Browsing by Author "John Blondin, Committee Member"

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Comparison of Monte Carlo Metropolis, Swendsen-Wang, and Wolff Algorithms in the Critical Region for the 2-dimensional Ising Model
(2008-03-22) Kyimba, Eloi-Alain; Dean Lee, Committee Chair; John Blondin, Committee Member; David Brown, Committee Member
We measure the efficiency of the Metropolis, Swendsen-Wang, and Wolff algorithms in the critical region as characterized by the correlation time . This correlation time is then used to determine the dynamical critical exponent z using = L with L representing the linear dimension of the 2-dimensional Ising model.
Generation and Characterization of Micron and Sub-micron Sized Particulate using Electrothermal Plasma Source SIRENS
(2003-07-07) Magid, Karen Ruth; Mohamed Bourham, Committee Chair; John Gilligan, Committee Member; John Blondin, Committee Member; Mansoor Haider, Committee Member
The Surface Interaction Research Experiment at North Carolina State (SIRENS) is an electrothermal plasma facility which was recently used to generate particulate for the enhancement and modification of surfaces. The modification of fabrics by surface coating or particle implantation was a specific goal of the work. The SIRENS facility generates a low temperature, high density plasma using an exchangeable liner. The plasma expands from a 4mm diameter capillary into a 180mm diameter expansion cell inside a larger vacuum chamber where collection substrates and diagnostics can be used to collect particulate and analyze the plasma. A variety of conductive and nonconductive materials were used as both sources and substrates. Important for the surface modification applications is to analyze the particulate for composition and size from scanning electron microscope (SEM) images with particle counting software. Also important for the goal of eventually linking the plasma to the particulate generated is to characterize the plasma as it expands into the collection chamber. Therefore, the plasma density and temperature were measured using optical emission spectroscopy at distances 7, 32, 47, and 68cm from the source exit. Shots were performed at similar input energies, approximately 5.7±0.14kJ. Particulate was collected using aluminum, copper, mixed aluminum/copper, Lexan, and Teflon liners. The aluminum, copper, and mixed materials all produced significant amounts of particulate that was visible with an SEM on both metal and fabric substrates. The Lexan and Teflon liners produced particulate that was only visible on fabric substrates. Washing tests showed that some particulate remained on woven fabrics after repeated washings. The SEM images were recorded and analyzed to determine the number and size of the particulate on a substrate. Based on observations of the countable particulate, the particles were approximated as spheres and sized by the diameter determined from the measured area. Particle size ranged in diameter from approximately 0.1µm to 3.5µm, with the average size falling at or slightly below 1µm in diameter. Important observations of aluminum particulate was that much melting occurred so that long streaks of solidified molten material were observed on the metal substrates. The size of the aluminum particles also showed a generally increasing trend with increasing distance from the source. The copper particles did not show the increasing trend and were, on average, smaller at each location. The mixed materials test returned particles composed of both metals, and with average diameters between those of pure aluminum and copper. The Lexan and Teflon particulate on fabric was too difficult to count and size; however one sample exposed to Teflon was more hydrophobic than an unexposed sample of the same fabric. The plasma was also analyzed for temperature and density using optical emission spectroscopy. The results obtained experimentally were also compared to estimations of the plasma parameters based on the electrical and mass difference measurements of the discharge. Using the relative line method to construct Boltzmann plots, the temperatures of aluminum, copper, and Lexan plasmas were determined to be 0.5±0.125eV from the neutral copper lines. This temperature remained constant over the length of the discharge. The electron densities were determined from both Stark broadening of the H-alpha line and the neutral copper lines. The densities were found to be in the range of 10²²-10²⁴ m⁻³, with a more distinct decreasing trend with distance using the densities from the hydrogen line broadening. The parameter estimates from the discharge characteristics returned higher temperatures and lower densities. The estimates are useful for confirming the neutral-dominated and LTE assumptions about the plasma.
Topics in Numerical Relativity: Solving the Initial Value Problem Using Adaptive Mesh Refinement, Examining Evolution Stability Using Spectral Methods, and Finding Apparent Horizons using a Mean Curvature--Level Set Method
(2008-08-08) Lowe, Lisa Lenore; Dr. J. David Brown, Committee Chair; John Blondin, Committee Member; Stephen Reynolds, Committee Member; Gail McLaughlin, Committee Member
ULEDS-SVMs: Upper/Lower Limits and Error Data Supposted Support Vector Machines
(2004-11-18) Sun, Xuejun; Jon Doyle, Committee Chair; John Blondin, Committee Member; Robert Funderlic, Committee Member
A Support Vector Machine, ULEDS-SVMs, was developed for classification in data domain which contains limits or errors. Data with upper or lower limits are different from missing data. They provide constraints at a certain level in data classification and modeling. Data with errors may be recognized as the special case of an upper and a lower limit existing at the two boundaries at an attribute. Such kind of data quality exists widely, from scientific data measurement, to databases resulted from integration and emerge with different quality. Including these data in training rather than dropping them or arbitrarily filling with some value is very desired to provide useful constraints in machine learning. A simple enhanced 1R algorithm is described which may be able to handle data in such a domain, and which principle may be extendable to other machine learning methods. But this is not favored because of its time complicity. Support Vector Machines (SVMs) treatment of the data in such a domain is, however, very promising. We provided the mathematical foundation to treat this kind of problem by recognizing the concepts of feasibilities for training, testing and predicting in SVMs. Algorithms were described by utilizing the theorems. For applying ULEDS-SVMs, we made an integration of a data set in astronomy (CHDF-N) based on Chandra Deep Field (CDF) and Hubble Deep Field (HDF) North observations. Classification of the astronomical objects is interesting for the study of formation and evolution of galaxies in the deep universe. This direction contains the deepest observations made with the largest astronomical facilities currently available. We used CHDF-N as a test bed for the ULEDS-SVMs algorithms application implemented via Matlab. The separation between stars and extragalactic objects gets a 100% accuracy, which would be otherwise more ambiguous in determining the separation plane if limit data in extragalactic class were not included. Training and testing using leave-one-out partition achieved 82% accuracy for separation of galaxies and active galactic nuclei (AGNs). This is better than 72.4% accuracy by using conventional R-log(F_x) plot separation method commonly used in the astronomical community. Prediction rate increased from 49.6% by using conventional SVMs to 75.5% by using ULEDS-SVMs.