AIERO: An algorithm for identifying engineering relationships in ontologies

Semantic technologies are playing an increasingly popular role as a means for advancing the capabilities of knowledge management systems. Among these advancements, researchers have successfully leveraged semantic technologies, and their accompanying techniques, to improve the representation and search capabilities of knowledge management systems. This paper introduces a further application of semantic techniques. We explore semantic relatedness as a means of facilitating the development of more “intelligent” engineering knowledge management systems. Using semantic relatedness quantifications to analyze and rank concept pairs, this novel approach exploits semantic relationships to help identify key engineering relationships, similar to those leveraged in change management systems, in product development processes. As part of this work, we review several different semantic relatedness techniques, including a meronomic technique recently introduced by the authors. We introduce an aggregate measure, termed “An Algorithm for Identifying Engineering Relationships in Ontologies,” or AIERO, as a means to purposely quantify semantic relationships within product development frameworks. To assess its consistency and accuracy, AIERO is tested using three separate, independently developed ontologies. The results indicate AIERO is capable of returning consistent rankings of concept pairs across varying knowledge frameworks. A PCB (printed circuit board) case study then highlights AIERO’s unique ability to leverage semantic relationships to systematically narrow where engineering interdependencies are likely to be found between various elements of product development processes

UC-59 Analyzing Concentration Levels in Online Learning with Facial Values

Can deep learning models accurately predict whether an individual is focused or distracted on a task in order to improve learning efficiency? In the context of online learning with the use of a webcam, this project is aimed at detecting concentration levels of students to potentially assist with improving learning efficiency. Machine learning technologies have been utilized to evaluate students’ facial expression and eye movements to identify whether a student is focused or distracted. The machine learning branch that is employed is a supervised learning model. This supervised learning model makes predictions based on given input features. A total of 6 different models were employed. 4 of those models employed collected eye data. The other two models employed the use of facial and eye data to predict concentration. Ultimately, the eye model accuracy hovered between 50% and 56% accuracy in prediction, with a significant amount of loss. The eye models with attention provided the best accuracy and loss rates out of the four eye models. Secondly, the facial and eye models also hovered right around 50% accuracy with significant loss of around 3.8 and 3.7. The reported results suggest that the data was inaccurate or insufficient in some models to accurately predict concentration levels in an individual. Given a larger collection and more consistent data, the reported results would provide to be more accurate at predicting concentration.Advisors(s): Dr. Linh Le (Sponsor/Project Owner) Dr. Ying Xie (Sponsor/Project Owner)Topic(s): Artificial IntelligenceIT 498

Further development of the cleanable steel HEPA filter, cost/benefit analysis, and comparison with competing technologies

We have made further progress in developing a cleanable steel fiber HEPA filter. We fabricated a pleated cylindrical cartridge using commercially available steel fiber media that is made with 1 {mu}m stainless steel fibers and sintered into a sheet form. Test results at the Department of Energy (DOE) Filter Test Station at Oak Ridge show the prototype filter cartridge has 99.99% efficiency for 0.3 {mu}m dioctyl phthalate (DOP) aerosols and a pressure drop of 1.5 inches. Filter loading and cleaning tests using AC Fine dust showed the filter could be repeatedly cleaned using reverse air pulses. Our analysis of commercially optimized filters suggest that cleanable steel HEPA filters need to be made from steel fibers less than 1 {mu}m, and preferably 0.5 {mu}m, to meet the standard HEPA filter requirements in production units. We have demonstrated that 0.5 {mu}m steel fibers can be produced using the fiber bundling and drawing process. The 0.5 {mu}m steel fibers are then sintered into small filter samples and tested for efficiency and pressure drop. Test results on the sample showed a penetration of 0.0015% at 0.3 {mu}m and a pressure drop of 1.15 inches at 6.9 ft/min (3.5 cm/s) velocity. Based on these results, steel fiber media can easily meet the requirements of 0.03% penetration and 1.0 inch of pressure drop by using less fibers in the media. A cost analysis of the cleanable steel HEPA filter shows that, although the steel HEPA filter costs much more than the standard glass fiber HEPA filter, it has the potential to be very cost effective because of the high disposal costs of contaminated HEPA filters. We estimate that the steel HEPA filter will save an average of $16,000 over its 30 year life. The additional savings from the clean-up costs resulting from ruptured glass HEPA filters during accidents was not included but makes the steel HEPA filter even more cost effective. We also present the results of our evaluation of competing technologies with metallic and ceramic powder filters, ceramic fiber falters, and reinforced glass fiber filters. In general, the metallic and ceramic powder filters have pressure drops in excess of 25 inches of water for HEPA grade efficiencies and are therefore not viable candidates. The ceramic fiber filters cannot meet the HEPA efficiency because the fiber diameters are too large. The reinforced glass fiber filter is a promising candidate for the cleanable HEPA filter but requires additional development and testing to confirm its potential to be repeatedly cleaned. This report is based upon material extracted from a DOE technical review of the Mixed Waste Integrated Program and from the final report of a systems analysis of cleanable steel HEPA filters

Resonant Two-body D Decays

The contribution of a $K^*(1430)$ $0^+$ resonance to $D^0\to K^-\pi^+$ is calculated by applying the soft pion theorem to $D^+ \to K^* \pi^+$, and is found to be about 30% of the measured amplitude and to be larger than the $\Delta I=3/2$ component of this amplitude. We estimate a 70% contribution to the total amplitude from a higher $K^*(1950)$ resonance. This implies large deviations from factorization in D decay amplitudes, a lifetime difference between D^0 and D^+, and an enhancement of $D^0-\bar D^0$ mixing due to SU(3) breaking.Comment: To be published in Physical Review Letters, some corrections, references update

Heavy Meson Decays into Light Resonances

We analyse the Lorentz structures of weak decay matrix elements bewteen meson states of arbitrary spin. Simplifications arise in the transition amplitudes for a heavy meson decaying into a light one via a Bethe-Salpeter approach which incorporates heavy quark symmetry. Phenomenological consequences on several semileptonic, nonleptonic and FCNC induced decays of heavy flavoured mesons are derived and discussed.Comment: 20 RevTex pages, Preprint # UTAS-PHYS-94-0

Radiogenic and Muon-Induced Backgrounds in the LUX Dark Matter Detector

The Large Underground Xenon (LUX) dark matter experiment aims to detect rare low-energy interactions from Weakly Interacting Massive Particles (WIMPs). The radiogenic backgrounds in the LUX detector have been measured and compared with Monte Carlo simulation. Measurements of LUX high-energy data have provided direct constraints on all background sources contributing to the background model. The expected background rate from the background model for the 85.3 day WIMP search run is $(2.6\pm0.2_{\textrm{stat}}\pm0.4_{\textrm{sys}})\times10^{-3}$~events~keV$_{ee}^{-1}$~kg$^{-1}$~day$^{-1}$ in a 118~kg fiducial volume. The observed background rate is $(3.6\pm0.4_{\textrm{stat}})\times10^{-3}$~events~keV$_{ee}^{-1}$~kg$^{-1}$~day$^{-1}$, consistent with model projections. The expectation for the radiogenic background in a subsequent one-year run is presented.Comment: 18 pages, 12 figures / 17 images, submitted to Astropart. Phy