Engineering Commons^™

Machine Learning-Guided Design Of Nanolubricants For Minimizing Energy Loss In Mechanical Systems, Kollol Sarker Jogesh, Md. Aliahsan Bappy

Mechanical Engineering Faculty Publications and Presentations

This study explores the significant potential of machine learningguided design in optimizing nanolubricants, focusing on their application in reducing friction and wear in mechanical systems. Utilizing neural networks and genetic algorithms, the research demonstrates how advanced computational techniques can accurately predict and enhance the tribological properties of nanolubricants. The findings reveal that nanolubricants, particularly those containing graphene and carbon nanotubes, exhibit marked improvements in reducing friction coefficients and wear rates compared to traditional mineral oil-based lubricants. Additionally, the enhanced thermal stability and load-carrying capacity of these nanolubricants contribute to substantial energy savings and increased operational efficiency. The study underscores the …

Asset Cueing Nuclear Radiation Anomaly Detection Using An Embedded Neural Network Resource, April Inamura

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Nuclear radiation detection is inherently a challenging task, coupled with a high background variation or increase in anomalies, the accuracy for detection can plummet. A key factor in the success of nuclear detection hinges on the sensor’s ability to generalize its model and directly leads to the model’s robustness. The goal of this project is to develop algorithms suitable for use on the University of Nebraska-Lincoln’s Pingora chip, a low-power, system-on-chip device with an active neural processing unit (NPU) made for nuclear radiation detection. The thesis aims to improve Pingora’s overall generalization ability in nuclear radiation source detection. A multiphase …

Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This paper explores using Cluster Validity Indices Fuzzy Adaptative Resonance Theory (CVI Fuzzy ART) to cluster ground motion records (GMRs). Clustering the features extracted from a supervised network trained for predicting the structure damage results in less overfitting from the trained network. Using Cluster Validity Indices (CVIs) to evaluate the clustering gives feedback to how well the data is being classified, allowing further separation of the data. By using CVI Fuzzy ART in combination with features extracted from a trained Convolutional Neural Network (CNN), we were able to form additional clusters in the data. Within the primary clusters, accuracy was …

Application Of Shallow Neural Networks To Retail Intermittent Demand Time Series, Urko Allende

Dissertations

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting …

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Artificial Intelligence And Applications, Sanjay Singh Dr.

Technical Collection

I work in the broad areas of computational intelligence, artificial intelligence, neural networks, machine learning, deep learning, game theory, mathematical logic, and natural language processing. I am also actively working in the area of algorithmic fairness and explainable AI (XAI). Currently, we are developing neuro-symbolic logic learning systems for common sense reasoning, which aims to augment the existing conventional artificial intelligence, which is logically based. The neuro-symbolic logic-based systems will provide more accurate results than their GOAI (Good Old Artificial Intelligence) version. We are also working on the area of abstractive summarization methods. We intend to develop an efficient abstractive …

Impact Of Dedicated Bus Lanes On Intersection Operations And Travel Time Model Development, Stephen Arhin, Babin Manandhar, Kevin Obike, Melissa Anderson

Mineta Transportation Institute

Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the District of Columbia leading to the installation of red-painted DBLs on corridors of H Street, …

Optimization Of Orbital Trajectories Using Neuroevolution Of Augmenting Topologies, Nathan Wetherell

University Scholar Projects

This project aims to determine the feasibility of using NeuroEvolution of Augmenting Topologies (NEAT), an advanced neural network evolution scheme, to optimize orbital transfer trajectories. More specifically, this project compares a genetically evolved neural network to a standard Hohmann transfer between Earth and Mars. To test these two methods, an N-body simulation environment was created to accurately determine the result of gravitational interactions on a theoretical spacecraft when combined with planned engine burns. Once created, this simulation environment was used to train the neural networks created using the NEAT Python module. A genetic algorithm was used to modify the topology …

Building Marginal Pattern Library With Unbiased Training Dataset For Enhancing Model-Free Load-Ed Mapping, Qiwei Zhang, Fangxing Li, Wei Feng, Xiaofei Wang, Linquan Bai, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a …

Artificial Intelligence In Real-Time Diagnostics And Prognostics Of Composite Materials And Its Uncertainties – A Review, Muthu Ram Prabhu Elenchezhian, Vamsee Vadlamudi, Rassel Raihan, Kenneth Reifsnider, Erick Reifsnider

UTARI Researcher Publications

In the era of the 4th industrial revolution of big data, Artificial Intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing (NDT), Structural Health Monitoring (SHM) and Prognostics of its Remaining Useful Life (RUL), Material State (MS) and damage modes. While these AI models are rapidly developed and integrated into the Industrial Internet of Things (IIoT) to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous material state. This …

Identifying Significant Features For Player Evaluation In Nfl Comparing Anns And Traditional Models, Ronan Walsh

Dissertations

The evaluation of player performance in sports is popular and important in modern sports, enabling teams to use real data in the construction of their rosters. This dissertation proposes to apply machine learning algorithms to predicting the player evaluations from a leading NFL analytics company who use a combination of statistics and expert evaluation. In addition, it will investigate what features are significant in the evaluation of a position. Data for the dissertation is obtained from multiple online sources - Pro Football Reference and Pro Football Focus (the the NFL analytics company). These data sets are combined and analysed before …

Scalable Profiling And Visualization For Characterizing Microbiomes, Camilo Valdes

FIU Electronic Theses and Dissertations

Metagenomics is the study of the combined genetic material found in microbiome samples, and it serves as an instrument for studying microbial communities, their biodiversities, and the relationships to their host environments. Creating, interpreting, and understanding microbial community profiles produced from microbiome samples is a challenging task as it requires large computational resources along with innovative techniques to process and analyze datasets that can contain terabytes of information.

The community profiles are critical because they provide information about what microorganisms are present in the sample, and in what proportions. This is particularly important as many human diseases and environmental disasters …

Aspect And Opinion Aware Abstractive Review Summarization With Reinforced Hard Typed Decoder, Yufei Tian, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.

Artificial Neural Network Model For Bridge Deterioration And Assessment, G. Ali, A. Elsayegh, R. Assaad, Islam H. El-Adaway, I. S. Abotaleb

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Missouri has the seventh largest number of bridges nationwide, yet must maintain its inventory with funding from just the fourth lowest gasoline tax in the country. Estimation and prediction of the condition of bridges is necessary to create and optimize future maintenance, repair, and rehabilitation plans as well as to assign the necessary associated budgets. Previous studies have used statistical analysis, fuzzy logic, and Markovian models to develop algorithms for predicting future bridge conditions. Due to the non-linear nature of the relationship between the characteristics of bridges and their deterioration behavior, Artificial Neural Networks (ANN) have shown to be more …

Artificial Intelligence In The Context Of Human Consciousness, Hannah Defries

Senior Honors Theses

Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural …

Solar Concentrators Manufacture And Automation, Ernst Kussul, Tetyana Baydyk, Alberto Escalante Estrada, Maria Tersa Rodriguez Gonzalez, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Solar energy is one of the most promising types of renewable energy. Flat facet solar concentrators were proposed to decrease the cost of materials needed for production. They used small flat mirrors for approximation of parabolic dish surface. The first prototype of flat facet solar concentrators was made in Australia in 1982. Later various prototypes of flat facet solar concentrators were proposed. It was shown that the cost of materials for these prototypes is much lower than the material cost of conventional parabolic dish solar concentrators. To obtain the overall low cost of flat facet concentrators it is necessary to …

Elm-Som: A Continuous Self-Organizing Map For Visualization, Renjie Hu, Venous Roshdibenam, Hans J. Johnson, Emil Eirola, Anton Akusok, Yoan Miche, Kaj Mikael Björk, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Novel Dimensionality Reduction Technique: Elm-Som. This Technique Preserves the Intrinsic Quality of Self-Organizing Maps (Som): It is Nonlinear and Suitable for Big Data. It Also Brings Continuity to the Projection using Two Extreme Learning Machine (Elm) Models, the First One to Perform the Dimensionality Reduction and the Second One to Perform the Reconstruction. Elm-Som is Tested Successfully on Six Diverse Datasets. Regarding Reconstruction Error, Elm-Som is Comparable to Som While Bringing Continuity.

Modeling And Simulation Of Microgrid, Ahmad Alzahrani, Mehdi Ferdowsi, Pourya Shamsi, Cihan H. Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control …

Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi

Electrical and Computer Engineering Faculty Research & Creative Works

Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying …

Critical Comparison Of The Classification Ability Of Deep Convolutional Neural Network Frameworks With Support Vector Machine Techniques In The Image Classification Process, Robert Kelly

Dissertations

Recently, a number of new image classification models have been developed to diversify the number of options available to prospective machine learning classifiers, such as Deep Learning. This is particularly important in the field of medical image classification as a misdiagnosis could have a severe impact on the patient. However, an assessment on the level to which a deep learning based Convolutional Neural Network can outperform a Support Vector Machine has not been discussed. In this project, the use of CNN and SVM classifiers is used on a dataset of approx. 55,000 images. This dataset was used to assess the …

