavior. The interface energy of the Arrhenius law corresponds to the domain wall energy of a domain wall across a single stripe, separating fluctuating regions of homogeneous magnetization. The domain wall energy increases linearly with the width of the stripes, revealing a negative offset which we attribute to boundary effects. Domain wall energies have been determined for Fe/W(110) nanostripes coated with Au and Pd and are compared to values for uncoated Fe/W(110) nanostripes in ultrahigh vacuum.
MBE growth and properties of low-density InAs/GaAs quantum dot structures.
2011
We present the results of a comprehensive study carried out on morphological, structural and optical properties of InAs/GaAs quantum dot structures grown by Molecular Beam Epitaxy. InAs quantum dots were deposited at low growth rate and high growth temperature and were capped with InGaAs upper confining layers. Owing to these particular design and growth parameters, quantum dot densities are in the order of 4-5x109 cm-2 with emission wavelengths ranging from 1.20 to 1.33 µm at 10 K, features that make these structures interesting for single-photon operation at telecom wavelength. High resolution structural techniques show that In content and composition profiles in the structures depend on …
Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine
2013
Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial ne…
Multiple criteria assessment of methods for forecasting building thermal energy demand
2020
Abstract Nowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of s…
Regularized RBF Networks for Hyperspectral Data Classification
2004
In this paper, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dimensionality are tested for six images containing six crop classes. Also, regularization, sparseness, and knowledge extraction are paid attention.
Magnetorotational Collapse of Supermassive Stars: Black Hole Formation, Gravitational Waves and Jets
2017
We perform MHD simulations in full GR of uniformly rotating stars that are marginally unstable to collapse. Our simulations model the direct collapse of supermassive stars (SMSs) to seed black holes (BHs) that can grow to become the supermassive BHs at the centers of quasars and AGNs. They also crudely model the collapse of massive Pop III stars to BHs, which could power a fraction of distant, long gamma-ray bursts (GRBs). The initial stellar models we adopt are $\Gamma = 4/3$ polytropes seeded with a dynamically unimportant dipole magnetic field (B field). We treat initial B-field configurations either confined to the stellar interior or extending out from the interior into the stellar ext…
Search for microscopic black holes in a like-sign dimuon final state using large track multiplicity with the ATLAS detector
2013
A search is presented for microscopic black holes in a like-sign dimuon final state in proton-proton collisions at √s= 8 TeV. The data were collected with the ATLAS detector at the Large Hadron Collider in 2012 and correspond to an integrated luminosity of 20.3 fb-1. Using a high track multiplicity requirement, 0.6±0.2 background events from Standard Model processes are predicted and none observed. This result is interpreted in the context of low-scale gravity models and 95% CL lower limits on microscopic black hole masses are set for different model assumptions.
Search for strong gravity signatures in same-sign dimuon final states using the ATLAS detector at the LHC
2012
A search for microscopic black holes has been performed in a same-sign dimuon final state using 1.3 fb[superscript −1] of proton–proton collision data collected with the ATLAS detector at a centre of mass energy of 7 TeV at the CERN Large Hadron Collider. The data are found to be consistent with the expectation from the Standard Model and the results are used to derive exclusion contours in the context of a low scale gravity model.
What Flow Conditions are Conducive to Banner Cloud Formation?
2016
Abstract Banner clouds are clouds that are attached to the leeward slope of a steep mountain. Their formation is essentially due to strong Lagrangian uplift of air in the lee of the mountain. However, little is known about the flow regime in which banner clouds can be expected to occur. The present study addresses this question through numerical simulations of flow past idealized orography. Systematic sets of simulations are carried out exploring the parameter space spanned by two dimensionless numbers, which represent the aspect ratio of the mountain and the stratification of the flow. The simulations include both two-dimensional flow past two-dimensional orography and three-dimensional fl…