, Page 14.575.2such as seniors and juniors, to supervise and mentor younger college students. A faculty advisorPage 14.575.3Page 14.575.4 The amount of students who choose Engineering Technology majors within thedepartment has increased from 12 to 14 percent and is expected to continue in this trend (seeTable 2). Table 2: Percent Engineering Technology Per Academic Semester F 08 S 05 14% 12% F 05 S 08
temperature (and with that performance)of solar modules, is the airflow around them. With only average daily and not hourly wind data available from anearby town, this data may only be used to verify a visual trend of any impact stronger winds may have onmodule temperature. Figure 4 gives an example of this data, the trend-line creating a very clear divide on highinsolation days, between higher winds (red- above 2.65m/s) and lower winds (blue- less than 2.65m/s).This didnot hold up for some other months, as seen in Figure 5. While seemingly random, all high winds for themonth(>=3m/s – Figure 6) did correlate to lower temperatures (though not vice versa). This is expected to bedue to the necessity for much stronger winds in order to cool the
Renewable Energy Systems Courses D. J. Burnham,⋆ J. C. Campbell,⋆ S. Santoso,⋆ A. Compean,⋆⋆ J. Ramos⋆⋆1 IntroductionIn recent years wind turbine technologies have made significant advances, and more than 30 U.S.states have implemented aggressive renewable portfolio standards. These standards require thatelectric utilities obtain 10% to 30% of their energy from renewable sources, with target datesbetween 2020 and 2030.1 In support of this effort the U.S. Department of Energy is consideringthe viability of wind energy to supply up to 20% of nation’s electricity by 2030.2 In addition tothe technical challenge of integrating wind power into the national grids, another criticalchallenge in the 20% wind power scenario involves preparing the
A Hybrid Approach to Evaluate the Performance of Engineering Schools School of Engineering University of Bridgeport Bridgeport, CT 06604 ABSTRACTScience and engineering (S&E) are two disciplines that are highly receptive to the changes indemand for products and services. These disciplines can either be leading in nature, viz., they createthe demand in the market (push) for new products and/or services, or can adopt the changes causedby the varying market conditions (pull). Regardless of the reason, both science and engineering havethe responsibility to be compatible
and prevention.Simultaneously, business and industry are increasingly seeking graduates withappropriate background and training in this emerging and lucrative field of biomedicalengineering and technology. The United States Labor Department supports this area ofconcentration by forecasting a job growth of 31.4 percent through 2010, double the ratefor all other jobs combined. The aging U. S. population as well as the increase demandfor improved medical devices and systems, are contributing to this increase in demand.Women will be motivated so that the stagnant or even decreasing 20 percent level ofenrollment in engineering and technology fields nationwide may be lifted byunderstanding that the careers in this area are exciting, rewarding
various learning styles by individual learners can be catered to by drifting awayfrom typical scholastic activities: lecture – reading – theory-reinforcing calculation exercises –examination. The 21 learning activities listed in Table 2 can address most of the eight MI.Course evaluations will be undoubtedly more favorable if every student finds his/her favoriteniche activities during the course.QFD for Defining Course ActivitiesQuality Function Deployment (QFD) technique parallels engineering procedures used forexamining specifications and performances of products and processes 10, 11. Developed in the1970’s in Japan and used in Kobe Shipyard of Mitsubishi Heavy Industries, QFD methodologystemmed from quality improvement tables and was originally
teacher assigned ID andpassword (Fig. 5). After that s/he can select any module s/he likes to work on (Fig. 6), in thisstep, the program will tell the student how many time s/he has already tried. If the maximumtrial time is met, the system will not allow her/him to continue on this particular module. Page 14.643.7 Figure 4: Programming logic for accessing the database. Figure 5: GIVE model log in system Figure 6: Module selectionFigure 7(a) gives the overall picture of the course module. In Fig. 7(b) a movie clipcorresponding to the question is playing. In the movie clip, important aspects are highlighted
19 20 10 0 s n lls c
in most texts ≠ A numerical approach with Microsoft Excel®74 ≠ Excel/VBA based simulation75Trouble SpotsTrouble spots for this course can include: ≠ Students not understanding the physical meaning of the Laplace variable “s”. This will likely remain a mystery. Instead, focus on how conservation laws in the Laplace domain can be arranged to yield key information about process behaviors through parameters like gains and time constants. ≠ Bringing in computing tools too early or too late. Students must understand the how and why before actively developing models with software like Simulink. The appropriate time to introduce them will depend on your curriculum, but probably should be after
roots of the beams. Since these sensors are an integral part of the beams,they would experience the same levels of stress / strain. At a certain level of deformation, theywill generate the necessary level of voltage to activate the switch(es) for deploying the airbag(s). Page 14.795.2 Proceedings of the 2009 American Society for Engineering Education Annual Conference & Exposition Copyright © 2009, American Society for Engineering Education m – 1 = Hanging (proof) mass #1 m – 2
. Page 14.259.8 [6] James E. Corter, Jeffrey V. Nickerson, Sven K. Esche, and Constantin Chassapis. Remote ver- sus hands-on labs: A comparative study. 34th ASEE/IEEE Frontiers in Education Conference, 2006. [7] D. Deniz, A. Bulancak, and G. Ozcan. A novel approach to remote laboratories. ASEE/IEEE Frontiers in Education Conference, pages T3E–8–12, November 5-8 2003. [8] S. K. Esche, C. Chassapis, J. W. Nazalewicz, and D. J. Hromin. A scalable system architecture for remote experimentation. Proceedings of the 32nd ASEE IEEE Frontiers in Education Conference, Boston. MA, November 6-9 2002. [9] L. Hesselink et al. Cyberlab: A new paradigm in distant learning. NSF Workshop: Learning from the Net: The Leading Edge in Internet
FutureProject’ was recently added to the curriculum for the College Institute ES 100 taught to12th graders at Thomas S. Wootton High School in Rockville, Maryland. This projectinvolves motivating students to develop an academic map/career plan for themselves byobserving and interviewing successful engineers in different fields, creating overviewprofiles for each of them, and studying these profiles to determine how they connect withtheir career intentions.A detailed explanation of the Engineering your Future Project and how it fits into the ES100 curriculum is given in this paper. Results from qualitative and quantitative evaluationof the project will be presented. In addition, since the project was also added to one of theES 100 sections taught to
unchanged pre to post.Table 1: All Students Pre to Post Comparison Pre Post Pre Post S Effect S Effect#1 Mean2 Mean2 Size3 Opinion4 Change5 #1 Mean2 Mean2 Size3 Opinion4 Change5 Interest in learning Relationship to Math and Science1 2.5 2.43 0.06 Disagree Extreme *6 6 3.93 3.98 0.05 Agree Extreme12 3.09 3.16 0.07 Agree Extreme 11 4.17 4.05
Page 14.632.10Figure 10. Filtered signal in the frequency domain.n= 128;subplot(2,1,1);plot(t(1:n),x( 1:n));grid on; axis([0 8e-3 -3 3]);xlabel('time(s)');ylabel('Amplitude ');title('Original and Filtered Signal ');subplot(2,1,2);plot(t(1:n),y(1:n));grid on; axis([0 8e-3 -3 3]);xlabel('times(s)');ylabel('Amplitude '); Original and Filtered Signal 3 2 1 Amplitude 0 -1 -2 -3 0 1 2 3 4 5 6 7 8 time(s) -3
(two prospective freshmen females, two freshman minority student, one juniorminority student, and one sophomore). The duration of the study was eight weeks. A rubric forresearch notebooks was developed and discussed. The rubric has a potential for usage as aneffective tool to map creativity instances during team activities in a research project on design.Acknowledgement The first author would like to acknowledge the grant from the ?? program at ?? University. Page 14.1304.4Bibliography1. Ekwaro-Osire S, Orono PO, "Design notebooks as indicators of student participation in team activities," in Proceedings of 2007 Frontiers in Education
AC 2009-389: DEVELOPMENT OF A SOLID MODELING COURSE FORELECTRICAL AND COMPUTER ENGINEERING TECHNOLOGY (ECET)STUDENTSFredrick Nitterright, Pennsylvania State University, Erie Mr. Fred Nitterright is a lecturer in engineering at Penn State Erie, The Behrend College. He received the A. A. S. in Mechanical Drafting and Design in 1989 from Westmoreland County Community College, the B. S. in Mechanical Engineering Technology in 1991 from Penn State Erie, The Behrend College, and the M. S. in Manufacturing Systems Engineering from the University of Pittsburgh in 1998. Mr. Nitterright is a senior member of the Society of Manufacturing Engineers (SME), and a member of the American Society for