Towards Improving Visqol (Virtual Speech Quality Objective Listener) Using Machine Learning Techniques, Joseph Mcnally

Dissertations

Vast amounts of sound data are transmitted every second over digital networks. VoIP services and cellular networks transmit speech data in increasingly greater volumes. Objective sound quality models provide an essential function to measure the quality of this data in real-time. However, these models can suffer from a lack of accuracy with various degradations over networks. This research uses machine learning techniques to create one support vector regression and three neural network mapping models for use with ViSQOLAudio. Each of the mapping models (including ViSQOL and ViSQOLAudio) are tested against two separate speech datasets in order to comparatively study accuracy …

“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, Samuel C. Adams

Senior Honors Theses

Alan Turing asked if machines can think, but intelligence is more than logic and reason. I ask if a machine can feel pain or joy, have visions and dreams, or paint a masterpiece. The human brain sets the bar high, and despite our progress, artificial intelligence has a long way to go. Studying neurology from a software engineer’s perspective reveals numerous uncanny similarities between the functionality of the brain and that of a computer. If the brain is a biological computer, then it is the embodiment of artificial intelligence beyond anything we have yet achieved, and its architecture is advanced …

A New Hybrid Approach For Forecasting Interest Rates, David Enke, Nijat Mehdiyev

Engineering Management and Systems Engineering Faculty Research & Creative Works

The dynamic, non-linear, volatile and complex nature of interest rates makes it hard to predict their future movements. in order to deal with these complexities, the authors propose a two-stage neuro-hybrid forecasting model. in the initial data preprocessing stage, multiple regression analysis is implemented to determine the variables that have the strongest prediction ability. the selected variables are then provided as inputs to a Fuzzy Inference Neural Network to forecast future interest rate values. the proposed hybrid model is implemented using data from the U.S. interest rate market. © 2012 Published by Elsevier B.V.

Novel Dynamic Representation And Control Of Power Systems With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow

Electrical and Computer Engineering Faculty Research & Creative Works

FACTS devices have been shown to be useful in damping power system oscillations. However, in large power systems, the FACTS control design is complex due to the combination of differential and algebraic equations required to model the power system. In this paper, a new method to generate a nonlinear dynamic representation of the power network is introduced to enable more sophisticated control design. Once the new representation is obtained, a back stepping methodology for the UPFC is utilized to mitigate the generator oscillations. Finally, the neural network approximation property is utilized to relax the need for knowledge of the power …

Comparison Of Feedforward And Feedback Neural Network Architectures For Short Term Wind Speed Prediction, Ganesh K. Venayagamoorthy, Richard L. Welch, Stephen M. Ruffing

Electrical and Computer Engineering Faculty Research & Creative Works

This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy Laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP …

Novel Dynamic Representation And Control Of Power Networks Embedded With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow

Electrical and Computer Engineering Faculty Research & Creative Works

FACTS devices have been shown to be powerful in damping power system oscillations caused by faults; however, in the multi machine control using FACTS, the control problem involves solving differential-algebraic equations of a power network which renders the available control schemes ineffective due to heuristic design and lack of know how to incorporate FACTS into the network. A method to generate nonlinear dynamic representation of a power system consisting of differential equations alone with universal power flow controller (UPFC) is introduced since differential equations are typically preferred for controller development. Subsequently, backstepping methodology is utilized to reduce the generator oscillations …

Intelligent Tool For Determining The True Harmonic Current Contribution Of A Customer In A Power Distribution Network, Joy Mazumdar, Ronald G. Harley, Frank C. Lambert, Ganesh K. Venayagamoorthy, Marty L. Page

Electrical and Computer Engineering Faculty Research & Creative Works

Customer loads connected to power distribution network may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence, any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on artificial neural networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, …

Live Wire Segmentation Tool For Osteophyte Detection In Lumbar Spine X-Ray Images, Santosh Seetharaman, R. Joe Stanley, Soumya De, Sameer Antani, L. Rodney Long, George R. Thoma

Electrical and Computer Engineering Faculty Research & Creative Works

Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 …

Change In Voltage Distortion Predictions At The Pcc Due To Changing Nonlinear Load Current Profile Using Plant Startup Data, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics in a power distribution network. The interaction of the nonlinear load harmonics with the network impedances creates voltage distortions at the point of common coupling (PCC) which in turn affects other loads connected to the same PCC. When several nonlinear loads are connected to the PCC, it is difficult to predict mathematically how each nonlinear load is affecting the voltage distortion level at the PCC. Typically, customers with nonlinear loads apply harmonic filtering techniques to clean up their current and …