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Estimation of Experimental Errors Using Monte Carlo Analysis in the Introductory Electrical Circuits Laboratory

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Division for Experimentation & Lab-oriented Studies Technical Session 1

Tagged Division

Experimentation and Laboratory-Oriented Studies

Page Count

9

Permanent URL

https://peer.asee.org/30441

Download Count

70

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Paper Authors

biography

Shaghayegh Abbasi University of San Diego

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Shaghayegh Abbasi received her Ph.D. in Electrical Engineering from University of Washington in 2011. In her thesis, titled ‘Integrating top-down and bottom-up nanomanufacturing: Controlling the growth and composition of seeded nanostructures’, an innovative nanomanufacturing method is explored and optimized. Upon graduation, she started her career as Senior System Design Engineer at Lumedyne Technologies. She worked on design, simulation, and testing of a Time Domain Switched (TDS) accelerometer.

Dr. Abbasi joined University of San Diego as an adjunct faculty for Shiley-Marcos School of Engineering in 2014, and is currently a full-time faculty at University of San Diego. She is also doing collaborative research with Bioengineering Department at University of California, San Diego on data analysis of glucose sensors for diabetic patients.

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Ernest M. Kim University of San Diego

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Ernie Kim received his BSEE from the University of Hawaii at Manoa, and MSEE and PhD in Electrical Engineering from New Mexico State University. He has been an electronics engineer at the National Bureau of Standards (now NIST) at the Boulder CO labs where he performed research on precision optical fiber metrology, staff engineer with the Advanced Systems Group of Burroughs Corporation, Manager of Electro-Optics at Ipitek Corporation where he developed early fiber optic CATV systems. Dr. Kim has worked at a number of start-up companies in fiber optic transmission including All Optical Networks, and Lightwave Solutions in San Diego. He joined the University of San Diego Department of Electrical Engineering in 1990. Dr. Kim is a licensed Professional Engineer (EE), and regularly teaches FE and PE exam review courses.

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Thomas F. Schubert Jr. P.E. University of San Diego

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Thomas F. Schubert, Jr. received his B.S., M.S., and Ph.D. degrees in electrical engineering from the University of California, Irvine. He is currently a Professor of electrical engineering at the University of San Diego, San Diego, CA and came there as a founding member of the engineering faculty in 1987. He previously served on the electrical engineering faculty at the University of Portland, and Portland State University, and on the engineering staff at Hughes Aircraft Company. Prof. Schubert is a member of ASEE and IEEE and is a registered professional engineer in Oregon. He is the 2012 winner of the ASEE Robert G. Quinn award for excellence in engineering education.

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Abstract

It is a challenge at times to include probability and statistics in electrical engineering courses. In this student experience, experimental data was compared to statistical analysis in an Introductory Electrical Circuits Laboratory Experiment. Experimental data often are used to supplement engineering analysis as a basis for design. Not all data are equally good: errors are a part of every engineering experiment. Gross errors and statistical errors comprise the two major groups of experimental error. Gross errors are due to mistakes made by the humans that conduct the experiments and tests. An example of a gross error is reading the incorrect scale on a meter. Statistical errors are due to randomness in measurement processes, component values, and equipment inaccuracies. The goal of this experiment in the Introductory Electrical Circuits laboratory was to estimate the uncertainty in experimental measurements and calculated results due to random errors. Single resistor variations in DC electric circuits was used to determine variable uncertainty intervals. The data was used to determine errors in variable values and their effect on measured quantities. Each group’s measured values were recorded and histograms of those values were plotted. They were then compared to the data collected by the entire laboratory section and composite histograms produced. Experimental results were then compared to the results of a MultiSim Monte Carlo circuit simulation. This paper presents the laboratory experiment and procedure, results of student experiments, and assessment of student learning in this required sophomore engineering class and laboratory.

Abbasi, S., & Kim, E. M., & Schubert, T. F. (2018, June), Estimation of Experimental Errors Using Monte Carlo Analysis in the Introductory Electrical Circuits Laboratory Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30441

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