and Bowers (1997) of studentsstudying physics found that reading is, in fact, more important than hearing.IntroductionHaving been challenged by a member of the public—specifically a K-12 school teacher—toprovide authoritative source(s) of the STATEMENT, what was envisioned as a simple search andproof would ultimately reveal a lack of evidence for the cited statistics. The STATEMENT beingreferred to here is that people (or students) learn (or recall/remember): • 10% of what they read • 20% of what they hear • 30% of what they see • 50% of what they hear and see • 70% of what they say (and write) • 90% of what they say as they do a thingThere are various forms and permutations of the STATEMENT found in published
collected prior to the beginning ofthe first year of study to answer the following research questions:- To what extent do the data collected for this study support the Gender Similarity Hypothesis?- For characteristics which show a difference, is there evidence that these differences are decreasing over this four year period?Background:Prior to the 1980’s, theories that male students were superior to female students in mathematicalability were widely accepted. For example, in 1974, Maccoby and Jacklin5 wrote “Boys excel inmathematical ability” under the heading “Sex Differences That Are Fairly Well Established.” Page 14.612.2They state that
temperature sensor produces an output voltage of 10mV/°F. Figure 6. Data sample after analysis.Figure 6 shows a sample of actual student analyzed data corresponding to the same intervalshown previously in the raw data of Figure 5. Student analyzed data for the full daylight periodis plotted in Figure 7. The electrical engineering technology students were asked to determinethe time(s) of the maximum solar panel and battery voltage and current as well as the maximumsolar panel output power. Figure 7 also shows this tabulated information. The students werealso required to write a summary of the events that are detectible in the data. Events such aswhen the audio system was used, time of sunrise, sunset, cloudy periods, etc
view the inside of the boxes the students werepleased and somewhat surprised.The question of whether a fractional factorial design could have been used was aunanimous ‘yes’. A one half or even one quarter design would have yielded verysimilar results.This opinion was validated by comparing the main effect plots for the fullfactorial and ½ fractions DOE’s. The main effects for the full factorial and ½fraction are shown below in figures 3 and 4. Main Effects Plot (data means) for S/M/E Blue Green 60 Mean of Stephanie/Mark/Erynne 50
Module Module Course Development AA-0001 (Module 1; S) AA-0002 (Module 2; S+H) AA-0003 (Module 3,4,5 and 8; S + H) AA-0004/BB-0001(Module 3, 4 and 6; S+H) BB-0002 (Module 6, 7; S) BB-0003 (Module 6, 7; S) Note: S (Software); H(Hardware) Quasi Web Based Delivery Mechanism 1. Synchronized: Face to Face Lectures (p%) 2. Asynchronized: Audio/Video Embedded in Power Point Slides (1-P)% Summer workshop/Seminar At the e-Manufacturing
., Barnes, S., Coe, S., Reinhard, C., and Subramania, K., “Globalization and the Undergraduate Manufacturing Engineering Curriculum,” 2002, ASEE Journal of Engineering Education 91, pp. 255-261.[2] National Association of Manufacturing, “Keeping America Competitive: How A Talent Shortage Threats U.S. Manufacturing,” a white paper on http://www.nam.org/~/media/Files/s_nam/docs/226500/226411.pdf.ashx, accessed October 6, 2008.[3] Bee, D., and Meyer, B., “Opportunities and Challenges for Manufacturing Engineering,” 2007, Proceedings of the 2007 ASEE Annual Conference & Exposition, June 24-27, 2007, Honolulu, HI.[4] Waldorf, D., Alptekin, S., and Bjurman, R., “Plotting a Bright Future for Manufacturing
ambient energysource to storage device is needed to avoid voltage drops in the wires. For the purpose of placingstorage devices closer to every energy source, a detailed routing investigation will be conducted.References[1] Hinrics A. R., Kleinbach M. (2002). Energy: Its Use and the Environment. 3rd Edition,Orlando, Florida: Harcourt, Inc.[2] Yildiz, F., Zhu, J., & Pecen, R., Guo, L. (2007). Energy Scavenging for Wireless Sensor Page 14.1050.12Nodes with a Focus on Rotation to Electricity Conversion, American Society of EngineeringEducation, AC 2007-2254:[3] Rabaey, J. M., Ammer, M. J., Da Silva Jr, J. L., Patel, D., & Roundy, S. (2000
