Computer Engineering Commons^™

Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich

Doctoral Dissertations

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space.

Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. …

Using Eye And Head Movements As A Control Mechanism For Tele-Operating A Ground-Based Robot And Its Payload, Kathryn C. Hicks

Computational Modeling & Simulation Engineering Theses & Dissertations

To date, eye and head tracking has been used to indicate users' attention patterns while performing a task or as an aid for disabled persons, to allow hands-free interaction with a computer. The increasing accuracy and the reduced cost of eye- and head-tracking equipment make utilizing this technology feasible for explicit control tasks, especially in cases where there is confluence between the visual task and control.

The goal of this research was to investigate the use of eye-tracking as a more natural interface for the control of a camera-equipped, remotely operated robot in tasks that require the operator to simultaneously …

An Intelligent Attitude Determination And Control System Concept For A Cubesat Class Spacecraft, Jeremy Straub

Jeremy Straub

An attitude determination and control system (ADCS) is used to orient a spacecraft for a wide variety of purposes (e.g., to keep a camera facing Earth or orient the spacecraft for propulsion system use). The proposed intelligent ADCS has several key features: first, it can be used in multiple modes, spanning from passive stabilization of two axes and unconstrained spin on a third to three-axis full active stabilization. It also includes electromagnetic components to ‘dump’ spin from the reaction wheels. Second, the ADCS utilizes an incorporated autonomous control algorithm to characterize the effect of actuation of the system components and, …

Towards Real-Time, On-Board, Hardware-Supported Sensor And Software Health Management For Unmanned Aerial Systems, Johann M. Schumann, Kristin Y. Rozier, Thomas Reinbacher, Ole J. Mengshoel, Timmy Mbaya, Corey Ippolito

Ole J Mengshoel

Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo

MODVIS Workshop

Computer vision research rarely makes use of symmetry in stereo reconstruction despite its established importance in perceptual psychology. Such stereo reconstructions produce visually satisfying figures with precisely located points and lines, even when input images have low or moderate resolution. However, because few invariants exist, there are no known general approaches to solving symmetry correspondence on real images. The problem is significantly easier when combined with the binocular correspondence problem, because each correspondence problem provides strong non-overlapping constraints on the solution space. We demonstrate a system that leverages these constraints to produce accurate stereo models from pairs of binocular images …

Supervisory Control And Data Acquisition (Scada) Control Optimization, Garrett Johnson, Jeremy Straub, Eunjin Kim

Jeremy Straub

SCADA systems are generally used to monitor and control multiple systems of the same type to allow them to be remotely controlled and monitored. Water plants, for example, could be controlled and monitored by a SCADA system. This project seeks to optimize a SCADA system using Artificial Intelligence. A constraint satisfaction / optimization algorithm is used to maximize performance relative to weighted system goals.

An Onboard Distributed Multiprocessing System For A Cubesat Spacecraft Created From Gumstix Computer-On-Module Units, Michael Wegerson, Jeremy Straub, Ronald Marsh

Jeremy Straub

The OpenOrbiter Small Spacecraft Development Initiative at the University of North Dakota [1] aims to make ac-cess to space for research and educational purposes easier by enabling the creation of low-cost CubeSats. It is creating the Open Prototype for Educational Nanosats (OPEN), a framework for developing a 1-U CubeSat space-craft with a parts cost of less than $5,000 [2]. The designs [3], documentation and computer code from this will be made publically available to enable the development of programs at other institutions.

Considering Scheduling Algorithms For An Open Source Software Spacecraft, Calvin Bina, Jeremy Straub, Ronald Marsh

Jeremy Straub

The OpenOrbiter Small Satellite Development Initiative at the University of North Dakota [1] is working make space research and education more accessible world-wide [2], through the design and public release of a complete set of plans, software and other documents (see [3]) for a 1-U CubeSat. This design targets a parts cost of no more than $5,000 [4]. These lowered costs, combined with the efficiencies of the CubeSat form fac-tor [5] and free-to-qualified-developer launch services [6, 7] should facilitate greater access to space for the ed-ucational, research and other communities.

Trajectory Generation For Lane-Change Maneuver Of Autonomous Vehicles, Ashesh Goswami

Open Access Theses

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during …

Learning Parameterized Skills, Bruno Castro Da Silva

Doctoral Dissertations

One of the defining characteristics of human intelligence is the ability to acquire and refine skills. Skills are behaviors for solving problems that an agent encounters often—sometimes in different contexts and situations—throughout its lifetime. Identifying important problems that recur and retaining their solutions as skills allows agents to more rapidly solve novel problems by adjusting and combining their existing skills. In this thesis we introduce a general framework for learning reusable parameterized skills. Reusable skills are parameterized procedures that—given a description of a problem to be solved—produce appropriate behaviors or policies. They can be sequentially and hierarchically combined with other …

Swarm Intelligence, A Blackboard Architecture And Local Decision Making For Spacecraft Command, Jeremy Straub

Jeremy Straub

Control of a multi-spacecraft constellation is a topic of significant inquiry, at present. This paper presents and evaluates a command architecture for a multi-spacecraft mission. It combines swarm techniques with a decentralized / local decision making architecture (which uses a set of shared blackboards for coordination) and demonstrates the efficacy of this approach. Under this approach, the Blackboard software architecture is used to facilitate data sharing between craft as part of a resilient hierarchy and the swarm techniques are used to coordinate activity. The paper begins with an overview of prior work on the precursor command technologies and then presents …

A Proposed Model For The Approach To Augmented Reality Deployment In Marketing Communications, Stephen O'Mahony

Dept. of Marketing & International Business Conference Material

In the contemporary landscape, human interaction is characterized by the pervasive mediation of intelligent agents. Owing to advances in computer science and engineering, phenomena once limited to fixed locations are metamorphosing from extraneous entities to implicit components of the everyday. Computing power has simultaneously enhanced and miniaturized to the extent that contemporary consumer devices demonstrate power equivalent to or greater than that of personal computers of recent memory. At the same time, network connectivity has proliferated to ubiquitous levels, reflected in the fact that the amount of inanimate objects connected to the Internet has recently surpassed the human population of …

Energy Cost Forecasting For Event Venues, Katarina Grolinger, Andrea Zagar, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning …

Fuzzy Adaptive Resonance Theory: Applications And Extensions, Clayton Parker Smith

Masters Theses

"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully …