Engineering Commons^™

A Case For An Independent Cyber Force, Ian C. Heffron, Mark Reith, James W. Dean

Faculty Publications

Although cyberspace is considered the newest warfighting domain, military analysts and scholars have opined the United States remains woefully behind its peers in cyberspace and have called for the creation of a separate cyber service component. Yet a cohesive and robust discussion on this topic has yet to emerge. This article proposes a general framework that builds on the Joint doctrine, organization, training, materiel, leadership and education, personnel, facilities, and policy (DOTMLPF-P) analysis to address questions of sufficiency and necessity. Such analysis reveals DoD cyber operations do not maximize the United States’ ability to fight a cyber war, especially when …

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen

Turkish Journal of Electrical Engineering and Computer Sciences

The classic formulation of the extended boundary condition method is revisited to inject the regularization operators for the unknown coefficients of the eigen-function expansions for the travelling and standing waves throughout the dielectric scatterer. It is shown that, using the new definitions, the existing algorithm of the scattering field calculation can be kept the same for its well-conditioned version. This is exemplified for scalar 2D problems for both TM and TE polarization under illumination of a line source. The condition numbers of the matrix operators in the new version of the algorithm are drastically reduced when the regularization interfaces are …

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …

A Review Of Solar Hybrid Photovoltaic-Thermal (Pv-T) Collectors And Systems, Todd Otanicar

Mechanical and Biomedical Engineering Faculty Publications and Presentations

In this paper, we provide a comprehensive overview of the state-of-the-art in hybrid PV-T collectors and the wider systems within which they can be implemented, and assess the worldwide energy and carbon mitigation potential of these systems. We cover both experimental and computational studies, identify opportunities for performance enhancement, pathways for collector innovation, and implications of their wider deployment at the solar-generation system level. First, we classify and review the main types of PV-T collectors, including air-based, liquid-based, dual air–water, heat-pipe, building integrated and concentrated PV-T collectors. This is followed by a presentation of performance enhancement opportunities and pathways for …

Jme 4110: Upgraded Refrigeration Door Cycling System, Keenan Bland, Kelvin Woods, Tyler Mclaughlin

Washington University / UMSL Mechanical Engineering Design Project JME 4110

This project focuses on improvements to a door cycling system. Both a fortified rope for the pulley system, ensuring extended operation, and the integration of a support track. This track is designed to bolster the clevis linked to an air piston that facilitates smooth piston rod travel without deflection. With these advancements, our door cycling system attains a new level of reliability, durability and functionality.

The Plastics Collection Reference Packet, Special Collections Research Center

Special Collections Research Center

This reference packet is an informational tool to support further research into the history of plastics—whether interested in companies, individuals within the plastics industry's history, historical plastics materials, essays, and more. All content featured within this packet was previously published on the former plastics.syr.edu website as part of a Syracuse University Libraries and Special Collections Research Center (SCRC) partnership established in 2007 with the Plastics Pioneers Association (PPA)—an association of plastics industry professionals interested in preserving the plastics industry's past.

Enhancing And Securing Wireless Medical Technology For Diagnosis And Treatment Of Lower Urinary Tract Dysfunction, Farhath Zareen

USF Tampa Graduate Theses and Dissertations

Lower urinary tract dysfunction (LUTD) is a debilitating medical condition that affects millions of individuals worldwide. Urodynamics is the current gold standard for diagnosing LUTD but uses non-physiologically fast, retrograde cystometric filling to obtain a brief snapshot of bladder function. Current state-of-the-art research in bladder monitoring includes ambulatory urodynamics using wireless implantable devices to evaluate bladder function during natural filling for long-term monitoring. However, there are various challenges and limitations to this multi-sensor approach. This research focuses on developing frameworks for automated event detection, data analysis, and optimization of long-term bladder recordingsto improve the diagnosis and treatment of LUTD. In …

Evaluation Of Amnion Membrane Made Vascular Graft In Rat Model And Porcine Model, Xiaolong Wang

Master's Theses (2009 -)

Heart bypass surgery has become a common therapeutic strategy to save heart attack patients from death. However, there are issues with the availability of graft resources for patients who cannot use autologous vessels due to their health conditions. To solve this problem, this study aims to develop a small-diameter vascular graft based on the human decellularized amniotic membrane (DAM) as an alternative to autologous vessels. The human amniotic membrane was harvested from the placenta, obtained from consenting and de-identified donors, and decellularized to remove cellular components while preserving its extracellular matrix. Small-diameter vascular grafts were fabricated using the DAM as …

Application Of Artificial Neural Network (Ann) In Development Of Prediction Models For Pavement Performance And Material Properties, Prashanta Kumar Acharjee

Civil Engineering Theses

This dissertation presents the development and application of Artificial Neural Network (ANN)- based prediction models for Dynamic Modulus (E*), Dynamic Shear Modulus (|Gb*|, Phase Angle (b), Soil-Water Characteristics Curve (SWCC) parameters, and International Roughness Index (IRI). The IRI prediction model considering climatic and traffic conditions of Texas with data from the Long-Term Pavement Performance (LTPP) database with R 2 = 0.92 can be utilized by Local transportation agencies. An E* prediction model with three neurons, using 7400 data points obtained from 346 mixtures with R2=0.82 can bypass the need for laboratory tests. ANN-based |Gb*| and b prediction models were also …

Development Of Reduced Cementitious Materials Concrete (Rcmc) Mixtures For Bridge Decks And Rails, Soumitra Das

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Right after construction, drying shrinkage of restrained concrete bridge decks and rails causes early-age cracking that eventually leads to delamination and spalling of concrete due to insertion of water and chemicals, and corrosion of reinforcing steel. The main objective of this research is to control early-age shrinkage cracking by reducing cementitious material content in the bridge deck and rail concrete mixtures. Several reduced cementitious materials concrete (RCMC) mixtures were developed by optimizing aggregate particle packing and conducting overall performance evaluation. This evaluation was carried out in three phases: The first phase investigated the feasibility of new RCMC mixtures by testing …

Mentoring Experiences Of Undergraduate Students And Faculty Members In Science, Technology, Engineering, And Mathematics, Pamela Martínez Oquendo

School of Natural Resources: Dissertations, Theses, and Student Research

I present a comprehensive view of mentoring experiences of undergraduate students and faculty members in science, technology, engineering, and mathematics (STEM). In Chapter 1, I describe a brief outline of this dissertation. In Chapter 2, I present an interpretative phenomenological analysis of the lived experiences of former STEM undergraduate mentors of the Nebraska STEM For You (NE STEM 4U) afterschool mentoring program. In Chapter 3, I describe how the ramifications of faculty mentorship influence the science pipeline using a qualitative synthesis. In Chapter 4, I describe how the STEM faculty-student mentoring engagement involves a strong psychological support component using a …

Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han

Mathematics and Statistics Faculty Research & Creative Works

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …

Fusion Of Microgrid Control With Model-Free Reinforcement Learning: Review And Vision, Buxin She, Fangxing Li, Hantao Cui, Jingqiu Zhang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of …

Don’T Touch That Dial: Psychological Reactance, Transparency, And User Acceptance Of Smart Thermostat Setting Changes, Matthew Heatherly, Denise A. Baker, Casey I. Canfield

Psychological Science Faculty Research & Creative Works

Automation inherently removes a certain amount of user control. If perceived as a loss of freedom, users may experience psychological reactance, which is a motivational state that can lead a person to engage in behaviors to reassert their freedom. In an online experiment, participants set up and communicated with a hypothetical smart thermostat. Participants read notifications about a change in the thermostat's setting. Phrasing of notifications was altered across three dimensions: strength of authoritative language, deviation of temperature change from preferences, and whether or not the reason for the change was transparent. Authoritative language, temperatures outside the user's preferences, and …

Exploring The Impact Of Students Demographic Attributes On Performance Prediction Through Binary Classification In The Kdp Model, Issah Iddrisu, Peter Appiahene, Obed Appiah, Inusah Fuseini

Knowledge Engineering and Data Science

During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) …

Maximum Marginal Relevance And Vector Space Model For Summarizing Students' Final Project Abstracts, Gunawan Gunawan, Fitria Fitria, Esther Irawati Setiawan, Kimiya Fujisawa

Knowledge Engineering and Data Science

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of …

Identification And Validation Of A Predicted Risk-Taking Propensity Model Among General Aviation Pilots, Joel Samu

Doctoral Dissertations and Master's Theses

Risk-taking, a persistent topic of interest and concern in aviation, has been linked with unsafe behaviors and accidents. However, risk-taking propensity is a complex construct that encompasses numerous factors still being researched. Even within the limited research available about the factors affecting pilots’ risk-taking propensity, studies have yielded inconsistent results. Therefore, this quantitative study explores existing and novel factors that predict the propensity for risk-taking among general aviation (GA) pilots in the United States.

This study, conducted in two stages, involved developing a prediction model using backward stepwise regression to predict pilots’ risk propensity, followed by model fit testing using …

Modeling, Control, And Hardware Development Of A Thrust-Vector Coaxial Uav, Andrew North

Doctoral Dissertations and Master's Theses

This thesis introduces a unique thrust vector coaxial unmanned aerial vehicle (UAV) configuration and presents a comprehensive investigation encompassing dynamics modeling, hardware design, and controller development. Using the Newton-Euler method, a dynamic model for the UAV is derived to gain in-depth insights into its fundamental flight characteristics. A simple thrust model is formulated and modified by comparing it with data obtained from vehicle testing. The feasibility of manufacturing such a vehicle is assessed through the development of a hardware prototype. Finally, a linear state feedback controller is designed and evaluated using the non-linear dynamics model. The results demonstrate successful validation …

In-Flight Testing & Verification Of An Adaptive Distributed Fault-Tolerant Control Architecture, Michael Budihartono

Doctoral Dissertations and Master's Theses

The interest in utilizing multi-agent systems (MAS) has increased in the aerospace industry. Its scalability, efficiency, robustness, fault tolerance, and cost-effectiveness make it ideal for performing real-world missions that require more than one agent. However, in completing the tasks, the multi-agent systems are still vulnerable to environmental disturbances, cyber-attacks, and hardware failures. Therefore, an adaptive distributed fault-tolerant control architecture is needed to minimize the impacts of the previously stated circumstances and ensure the mission can continue successfully.

This thesis describes the development of an experimental setup for testing and validating an adaptive consensus algorithm and a bio-inspired health management architecture. …

Aeroponic System Optimization For Butterhead Lettuce Growth And Future Sustainability Using Flow Blurring Atomization, Taylor J. Johnson

Doctoral Dissertations and Master's Theses

The global population has grown by 6 billion people over the last century and is trending toward 9.7 billion people by the year 2050. Agriculture accounts for 70% of global fresh water usage. Technology must be developed to accommodate the increase of food production demanded by the growing global population and the subsequent increase in water usage. Aeroponic technology is a water-efficient vertical farming technology that can reduce water usage by 90% by suspending plant roots in air within a controlled chamber and supplying atomized droplets of a water-nutrient solution directly to the roots.

This study simultaneously tests six droplet …

Inter-Frame Video Compression Based On Adaptive Fuzzy Inference System Compression Of Multiple Frame Characteristics, Arief Bramanto Wicaksono Putra, Rheo Malani, Bedi Suprapty, Achmad Fanany Onnilita Gaffar, Roman Voliansky

Knowledge Engineering and Data Science

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods. Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized …

Ant Colony Optimization For Resistor Color Code Detection, Slamet Wibawanto, Kartika Candra Kirana, Hani Ramadhan

Knowledge Engineering and Data Science

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other …

K-Means Clustering And Multilayer Perceptron For Categorizing Student Business Groups, Miftahul Walid, Norfiah Lailatin Nispi Sahbaniya, Hozairi Hozairi, Fajar Baskoro, Arya Yudhi Wijaya

Knowledge Engineering and Data Science

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited …

Round-Robin Algorithm In Load Balancing For National Data Centers, I Kadek Wahyu Sudiatmika, Gede Indrawan, Sariyasa Sariyasa

Knowledge Engineering and Data Science

The Provincial Government of Bali assumes a crucial role in administering various public service applications to meet the requirements of its community, traditional villages, and regional apparatus. Nevertheless, the escalating magnitude of traffic and uneven distribution of requests have resulted in substantial server burdens, which may jeopardize the operation of applications and heighten the likelihood of downtime. Ensuring efficient load distribution is of utmost importance in tackling these difficulties, and the Round Robin algorithm is often utilized for this purpose. However, the current body of research has not extensively examined the distinct circumstances surrounding on-premise servers in the Bali Provincial …

Long-Term Traffic Prediction Based On Stacked Gcn Model, Atkia Akila Karim, Naushin Nower

Knowledge Engineering and Data Science

With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to …

Optimizing Random Forest Algorithm To Classify Player's Memorisation Via In-Game Data, Akmal Vrisna Alzuhdi, Harits Ar Rosyid, Mohammad Yasser Chuttur, Shah Nazir

Knowledge Engineering and Data Science

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. …

Freestanding Graphene Heat Engine Analyzed Using Stochastic Thermodynamics, J. Durbin, J. M. Mangum, M. N. Gikunda, F. Harerimana, P. Kumar, L. L. Bonilla, P. M. Thibado

Physics Faculty Publications and Presentations

We present an Ito-Langevin model for freestanding graphene connected to an electrical circuit. The graphene is treated as a Brownian particle in a double-well potential and is adjacent to a fixed electrode to form a variable capacitor. The capacitor is connected in series with a battery and a load resistor. The capacitor and resistor are given separate thermal reservoirs. We have solved the coupled Ito-Langevin equations for a broad range of temperature differences between the two reservoirs. Using ensemble averages, we report the rate of change in energy, heat, and work using stochastic thermodynamics. When the resistor is held at …

Adaptive Building Fabric As A Cyber-Physical System, Hani Alkhatib

Doctoral

Adaptive facades are cyber-physical systems where controlled interventions seek to enhance passive responses to changes in ambient or indoor physical conditions. For buildings in climates that vary seasonally, reducing the energy consumed to maintain occupant comfort can be aided by the presence of an adaptive façade. The performance of adaptive façades has been investigated for (i) daylighting (ii) thermal insulation and (iii) ventilation; both separately and when combined. The systems selected to represent these respective functionalities were (i) an electrochromic glazing, (ii) a thermally-insulated roller blind and (iii) a mechanically-ventilated double-skin façade. Each system was physically fabricated. Their performance was …