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Xiao B.,100 Institute Rd | Li K.,Oakland University | Rong Y.,100 Institute Rd
Strain | Year: 2011

Residual stress calibration coefficients are used to calculate residual stresses from the measured strains relieved during hole-drilling. The current residual stress measurement practice interpolates the published non-dimensional coefficients for a given measurement condition. Errors are always introduced from the interpolation. In addition, the calibration coefficients vary with respect to factors such as sample geometry dimensions, radius, offset and incline of the drilled hole, and material properties as shown in our sensitivity studies and other researchers' work. This paper presents a better solution that is to calculate the calibration coefficients for each specific measurement. A set of routines coded in Python language for Finite Element software ABAQUS is developed to address our sensitivity studies of these factors. With these automatic routines, a technician who is not familiar with Finite Element and programming can conveniently obtain the calibration coefficients for his measurement conditions and residual stresses automatically. Because coefficients are determined directly by Finite Element Analysis (FEA), dimensionless coefficients are not needed anymore; instead, a modified integral method is proposed and implemented. An experiment is conducted to demonstrate the practical procedures of measuring residual stresses using resistance strain rosette and calibration coefficients obtained with this set of routines. Bending stresses on a narrow and thin beam are measured using this set of routines and compared to the theoretical results and the stress obtained by interpolating non-dimensional coefficients. © 2009 Blackwell Publishing Ltd. Source


DeFreitas T.,Worcester Polytechnic Institute | DeFreitas T.,100 Institute Rd | Saddiki H.,University of Massachusetts Amherst | Flaherty P.,100 Institute Rd | And 2 more authors.
BMC Bioinformatics | Year: 2016

Background: Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile. Results: To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an O ( log n ) expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 105 samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. Conclusions: GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information. © 2016 DeFreitas et al. Source

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