Engineering Commons^™

Combining Green Metrics And Digital Twins For Sustainability Planning And Governance Of Smart Buildings And Cities, Casey R. Corrado, Suzanne M. Delong, Emily G. Holt, Edward Y. Hua, Andreas Tolk

VMASC Publications

Creating a more sustainable world will require a coordinated effort to address the rise of social, economic, and environmental concerns resulting from the continuous growth of cities. Supporting planners with tools to address them is pivotal, and sustainability is one of the main objectives. Modeling and simulation augmenting digital twins can play an important role to implement these tools. Although various green best practices have been utilized over time and there are related attempts at measuring green success, works in the published literature tend to focus on addressing a single problem (e.g., energy efficiency), and a comprehensive approach that takes …

Investigating How Speech And Animation Realism Influence The Perceived Personality Of Virtual Characters And Agents, Sean A. Thomas, Ylva Ferstl, Rachel Mcdonnell, Cathy Ennis

Articles

The portrayed personality of virtual characters and agents is understood to influence how we perceive and engage with digital applications. Understanding how the features of speech and animation drive portrayed personality allows us to intentionally design characters to be more personalized and engaging. In this study, we use performance capture data of unscripted conversations from a variety of actors to explore the perceptual outcomes associated with the modalities of speech and motion. Specifically, we contrast full performance-driven characters to those portrayed by generated gestures and synthesized speech, analysing how the features of each influence portrayed personality according to the Big …

Photocatalytic Degradation Of Lignin By Supported Silver Nanoparticles, Ning Wei

Theses and Dissertations--Chemical and Materials Engineering

Lignin is the second most abundant form of biomass on earth. The phenolic structure and high carbon to oxygen ratio make lignin an attractive renewable source of fuel and chemicals. However, its recalcitrance and heterogeneous nature prove difficult for decomposing lignin’s polymer structure and separation of the products. This work has focused on the use of low-energy catalytic approaches to overcome these barriers. A mimic of the lignin degrading enzyme laccase, consisting of a copper cluster Cu4Py4I4 modified with AgNO3, was developed to function similarly to the laccase active site. The prepared copper complex solution was found to be active …

Exploring Cyber-Physical Systems’ Security Governance In The Oil And Gas Industry, Soliman Mahmoud

Walden Dissertations and Doctoral Studies

The Fourth Industrial Revolution, which utilizes modern communication-dependent technologies, including cyber-physical systems (CPS), has made exploration and production operations more efficient in the oil and gas industry. CPS in this industry should be secured against operational threats to prevent interruption of critical oil and gas supplies and services. However, these systems are vulnerable to cyberattacks, and many oil and gas companies have not incorporated effective cybersecurity measures into their corporate management strategies. This qualitative, multiple-case study, which was guided by the routine activity theory, explored how cybersecurity governance was applied to develop controls that stopped or mitigated the consequences of …

The Implementation Of Advanced Digitalization In The Oil And Gas Industry, Ensieh Rezafar

Walden Dissertations and Doctoral Studies

AbstractThe immature advanced digitalization in the oil and gas industry can limit access to the potential value of mature progressive digitalization. Oil and gas leaders must expedite the implementation of advanced digitalization to increase organizational proficiencies and reduce costs and losses. Grounded in Pettigrew and Whipp’s dimensions of strategic change theory, the purpose of this qualitative multiple case study was to explore strategies some leaders in the oil and gas industry used to implement advanced digitalization. The participants were seven leaders in two oil service companies in North America involved in the successful implementation of progressive digitalization. Data were collected …

Human Ergonomic Simulation To Support The Design Of An Exoskeleton For Lashing/De-Lashing Operations Of Containers Cargo, Francesco Longo, Antonio Padovano, Vittorio Solina, Virginia D' Augusta, Stefan Venzl, Roberto Calbi, Michele Bartuni, Ornella Anastasi, Rafael Diaz

VMASC Publications

Lashing and de-lashing operations of containers cargo on board containerships are considered as quite strenuous activities in which operators are required to work continuously over a 6 or 8 hours shift with very limited break. This is mostly because containerships need to leave the port as soon as possible and containers loading and unloading operations must be executed with very high productivity (stay moored in a port is a totally unproductive time for a ship and a loss-making business for a shipping company). Operators performing lashing and de-lashing operations are subjected to intense ergonomic stress and uncomfortable working postures. To …

Empirical Comparison Of Machine Learning Methods For Wind Power Predictions, Sidny M. Stewart

EWU Masters Thesis Collection

No abstract provided.