shown in Figure 6. Page 14.98.9 Figure 6. PSpice schematics for the simulation.The second model is based on the calculated system transfer function shown below and used forthe MATLAB simulation. 1 s 2 ∗L3 − L2 + − s ∗R2 − R3 + −H (s) ? C L
was video-taped. Students were asked to verbalize what theywere doing as they took the practical examination and, if necessary, were prompted by the TA.Coding is currently being developed to analyze these videos.The second technician aspect students were trained in was analyzing and graphing acquired data.Students were shown how to upload data from the test instrumentation to LabView then exportthis data to Matlab. Data was presented in the form of Smith charts, and graphs of S parameters.Students were also shown how to distinguish theoretical from measured data. The measurementsperformed by students and data presentation assignments were designed to illustrate limitationsof the measurement instrumentation. Specific data analysis tasks
temperature probe provides an exponential curve (Newton’s law of cooling), the displacement of a falling ball onto the motion detector provides a power (quadratic) function and the force sensor can be exited linearly.Three sensors (i.e., temperature, force, and motion detection) are used to develop this activity.The system setup and LabVIEW output are shown in Figures 3 and 4, respectively.Figure 3. DAQ activity setup. The analog signals from temperature and force sensors are filteredout from noise, amplified and converted to digital (0’s and 1’s) in the Vernier SensorDAQ(middle). The Motion Detector implements these conversions internally
developed to administer this new responsibility, 2) the experiences of the first three years of program evaluator visits, 3) the institutions with ABET EAC-accredited multidisciplinary engineering programs, 4) the number and names of the multidisciplinary engineering program(s) at each institution, 5) the ABET EAC accreditation history of these programs, 6) a look ahead at the projected future evaluator workload, and 7) other issues related to this new accreditation role and to recent changes in the process.IntroductionOne of the significant distinctions of a substantial number of baccalaureate engineering programsis that they intentionally do not align naturally with a currently so-called “traditional discipline”(such as
, engineering economy, discretemathematics, and probability and statistics. Using the brief description that Industrial Engineersimprove processes, students were taught to think of equations as models for processes. Theindependent variable(s) is (are) the input(s) to the process and the dependent variable(s) is (are)the output(s) from the process.Systems of linear equations, matrices, and truth tables from discrete mathematics were taught tohelp prepare students for the computer programming courses and the linear algebra course taughtby the Mathematics department. Both of these courses were prerequisites for the first operationsresearch course taught in the Industrial Engineering degree program. It was emphasized thatlinear programming was mathematical
Technology. Georgia Tech’s record of training Armyengineers and aviators since WWI was probably the deciding factor, and it was with theinitiative of Army officers deputed to the Guggenheim Foundation, that the final schoolselection was madex. A grant of $300,000 was used to construct a building around anine-foot wind tunnel and invest in bonds for the future. In the following sections moredetails on the evolution of each of the seven schools to their present state will bepresented.New York University As mentioned in the introduction New York University (NYU) was the firstGuggenheim School, and the recipient of the largest grant. NYU developed excellentfacilities and was a renowned center for years. In the 1940’s it was joined by its cross-town
of the internet and supporting programs, many institutions of higher learning areexamining the possibility of offering at least some classes over the internet. Whilecorrespondence courses by mail have been offered for many decades, the immediate responseand information bandwidth of the internet offer the possibility of real-time remote interaction,electronic homework and exams, and instant streaming video and audio not available by mail.With proper support, web-based instruction computer programs such as Desire2Learn (D2L) 1, acommercial classroom management system (similar to Blackboard, etc.), allows remote studentsto password-access materials on their own schedule while requiring online discussions atspecified times, at the instructor‟s