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Full-Text Articles in Engineering

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi May 2021

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated ...


Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri Jul 2020

Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri

Theses and Dissertations

This work employs tools and methods from computer science to study clusters comprising a small number N of interacting particles, which are of interest in science, engineering, and nanotechnology. Specifically, the thermodynamics of such clusters is studied using techniques from spectral graph theory (SGT) and machine learning (ML). SGT is used to define the structure of the clusters and ML is used on ensembles of cluster configurations to detect state variables that can be used to model the thermodynamic properties of the system. While the most fundamental description of a cluster is in 3N dimensions, i.e., the Cartesian coordinates ...


A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz May 2020

A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz

Computer Science and Computer Engineering Undergraduate Honors Theses

Effective monitoring of adherence to at-home exercise programs as prescribed by physiotherapy protocols is essential to promoting effective rehabilitation and therapeutic interventions. Currently physical therapists and other health professionals have no reliable means of tracking patients' progress in or adherence to a prescribed regimen. This project aims to develop a low-cost, privacy-conserving means of monitoring at-home exercise activity using a gym mat equipped with an array of capacitive sensors. The ability of the mat to classify different types of exercises was evaluated using several machine learning models trained on an existing dataset of physiotherapy exercises.


Motor Control Systems Analysis, Design, And Optimization Strategies For A Lightweight Excavation Robot, Austin Jerold Crawford May 2019

Motor Control Systems Analysis, Design, And Optimization Strategies For A Lightweight Excavation Robot, Austin Jerold Crawford

Theses and Dissertations

This thesis entails motor control system analysis, design, and optimization for the University of Arkansas NASA Robotic Mining Competition robot. The open-loop system is to be modeled and simulated in order to achieve a desired rapid, yet smooth response to a change in input. The initial goal of this work is to find a repeatable, generalized step-by-step process that can be used to tune the gains of a PID controller for multiple different operating points. Then, sensors are to be modeled onto the robot within a feedback loop to develop an error signal and to make the control system self-corrective ...


Fault Adaptive Workload Allocation For Complex Manufacturing Systems, Charlie B. Destefano May 2019

Fault Adaptive Workload Allocation For Complex Manufacturing Systems, Charlie B. Destefano

Theses and Dissertations

This research proposes novel fault adaptive workload allocation (FAWA) strategies for the health management of complex manufacturing systems. The primary goal of these strategies is to minimize maintenance costs and maximize production by strategically controlling when and where failures occur through condition-based workload allocation.

For complex systems that are capable of performing tasks a variety of different ways, such as an industrial robot arm that can move between locations using different joint angle configurations and path trajectories, each option, i.e. mission plan, will result in different degradation rates and life-expectancies. Consequently, this can make it difficult to predict when ...


Design Of Artificial Swarms Using Network Motifs: A Simulation Study, Khoinguyen Trinh May 2019

Design Of Artificial Swarms Using Network Motifs: A Simulation Study, Khoinguyen Trinh

Mechanical Engineering Undergraduate Honors Theses

The objective of this research is to develop a new approach in engineering complex swarm systems with desired characteristics based on the theory of network motifs – subgraphs that repeat themselves (patterns) among various networks. System engineering has traditionally followed a top-down methodology which creates a framework for the system and adds additional features to meet specific design requirements. Meanwhile, complex swarm systems, such as ant colonies and bird flocks, are formed via a bottom-up manner where the system-level structure directly emerges from the interactions and behaviors among individuals. The behaviors of these individuals cannot be directly controlled, which makes the ...


Collaborative Robotic Path Planning For Industrial Spraying Operations On Complex Geometries, Steven Brown Jan 2018

Collaborative Robotic Path Planning For Industrial Spraying Operations On Complex Geometries, Steven Brown

Theses and Dissertations

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists of ...


Music Feature Matching Using Computer Vision Algorithms, Mason Hollis May 2017

Music Feature Matching Using Computer Vision Algorithms, Mason Hollis

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as ...


Inferring Intrinsic Beliefs Of Digital Images Using A Deep Autoencoder, Seok H. Lee May 2016

Inferring Intrinsic Beliefs Of Digital Images Using A Deep Autoencoder, Seok H. Lee

Computer Science and Computer Engineering Undergraduate Honors Theses

Training a system of artificial neural networks on digital images is a big challenge. Often times digital images contain a large amount of information and values for artificial neural networks to understand. In this work, the inference model is proposed in order to absolve this problem. The inference model is composed of a parameterized autoencoder that endures the loss of information caused by the rescaling of images and transition model that predicts the effect of an action on the observation. To test the inference model, the images of a moving robotic arm were given as the data set. The inference ...