Engineering Commons^™

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. …

High Performance Scene Generator For Testing Of Imaging Sensors, Austin Modoff

Electrical Engineering Theses and Dissertations

Since the invention of modern solid-state imaging sensors in the 1980s, considerable advancements have been made, improving aspects such as frequency response, dynamic range, resolution, and wavelength diversity, as well as inventing many different types of sensor architectures such as framing sensors and event sensors. While considerable improvements in this field are evolving every day, the methods by which to test and validate these systems remain unchanged. Many of these test platforms require specialized designs to test only one or two specific qualities. For example, a spinning disk in front of a black body source to measure the frequency response …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Tribological Behavior Of Polydopamine/Polytetrafluoroethylene Coating On Laser Textured Stainless Steel With Hilbert Curves, Firuze Soltani-Kordshuli, Nathaniel Harris, Min Zou

Mechanical Engineering Faculty Publications and Presentations

Shallow Hilbert curve patterns with easily programmable texture density were selected for laser texturing of stainless steel substrates. Two different texture path segment lengths (12 and 24 mu m) and four different laser power percentages (5%, 10%, 15%, and 20%) were investigated. The textured and smooth substrates were coated with thin polydopamine/polytetrafluoroethylene (PDA/PTFE) coatings for tribological property assessment. The effects of texture density (texture area coverage) and laser power on the durability and friction of the coated surfaces were studied. Laser texturing the substrates improved the coating durability up to 25 times, reduced the friction coefficient, and prevented coating global …

Magnetic Softness Tuned Superparamagnetic Nanoparticles For Highly Efficient Cancer Theranostics, Jie Wang

Theses and Dissertations

Magnetic resonance imaging (MRI)-guided magnetic nanofluid hyperthermia (MNFH) using iron oxide based superparamagnetic nanoparticles (SPNPs) has recently attracted considerable attention as a treatment modality for cancer theranostics, because MRI-guided MNFH can allow for diagnosis, therapeutics, and prognosis simultaneously using the same administrated magnetic nanofluid agent. However, several primary limiting factors: (1) insufficient AC magnetic heating induction (specific loss power/intrinsic loss power, SLP/ILP) at the biologically safe and physically tolerable range of AC magnetic field (H_AC,safe: f_appl × H_appl < 3.0 ~ 5.0×10⁹ A·m^-1·s^-1), (2) low _r2- relaxivity directly related to the low resolution of …

Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez

Theses and Dissertations

Currently, heat transfer in many industries is the limiting factor for innovation, especially in the energy sector. For example, maximizing thermal conductivity of ceramic coatings in power plant devices improves the overall electrical to thermal energy ratio, whereas minimizing thermal conductivity is required for desirable heat-to-electricity conversion in thermoelectric devices. As such, rapid discovery of new materials with extreme thermal conductivity values is quintessential for the near-future deployment of current and developing energy applications.

The vibrational properties of crystalline materials are essential for their ability to conduct heat. Fundamentally, the restorative atomic forces of displaced atoms are sufficient to represent …

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 …

Developing A Vision-Based Framework For Measuring And Monitoring Water Resource Systems Using Computer Vision And Deep Learning Techniques, Seyed Mohammad Hassan Erfani

Theses and Dissertations

Increased vulnerability of water systems to extreme events and climate change is among the profound challenges facing the management of water resource systems around the world. Extreme events, including droughts, floods, and natural hazards have become more frequent and intensive, particularly in coastal regions. Floods, for instance, caused tens of billions of US dollars losses and put the lives of thousands in danger, globally. To cope with the adverse consequences of floods, a wide range of structural, non-structural, and emergency measures are studied and deployed by flood management sectors. Various flood simulation, mapping, and forecast systems have been developed to …

Pectus Carinatum Patient Brace Data, Brandon Scott Sargent, Katie Varela, Dennis Eggett, Emily Mckenna, Christina Bates, Rebeccah Brown, Victor Garcia, Larry Howell

ScholarsArchive Data

Data for the journal article "Modeling of the chest wall response to prolonged bracing in pectus carinatum" submitted to PLOS One in 2023 by Sargent et al. The data is a recording of prescribed brace pressures, patient reported hours of wear, and clinician recorded pressure to correct through the treatment process. The data file was compiled via Microsoft Excel as part of the Microsoft 365 Office Suite. The top row of each sheet within the file is the header line with the data in all subsequent rows. The journal article contains an in-depth definition of the variables used in the …

Tio2 Coating Methods On Sb2o3 And Their Use As Anode Material In Lithium-Ion Batteries, Kithzia Czarina Gomez

Theses and Dissertations

The following thesis is concentrated on the development of TiO2Sb2O3 composite material through the hydrolysis of TiCl4 to be applied as anode material for lithium-ion batteries. Antimony (Sb) has been a material of interest due to its high capacity and good chemical properties. The TiO2 accommodated the harsh volume expansion that Sb encounters during the alloying process while still preserving the high capacity offered by Sb. Its electrochemical performance exhibited a stabilized capacity of 523 mAhg-1 after reaching 40 cycles with a capacity loss of 2.4% till reaching 100 cycles. High reversible capacity characteristics were present with an initial capacity …

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 …

A Dynamical Systems Approach To Characterizing Brain–Body Interactions During Movement: Challenges, Interpretations, And Recommendations, Derek C. Monroe, Nathaniel T. Berry, Peter C. Fino, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Brain–body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of ‘brain’ activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a …

A Robotic System For In-Situ Measurement Of Soil Total Carbon And Nitrogen, Husein Harun

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Surges in the cost of fertilizer in recent times coupled with the environmental effects of their over-application have driven the need for farmers to optimize the amount of fertilizer they apply on the farm. One of the key steps in determining the right amount of fertilizer to apply in a given field is measuring the amount of nutrients present in the soil. To ascertain nutrient deficiencies, most farmers perform wet chemistry analysis of soil samples which requires a lot of time and is expensive. In this research project, a robotic system was designed and developed that could autonomously move to …

Distributed Deep Learning Optimization Of Heat Equation Inverse Problem Solvers, Zhuowei Wang, Le Yang, Haoran Lin, Genping Zhao, Zixuan Liu, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

The inversion problem of partial differential equation plays a crucial role in cyber-physical systems applications. This paper presents a novel deep learning optimization approach to constructing a solver of heat equation inversion. To improve the computational efficiency in large-scale industrial applications, data and model parallelisms are incorporated on a platform of multiple GPUs. The advanced Ring-AllReduce architecture is harnessed to achieve an acceleration ratio of 3.46. Then a new multi-GPUs distributed optimization method GradReduce is proposed based on Ring-AllReduce architecture. This method optimizes the original data communication mechanism based on mechanical time and frequency by introducing the gradient transmission scheme …

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 …

Using Machine Learning And Distributed Hydrologic Modeling To Predict Soil Texture, Surface Soil Moisture And Evapotranspiration In Jornada Experimental Range, Southwestern U.S., Jorge Andres Mayo

Open Access Theses & Dissertations

In water-limited ecosystems, detailed knowledge of the soil, vegetation, and atmosphere interactions is critical to understand the processes that control the partitioning of energy, water fluxes, and biogeochemical cycles within the critical zone. This Master's thesis is divided into two main contributing sections. The first, is on the use of machine learning to reconstruct missing soil type information, and the second, on the calibration and validation of a physically-based distributed hydrological model to estimate soil moisture and evapotranspiration within the Jornada Experimental Range of the U.S. in southern New Mexico. For the first contribution, three explainable, shallow machine-learning techniques are …

Construction Semantics And Generative Pre-Trained Transformer (Gpt) Language Models, Ankur Mitra, Ahmed Hassan, Mark Mulville

Articles

The article explores the impact of ChatGPT, a powerful language model developed by OpenAI, on the construction industry. ChatGPT, with its ability to provide contextually accurate responses to a wide range of queries, has the potential to enhance safety, scheduling, and knowledge dissemination in construction. While it shows promise in tasks like hazard recognition and project scheduling, the article emphasizes the need for cautious optimism and human oversight. The construction industry could benefit from ChatGPT's digital assistance to boost productivity and address skills shortages, but it is essential to recognize the value of human expertise alongside AI advancements.

Creep Resistance And Microstructure In Binary Aluminum Cerium Alloy Produced By Laser Powder Bed Fusion, Jillian Ann Stinehart

Master's Theses (2009 -)

Currently, the only commercially available aluminum alloy for additive manufacturing (AM) is AlSi10Mg, which is not suitable for high temperature applications. Al-Ce based alloys have been shown to be highly printable, cost-efficient alloys. Compared to cast Al-Ce alloys, the eutectic features are refined (<1μm), which give AM Al-10Ce favorable strength and ductility at room temperature. The low diffusivity and solubility of cerium in aluminum improve the retention of mechanical properties at high temperatures. In order to quantify the effect of cerium on the thermal stability of AM aluminum and show its suitability for high-temperature applications, Al-10Ce was creep tested between 60-77% of its absolute melting temperature. The creep performance of AM Al-10Ce was favorable compared to that of cast binary Al-Ce and AM AlSi10Mg and was comparable to that of cast ternary Al-Ce alloys. The stress exponent, n, was approximately 1 in the low stress regime and 5-7 in the high stress regime. The activation energy was 231kJ/mol. In comparison, both cast binary Al-Ce and AM AlSi10Mg have higher stress exponents and lower activation energies, showing AM Al-10Ce to be more creep resistant. After creep testing, slight grain coarsening was observed, while grain orientation remained unchanged. The melt pool boundaries (MPBs) faded in appearance after creep testing, and the number of columnar grains decreased.