Application Of Biologically Activate Carbon For Treatment Of Sulfide-Laden Groundwater, Jessica Cormier

Electronic Theses and Dissertations, 2020-

Small-system water purveyors must overcome many challenges to provide an adequate and safe water supply to its consuming public. This dissertation reports on research related to the application of biologically activated carbon (BAC) media filters for the treatment of well water to remove disinfection by-product (DBP) precursor matter, measured as dissolved organic carbon (DOC), at two treatment plants serving a small community water system. Four research questions were investigated individually in discrete, yet interconnected studies at two water treatment facilities processing groundwater that contained hydrogen-sulfide (~1.2 mg/L) and dissolved organic carbon (~2.0 mg/L). The first study revealed that BAC filters …

Theoretical And Experimental Application Of Neural Networks In Spaceflight Control Systems, Pavel Galchenko

Doctoral Dissertations

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation …

A Machine Learning Approach To Intended Motion Prediction For Upper Extremity Exoskeletons, Justin Berdell

Graduate Research Theses & Dissertations

A fully solid-state, software-defined, one-handed, handle-type control device built around a machine-learning (ML) model that provides intuitive and simultaneous control in position and orientation each in a full three degrees-of-freedom (DOF) is proposed in this paper. The device, referred to as the “Smart Handle”, and it is compact, lightweight, and only reliant on low-cost and readily available sensors and materials for construction. Mobility chairs for persons with motor difficulties could make use of a control device that can learn to recognize arbitrary inputs as control commands. Upper-extremity exoskeletons used in occupational settings and rehabilitation require a natural control device like …

Reconstructability Analysis: Discrete Multivariate Modeling, Martin Zwick

Systems Science Faculty Publications and Presentations

An introduction to Reconstructability Analysis for the Discrete Multivariate Modeling course and for other purposes.

Developing An Open Database To Support Forensic Investigation Of Disasters In South East Asia: Forinsea V1.0, Andres Payo, Raushan Arnhardt, Angelo Carlo R. Galindo, Pham Van Dong, Ma. Aileen Leah G. Guzman, Yasmin O. Hatta, Andrew Bevan, Jose Claro N. Monje

Electronics, Computer, and Communications Engineering Faculty Publications

This article describes the development of a bespoke database, FORINSEA1.0, created to address the need for a systematic curation of information needed for the descriptive phase of the FORIN approach and its application to two study areas in the South East Asia region. FORINSEA1.0 allows researchers, for the first time, to explore and make use of subnational, geocoded data on major disasters triggered by natural hazards (flooding, earthquake, landslide and meteorological hazards) since 1945 until 2020 in the hydrological catchment of the Red River in Vietnam and the Marikina Basin in the Philippines. FORINSEA1.0 also contains relevant subnational information on …

Deepfakes: Ai Technology Of The Future, Hosanna Root

Cybersecurity Undergraduate Research Showcase

Deepfakes technology’s danger stems from its ability to create realistic but fake synthesized media that people often identify as something that is real. With this powerful technology in the wrong hands, deepfakes can cause devastating havoc through information warfare, election campaign disruptions, and more, creating distrust in society. Disinformation is already rampant today, even without wide deployments of deepfakes, which is concerning given the fact that deepfakes’ nefarious full potentials are yet to be reached.

Corporate Cybersecurity In The Context Of M&A Transactions, Cameron Beck

Cybersecurity Undergraduate Research Showcase

The rapid rise of digital devices has unlocked a new dimension of innovation and prosperity in the 21st century. Computers are now an integrated and ubiquitous part of our global culture. You would be hard-pressed to walk into any given room without several computer chips humming inaudibly inside the machines that facilitate our modern world. Even lightbulbs and doorbells are connected to the Internet, capturing information from the world around them and sending that information to the Cloud. The Internet expands access to communication, international marketplaces, entertainment, professional resources, and nearly every book in the world.

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

On The Implementation And Further Validation Of A Time Domain Boundary Element Method Broadband Impedance Boundary Condition, Fang Q. Hu, Douglas M. Nark

Mathematics & Statistics Faculty Publications

A time domain boundary integral equation with Burton-Miller reformulation is presented for acoustic scattering by surfaces with liners in a uniform mean flow. The Ingard-Myers impedance boundary condition is implemented using a broadband multipole impedance model and converted into time domain differential equations to augment the boundary integral equation. The coupled integral-differential equations are solved numerically by a March-On-in-Time (MOT) scheme. While the Ingard-Myers condition is known to support Kelvin-Helmholtz instability due to its use of a vortex sheet interface between the flow and the liner surface, it is found that by neglecting a second derivative term in the current …

Mechanism For Selective Binding Of Aromatic Compounds On Oxygen-Rich Graphene Nanosheets Based On Molecule Size/Polarity Matching, Heyun Fu, Bingyu Wang, Dongqiang Zhu, Zhicheng Zhou, Shidong Bao, Xiaolei Qu, Yong Guo, Lan Ling, Shourong Zheng, Pu Duan, Jingdong Mao, Klaus Schmidt-Rohr, Shu Tao, Pedro J.J. Alvarez

