Engineering Commons^™

Exploring The Behavior Repertoire Of A Wireless Vibrationally Actuated Tensegrity Robot, Zongliang Ji

Honors Theses

Soft robotics is an emerging field of research due to its potential to explore and operate in unstructured, rugged, and dynamic environments. However, the properties that make soft robots compelling also make them difficult to robustly control. Here at Union, we developed the world’s first wireless soft tensegrity robot. The goal of my thesis is to explore effective and efficient methods to explore the diverse behavior our tensegrity robot. We will achieve that by applying state-of-art machine learning technique and a novelty search algorithm.

Way-Finding: A New Approach To Studying Digital Communications, William Daniel Glade

Theses and Dissertations

This work further develops the way-finding model first proposed by Pearson and Kosicki (2017) which examines the flow of information in the digital age. Way-finding systems are online systems that help individuals find information—i.e. social media, search engines, email, etc. Using a grounded theory methodology, this new framework was explored in greater detail. Way-finding theory was created using the context of the elaboration likelihood model, gatekeeping theory, algorithmic gatekeepers, and the existence of the filter bubble phenomenon. This study establishes the three basic pillars of way-finding theory: the user’s mindset when accessing way-finding systems, the perception of how popular way-finding …

Multi-Path Automatic Ground Collision Avoidance System For Performance Limited Aircraft With Flight Tests: Project Have Medusa, Kenneth C. Gahan

Theses and Dissertations

A multi-path automatic ground collision avoidance system (Auto-GCAS) for performance limited aircraft was further developed and improved to prevent controlled flight into terrain. This research includes flight test results from the United States Test Pilot School's Test Management Project (TMP) titled Have Multi-Path Escape Decisions Using Sophisticated Algorithms (MEDUSA). Currently, the bomber and mobility air- craft communities lack an Auto-GCAS. The F-16 Auto-GCAS was proven successful for fighter-type aircraft with seven aircraft and eight lives saved from 2014 to 2018. The newly developed and tested Rapidly Selectable Escape Trajectory (RSET) sys- tem included a 5-path implementation which continuously updated at …

Simulation And Piloted Simulator Study Of An Automatic Ground Collision Avoidance System For Performance Limited Aircraft, James D. Carpenter

Theses and Dissertations

The F-16 Automatic-Ground Collision Avoidance System (Auto-GCAS) has been a resounding success since implementation in Nov 2014, saving 8 pilots and 7 aircraft from Controlled Flight into Terrain (CFIT). However, there is no implemented Auto- GCAS for "heavy" performance limited aircraft. This research endeavors to expand on the success of F-16 Auto-GCAS to other aircraft in the Air Force inventory such as the C-130, C-17, and B-1. MIL-STD-1797 classifies performance limited aircraft as large, heavy, and low to medium maneuverability. Using a stitched Learjet-25D model (LJ-25D), an Auto-GCAS algorithm was developed to predict multiple escape-maneuver trajectories, compare these paths to …

Recipe For Disaster, Zac Travis

MFA Thesis Exhibit Catalogs

Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …

An Integrated Approach For Remanufacturing Job Shop Scheduling With Routing Alternatives., Ling Ling Li, Cong Bo Li, Li Li, Ying Tang, Qing Shan Yang

Henry M. Rowan College of Engineering Faculty Scholarship

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, the stochastic natures inherent in the remanufacturing processes complicate its scheduling. This paper undertakes the challenge and presents a remanufacturing job shop scheduling approach by integrating alternative routing assignment and machine resource dispatching. A colored timed Petri net is introduced to model the dynamics of remanufacturing process, such as various process routings, uncertain operation times for cores, and machine resource conflicts. With the color attributes in Petri nets, two types of decision points, recovery routing selection and resource dispatching, are introduced and linked with places …

Rubik's Cube: A Visual And Tactile Learning Of Algorithms And Patterns, Lawrence Muller

Open Educational Resources

This is a classroom activity report on teaching algorithms as part of a second course in computer programming. Teaching an algorithm in an introductory level programming class is often a dry task for the instructor and the rewards for the student are abstract. To make the learning of algorithms and software more rewarding, this assignment employs a Rubik’s cube.

Dynamic Light Scattering Optical Coherence Tomography To Probe Motion Of Subcellular Scatterers., Nico J J Arezza, Marjan Razani, Michael C Kolios

Medical Biophysics Publications

Optical coherence tomography (OCT) is used to provide anatomical information of biological systems but can also provide functional information by characterizing the motion of intracellular structures. Dynamic light scattering OCT was performed on intact, control MCF-7 breast cancer cells and cells either treated with paclitaxel to induce apoptosis or deprived of nutrients to induce oncosis. Autocorrelations (ACs) of the temporal fluctuations of OCT intensity signals demonstrate a significant decrease in decorrelation time after 24 h in both the paclitaxel-treated and nutrient-deprived cell groups but no significant differences between the two groups. The acquired ACs were then used as input for …

The Role Of Artificial Intelligence In Business Decision Making, Chase Rainwater

Operations Management Presentations

When we think of artificial intelligence, we often are drawn to the self-driving cars, voice-based home technologies and automated online interactions that fill the news and drive our daily activities. However, the root of these advancements, machine learning, is a predictive analytics technique that has much broader applicability. With the age of “big data” and the buzz around “data science” continuing to grow, decision-makers are asking themselves if emerging technologies, such as machine learning, can help improve business processes.

In this seminar we will demystify the fundamental concepts that comprise machine learning. The differences between supervised and unsupervised learning, as …

Icdar 2019 Time-Quality Binarization Competition, Rafael Dueire Lins, Ergina Kavallieratou, Elisa Barney Smith, Rodrigo Barros Bernardino, Darlisson Marinho De Jesus

Electrical and Computer Engineering Faculty Publications and Presentations

The ICDAR 2019 Time-Quality Binarization Competition assessed the performance of seventeen new together with thirty previously published binarization algorithms. The quality of the resulting two-tone image and the execution time were assessed. Comparisons were on both in "real-world" and synthetic scanned images, and in documents photographed with four models of widely used portable phones. Most of the submitted algorithms employed machine learning techniques and performed best on the most complex images. Traditional algorithms provided very good results at a fraction of the time.

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …