University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Active Current Sharing and Source Management in Fuel Cell–Battery Hybrid Power System

Abstract: Fuel cells (FCs) are being considered as a potential alternative in long term to replace diesel/gasoline combustion engines in vehicles and emergency power sources. However, high cost and slow dynamic response of FC still persist as the main hurdles for wider applications. To remedy this problem, an energy storage system with adequate power capacity has to be incorporated. This paper presents a novel control design for FC-battery hybrid power system which enables both active current sharing and power source management control in such hybrid systems. Different hybrid power system structures are investigated and evaluated; dual-converter structure and four modes of operation are defined to provide efficient and sustainable solution to such a hybrid power system. A novel integrated control system with inherent current sharing and generation mode swapping capability is proposed; based on system component status, the control system is able to regulate the output power from each source under different scenarios. The dedicated control system is implemented in a TMS320F2812 DSP, and experimental results for an FC-battery-based uninterruptible power supply are provided to demonstrate the static and dynamic performance of the control system.

Time-Integrated Neutrino Source Searches with 10 Years of IceCube Data.

Abstract: This Letter presents the results from pointlike neutrino source searches using ten years of IceCube data collected between April 6, 2008 and July 10, 2018. We evaluate the significance of an astrophysical signal from a pointlike source looking for an excess of clustered neutrino events with energies typically above ∼1 TeV among the background of atmospheric muons and neutrinos. We perform a full-sky scan, a search within a selected source catalog, a catalog population study, and three stacked Galactic catalog searches. The most significant point in the northern hemisphere from scanning the sky is coincident with the Seyfert II galaxy NGC 1068, which was included in the source catalog search. The excess at the coordinates of NGC 1068 is inconsistent with background expectations at the level of 2.9σ after accounting for statistical trials from the entire catalog. The combination of this result along with excesses observed at the coordinates of three other sources, including TXS 0506+056, suggests that, collectively, correlations with sources in the northern catalog are inconsistent with background at 3.3σ significance. The southern catalog is consistent with background. These results, all based on searches for a cumulative neutrino signal integrated over the 10 years of available data, motivate further study of these and similar sources, including time-dependent analyses, multimessenger correlations, and the possibility of stronger evidence with coming upgrades to the detector.

Microlending in emerging economies: Building a new line of inquiry from the ground up

Abstract: The world's poor may be the last great frontier in international business. International microlenders are increasingly tapping into this emerging opportunity by extending small business loans to millions of borrowers. However, to date, there is very limited understanding of this domain from an international business perspective. Using qualitative case studies developed in Guatemala and the Dominican Republic we probe the causes and consequences of high performance and business failure for microloan recipients. The analysis of these cases led to the development of six testable propositions focused on the behavior of borrowers whose loans populate the portfolios of international microlenders. Our research aims to lay a foundation in international business for future research on microlending.

IPOG-IPOG-D: efficient test generation for multi-way combinatorial testing

Abstract: This paper presents two strategies for multi-way testing (i.e. t-way testing with t>2). The first strategy generalizes an existing strategy, called in-parameter-order, from pairwise testing to multi-way testing. This strategy requires all multi-way combinations to be explicitly enumerated. When the number of multi-way combinations is large, however, explicit enumeration can be prohibitive in terms of both the space for storing these combinations and the time needed to enumerate them. To alleviate this problem, the second strategy combines the first strategy with a recursive construction procedure to reduce the number of multi-way combinations that have to be enumerated. Both strategies are deterministic, i.e. they always produce the same test set for the same system configuration. This paper reports a multi-way testing tool called FireEye, and provides an analytic and experimental evaluation of the two strategies. Copyright © 2007 John Wiley & Sons, Ltd.

A convex formulation for semi-supervised multi-label feature selection

Abstract: Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigendecomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with state-of-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances.