123 research outputs found

    A Curriculum Model: Engineering Design Graphics Course Updates Based on Industrial and Academic Institution Requirements

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    Engineering design graphics courses taught in colleges or universities should provide and equip students preparing for employment with the basic occupational graphics skill competences required by engineering and technology disciplines.  Academic institutions should introduce and include topics that cover the newer and more efficient graphics techniques and technologies developed through research by academic institutions and professional organizations as well as information obtained from experienced engineering design graphics practitioners.  This paper presents the systematic approach used at the University of Nebraska at Kearney (UNK), Department of Industrial Technology (ITEC), to update and improve its existing multidiscipline engineering design graphics course.   Twenty five engineering design graphics course syllabi, all from programs accredited by either the Association of Technology, Management, and Applied Engineering (ATMAE) or the Accreditation Board for Engineering and Technology (ABET), were reviewed in this study.  A review of the course syllabi identified 20 of the most commonly taught engineering design graphics topics.  The 20 topics were used to develop a survey instrument subsequently sent to the top 10 employers of ITEC students majoring in Construction Management, Industrial Distribution, and Telecommunications Management.  The results obtained from the employer survey were analyzed and used to update the introductory engineering design graphics course at UNK so that engineering design graphics topics taught are consistent with academia and kept current and relevant to the needs of industry

    222-S Laboratory Quality Assurance Plan. Revision 1

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    Using Machine Learning to Analyze and Classify Echocardiogram Results

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    Color poster with text, charts, and graphs.Heart arrhythmias can be difficult to diagnosis simply from external observation, and many fail to present themselves through concerning symptoms until damage has been done to the heart and the rest of the body. One of the most common tools used to identify these problems is an electrocardiogram (ECG) test, which records impulses from the patient’s heart (heartbeats) and can reflect the state of the individual’s cardiovascular system. ECG test outputs are labored over by cardiologists, which can be time consuming and subject to misinterpretation. An efficient and accurate analysis of an ECG test is critical, as early detection—particularly with more serious arrhythmias—is extremely influential in treatment success. This research explores the potential of using machine learning algorithms to read and analyze echocardiograms based on several input factors. Using a computer algorithm can reduce the amount of time cardiologists spend analyzing and understanding the output of an ECG, while also potentially improving accuracy. This poster details the machine learning algorithms used in the diagnosis, as well as their individual performances.University of Wisconsin--Eau Claire Office of Research and Sponsored Program
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