Chemistry & Biochemistry Faculty Publications

Selective binding of organic compounds is the cornerstone of many important industrial and pharmaceutical applications. Here, we achieved highly selective binding of aromatic compounds in aqueous solution and gas phase by oxygen-enriched graphene oxide (GO) nanosheets via a previously unknown mechanism based on size matching and polarity matching. Oxygen-containing functional groups (predominately epoxies and hydroxyls) on the nongraphitized aliphatic carbons of the basal plane of GO formed highly polar regions that encompass graphitic regions slightly larger than the benzene ring. This facilitated size match–based interactions between small apolar compounds and the isolated aromatic region of GO, resulting in high binding …

A Local Mode Study Of Ring Puckering Effects In The Infrared Spectra Of Cyclopentane, Edwin L. Sibert Iii, Peter F. Bernath

Chemistry & Biochemistry Faculty Publications

We report and interpret recently recorded high-resolution infrared spectra for the fundamentals of the CH₂ scissors and CH stretches of gas phase cyclopentane at −26.1 and −50 ^○C, respectively. We extend previous theoretical studies of this molecule, which is known to undergo barrierless pseudorotation due to ring puckering, by constructing local mode Hamiltonians of the stretching and scissor vibrations for which the frequencies, couplings, and linear dipoles are calculated as functions of the pseudorotation angle using B3LYP/6-311++(d,p) and MP2/cc-pVTZ levels of theory. Symmetrization (D_5h) of the vibrational basis sets leads to simple vibration/pseudorotation Hamiltonians whose solutions …

A Highly Conductive, Flexible, And 3d-Printable Carbon Nanotube-Elastomer Ink For Additive Bio-Manufacturing, Andy Shar, Phillip Glass, Daeha Joung Ph.D.

Undergraduate Research Posters

The synthesis of a highly conductive, flexible, 3D-printable, and biocompatible ink has been of great interest in the field of bio-based additive manufacturing. Various applications include ultra-sensitive, microscale tactile sensors, patient-customizable scaffolds for cardiac and nerve tissue regeneration, and flexible electrocardiogram (ECG) electrodes. Here, a novel elastomeric carbon nanocomposite is presented consisting of amino-functionalized carbon nanotubes (CNT-NH₂) homogenously dispersed in a one-part room-temperature vulcanizing (RTV) silicone matrix. The use of acetone as a swelling solvent aids in electrical percolation through the elastomer matrix. CNT-NH₂ ratios can be tuned to fit various needs; higher tensile strength is favored …

Evaluation Of Snow And Streamflow In The National Water Model With Analysis Using Machine Learning, Engela Sthapit

Dissertations and Theses

Snow has great influence on land-atmosphere interactions and snowmelt from the mountains is a vital water source for downstream communities dependent on snow fed lakes, rivers and streams. This study explored the snow and streamflow prediction capabilities of process-based numerical prediction and data-driven machine learning models.

The overall goal of this study was to understand the deficiencies in the NOAA’s National Water Model (NWM) to represent snow, subsequently streamflow, and recognize the areas where it could be improved for future model developments. The goal was also to evaluate if the recent advancements in machine learning techniques is useful for predicting …

Data Fusion And Synergy Of Active And Passive Remote Sensing; An Application For Freeze Thaw Detections, Zahra Sharifnezhadazizi

Dissertations and Theses

There has been a recent evolvement in the field of remote sensing after increase of number satellites and sensors data which could be fused to produce new data and products. These efforts are mainly focused on using of simultaneous observations from different platforms with different spatial and temporal resolutions. The research dissertation aims to enhance the synergy use of active and passive microwave observations and examine the results in detection land freeze and thaw (FT) predictions. Freeze thaw cycles particularly in high-latitude regions have a crucial role in many applications such as agriculture, biogeochemical transitions, hydrology and ecosystem studies. The …

Core Point Pixel-Level Localization By Fingerprint Features In Spatial Domain, Xueyi Ye, Yuzhong Shen, Maosheng Zeng, Yirui Liu, Huahua Chen, Zhijing Zhao

Computational Modeling & Simulation Engineering Faculty Publications

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve …

Study On Performance Of Pruned Cnn-Based Classification Models, Mengling Deng

Electronic Theses and Dissertations

Convolutional Neural Network (CNN) is a neural network developed for processing image data. CNNs have been studied extensively and have been used in numerous computer vision tasks such as image classification and segmentation, object detection and recognition, etc. [1] Although, the CNNs-based approaches showed humanlevel performances in these tasks [2], they require heavy computation in both training and inference stages, and the models consist of millions of parameters. This hinders the development and deployment of CNN-based models for real world applications. Neural Network Pruning and Compression techniques have been proposed [3, 4] to reduce the computation complexity of trained CNNs …

