Engineering Commons^™

Curriculum Optimization Via Activity-On-Node Network Modeling, Caroline Rhomberg

Industrial Engineering Undergraduate Honors Theses

University degree plans must be carefully planned so that they allow students the best chance of succeeding. Although for the better, with the advancement of technology and its incorporation into the classroom, it can be argued that the complexity and difficulty of some long-established engineering core classes has changed. With this trend certain combinations of engineering courses have become unfavorable in terms of course withdrawal and fail rates stemming from the interaction of course challenges. A wealth of data has been collected on this topic and will be utilized in this project. As one can imagine, the probability of success …

Golng Off The Grid: Optimizing Solar Renewable Energy Systems At Remote Locations To Minimize Logistics Requirements, Increase Sustainability, And Strengthen Energy Assurance, Nathanael J. Thomsen

Theses and Dissertations

Grid-based electrical infrastructure is unavailable at many remote locations including developing nation communities, isolated construction sites, and military contingency bases. Powering these locations with diesel generators requires regular fuel resupply, resulting in increased costs, environmental impacts, and burdensome logistics—making generators an obstacle for energy resiliency and sustainability. This research examines using solar renewable energy systems to replace generators at remote locations and presents a multi-objective optimization model that minimizes logistics variables. Replacing a single deployed generator would save over 500,000 gal of fuel annually, eliminating the need for 100 fuel tanker deliveries.

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …