LEADER 02141nam 2200373 450 001 996280416003316 005 20231020005627.0 010 $a0-7381-2943-7 035 $a(CKB)1000000000035625 035 $a(NjHacI)991000000000035625 035 $a(EXLCZ)991000000000035625 100 $a20231020d2001 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIEEE Std 836-2001 $eIEEE Recommended Practice for Precision Centrifuge Testing of Linear Accelerometers /$fInstitute of Electrical and Electronics Engineers (IEEE) 210 1$aNew York :$cInstitute of Electrical and Electronics Engineers (IEEE),$d2001. 215 $a1 online resource (x, 110 pages) $cillustrations 311 $a0-7381-2942-9 330 $aThis recommended practice provides a guide to the conduct and analysis of precision centrifuge tests of linear accelerometers and covers each phase of the tests, beginning with the planning. Possible error sources and typical methods of data analysis are addressed. The intent is to provide users involved in centrifuge testing with a detailed understanding of the various factors affecting accuracy of measurement, both factors associated with the centrifuge and factors in the data collection process. Model equations are discussed, both for the centrifuge and for a typical linear accelerometer, each with the complexity needed to accommodate the various identified characteristics and error sources in each. An iterative matrix equation solution is presented for deriving the various model equation coefficients for the accelerometer under test from the centrifuge test data. Keywords: accelerometer, accelerometer test, centrifuge, linear accelerometer. 517 $aIEEE Std 836-2001 606 $aAccelerometers 606 $aAccelerometers$xTesting 615 0$aAccelerometers. 615 0$aAccelerometers$xTesting. 676 $a681.2 801 0$bNjHacI 801 1$bNjHacl 906 $aDOCUMENT 912 $a996280416003316 996 $aIEEE Std 836-2001$93575033 997 $aUNISA LEADER 02581nam 2200361zc 450 001 9910557524903321 005 20251116141925.0 010 $a978-3-0365-0792-7$bisbn 010 $a978-3-0365-0793-4$bpdf 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76480 035 $a(oapen)doab76480 035 $a(CKB)5400000000044329 035 $a(EXLCZ)995400000000044329 100 $a20220921d2021 u0 0 101 0 $aeng 135 $aurmn|---annan 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aData Science$eMeasuring Uncertainties$feditado por Carlos Alberto De Bragança Pereira, Adriano Polpo y Agatha Rodrigues 210 $cMDPI - Multidisciplinary Digital Publishing Institute 215 $arecurso en línea (256 p.)$cil 300 $aEste libro es una reimpresión del Special Issue Data Science: Measuring Uncertainties publicadoi previamente en Entropy 330 $aWith the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems. 517 1 $aData Science 606 $aCiencia de datos$2UAMSUB 610 $aBigdata 615 7$aCiencia de datos 700 $aPereira$b Carlos Alberto de Braganc?a$4edt$0755974 701 $aPereira$b Carlos Alberto de Braganc?a$0755974 912 $a9910557524903321 996 $aData Science$94519613 997 $aUNINA