Fish And Invertebrate Use Of Restored Vs. Natural Oyster Reefs In A Shallow Temperate Latitude Estuary, Jonathan H. Grabowski, Christopher J. Baillie, Adam Baukus, Rachael Carlyle, F. Joel Fodrie, Rachel K. Gittman, A. Randall Hughes, David L. Kimbro, Juhyung Lee, Hunter S. Lenihan, Sean P. Powers, Kevin Sullivan

University Faculty and Staff Publications

Coastal marine habitats continue to be degraded, thereby compelling largescale restoration in many parts of the world. Whether restored habitats function similarly to natural habitats and fully recover lost ecosystem services is unclear. In estuaries, oyster reefs have been degraded by multiple anthropogenic activities including destructive fishing practices and reduced water quality, motivating restoration to maintain oyster fisheries and other ecosystem services, often at relatively high cost. We compared fish and invertebrate communities on recently restored (0–1 year post-restoration), older restored (3–4 years post-restoration), and natural oyster reefs to determine if and when restored reefs support functionally similar faunal communities. …

Framework For The Evaluation Of Perturbations In The Systems Biology Landscape And Inter-Sample Similarity From Transcriptomic Datasets — A Digital Twin Perspective, Mariah Marie Hoffman

Dissertations and Theses

One approach to interrogating the complexities of human systems in their well-regulated and dysregulated states is through the use of digital twins. Digital twins are virtual representations of physical systems that are descriptive of an individual's state of health, an object fundamentally related to precision medicine. A key element for building a functional digital twin type for a disease or predicting the therapeutic efficacy of a potential treatment is harmonized, machine-parsable domain knowledge. Hypothesis-driven investigations are the gold standard for representing subsystems, but their results encompass a limited knowledge of the full biosystem. Multi-omics data is one rich source of …

Knowledge Management In Engineering Companies In The Nigeria Oil And Gas Industry, Babajide Adeniran Ojuola

Walden Dissertations and Doctoral Studies

Engineering companies in the Nigerian Oil and Gas Industry are not able to optimize their knowledge resources through the continual conversion of tacit knowledge to organizational knowledge. This is due to barriers that inhibit the holistic process of tacit knowledge conversion. The purpose of this qualitative case study was to understand the enablers and barriers to tacit knowledge conversion in engineering companies as perceived by engineering practitioners working in the Nigerian oil and gas industry. The central research questions focused on exploring the enablers and barriers to the conversion of tacit knowledge to organizational knowledge in oil and gas engineering …

Factors Influencing The Effectiveness Of Managing Human–Robot Teams, Theodore B. Terry

Walden Dissertations and Doctoral Studies

Certain factors can influence the capabilities of a robot–human team by affecting their social and behavioral dynamics in a work environment. But these factors were not known due to the progressive nature of human–robot partnerships and a lack of peer-reviewed literature on the topic. This e-Delphi study aimed to identify and understand these unknown influential factors based on the participants’ insights. The overarching research question asked about the need to determine factors that might influence the effectiveness of managing human-robot teams. The basis for the conceptual framework for this study was the theory of communication used in organizational management. Twelve …

Electrochemical Gelation Of Metal Chalcogenide Quantum Dots, Chathuranga Chinthana Hewa Rahinduwage

Wayne State University Dissertations

Quantum dots (QDs) are attractive because of their unique size-dependent optical and electronic properties and high surface area. They are tested in research for diverse applications, including energy conversion, catalysis, and sensing. Assembling QDs into functional solid-state devices while preserving their attractive properties is a challenge. Methods currently under the research are not effective in directly fabricating QDs onto devices, making large area assemblies, maintaining the high surface area by forming 3D porous structures, and conducting electricity for applications such as sensing. QD gels are an example of QD assemblies that consist of a 3D porous interconnected QD network. They …

Optical Signal Processing With Discrete-Space Metamaterials, Mohammad Moein Moeini

Wayne State University Dissertations

As digital circuits are approaching the limits of Moore’s law, a great deal of efforthas been directed to alternative computing approaches. Among them, the old concept of optical signal processing (OSP) has attracted attention, revisited in the light of metamaterials and nano-photonics. This approach has been successful in realizing basic mathematical operations, such as derivatives and integrals, but it is difficult to be applied to more complex ones. Inspired by digital filters, we propose a radically new OSP approach, able to realize arbitrary mathematical operations over a nano-photonic platform. We demonstrate this concept for the case of spatial differentiation, image …