Engineering Commons^™

Wearable Sensor-Based Walkability Assessment At Ferry Terminal Using Machine Learning: A Case Study Of Mokpo, Korea, Jungyeon Choi, Hwayoung Kim

Journal of Marine Science and Technology–Taiwan

Walkability assessments are becoming more popular, as walking offers numerous health, environmental, and economic benefits to communities. However, previous studies on ferry terminal walkability assessment have been inadequate. This study aimed to develop a wearable sensor system to automatically assess walkability at ferry terminals without conducting surveys. We applied seven machine learning (ML) classifiers to detect different walking environments, including flat ground (FG), downhill slope (DS), uphill slope (US), and uneven surface (UE). The ML models were evaluated across different combinations of classes: 2-class (FG vs. UE), 3-class (U) (FG vs. US vs. UE), 3-class (D) (FG vs. DS vs. …

Using Machine Learning To Predict Hypervelocity Fragment Propagation Of Space Debris Collisions, Katharine Larsen, Riccardo Bevilacqua

Student Works

The future of spaceflight is threatened by the increasing amount of space debris, especially in the near-Earth environment. To continue operations, accurate characterization of hypervelocity fragment propagation following collisions and explosions is imperative. While large debris particles can be tracked by current methods, small particles are often missed. This paper presents a method to estimate fragment fly-out properties, such as fragment, velocity, and mass distributions, using machine learning. Previous work was performed on terrestrial data and associated simulations representing space debris collisions. The fragmentation of high-velocity fragmentation can be modeled by terrestrial fragmentation tests, such as static detonations. Recently, stereoscopic …

Machine Learning Approach To Activity Categorization In Young Adults Using Biomechanical Metrics, Nathan Q. C. Holland

Mechanical & Aerospace Engineering Theses & Dissertations

Inactive adults often have decreased musculoskeletal health and increased risk factors for chronic diseases. However, there is limited data linking biomechanical measurements of generally healthy young adults to their physical activity levels assessed through questionnaires. Commonly used data collection methods in biomechanics for assessing musculoskeletal health include but are not limited to muscle quality (measured as echo intensity when using ultrasound), isokinetic (i.e., dynamic) muscle strength, muscle activations, and functional movement assessments using motion capture systems. These assessments can be time consuming for both data collection and processing. Therefore, understanding if all biomechanical assessments are necessary to classify the activity …

Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.

This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

On The Prediction Of The Mechanical Properties Of Limestone Calcined Clay Cement: A Random Forest Approach Tailored To Cement Chemistry, Taihao Han, Bryan K. Aylas-Paredes, Jie Huang, Ashutosh Goel, Narayanan Neithalath, Aditya Kumar

Materials Science and Engineering Faculty Research & Creative Works

Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder's carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models' prediction performance and interpretability. This …

Accelerating Training Of Mlips Through Small-Cell Training, Jason A. Meziere, Yu Luo, Yi Xia, Laurent Karim Béland, Mark R. Daymond

Mechanical and Materials Engineering Faculty Publications and Presentations

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the α-β …

Real-Time Product Structural Validation For Fused Filament Fabrication, Yanzhou Fu

Theses and Dissertations

Fused filament fabrication (FFF) is one of the fastest-growing, most promising, and widely-used Additive manufacturing (AM) technologies, as its capabilities of largely reducing material waste, feasible and flexible designing complicated structure or geometry product. Except for the advantages mentioned above, the unique affordability has made FFF a popular technique for small-scale 3D printing. However, its widescale adaption for industrial applications still faces significant challenges, especially for Industry 4.0, due to the process’s intrinsic uncertainties. During the printing process, even a trivial bonding gap or material void dramatically influences the final product’s structural quality and mechanical properties. Therefore, a defect detection …

Novel Approach To In-Situ Mocvd Oxide/Dielectric Deposition For Iii-Nitride-Based Heterojunction Field Effect Transistors, Samiul Hasan

Theses and Dissertations

III-Nitride-based compound semiconductors have unique properties such as high bandgap and high breakdown field, which make them attractive for a variety of applications, including high-power and high-frequency electronics and optoelectronics. The most common types of III-Nitride-based field effect transistors (FETs) are aluminum gallium nitride (AlGaN)/gallium nitride (GaN) based, which suffer from some inherent problems such as virtual gate effect, current collapse, gate leakage, etc. The solution to this problem can be the inclusion of a dielectric passivation layer under the gate. However, the addition of the dielectric layer impacts one of the most critical device-controlling parameters, “threshold voltage”, which suffers …

Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer

Turkish Journal of Electrical Engineering and Computer Sciences

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …

Analysing Child Sexual Abuse Activities In The Dark Web Based On An Efficient Csam Detection Algorithm, Vuong Ngo, Christina Thorpe, Susan Mckeever

Articles

Abstract: Child sexual abuse material (CSAM) activities are prevalent on the Dark Web to evade detection, posing a global challenge for law enforcement. Our objective is to analyze CSAM discussions in this concealed space using a Support Vector Machine model, achieving an accuracy of 87.6%. Across eight forums, approximately 28.4% of posts contained CSAM, with victim ages most commonly reported as 12, 14, 13, and 11 years old for YouTube, Skype, Instagram, and Facebook, respectively. Additionally, in forums discussing boys, the most frequently mentioned nationalities in CSAM posts were English, German, and American, accounting for 12%, 7.8%, and 6% of …

A Probabilistic View Of Protein Stability, Conformational Specificity, And Design, Jacob A. Stern, Tyler J. Free, Kimberlee L. Stern, Spencer Gardiner, Nicholas A. Dalley, Bradley Charles Bundy, Joshua L. Price, David Wingate, Dnnis Della Corte

Faculty Publications

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the p(stucture|seq) Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as “BayesDesign”, that leverages Bayes’ Rule to maximize the p(stucture|seq) objective instead of the p(seq|structure) objective common in inverse folding …

Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu

Applied Mathematics & Information Sciences

In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …

Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system …

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

Predicting University Campus Parking Demand Using Machine Learning Models, Sohil Paudel, Matthew Vechione, Okan Gurbuz

Civil Engineering Faculty Publications and Presentations

Parking demand at university campuses has been an issue for decades and is gradually increasing each year. With limited capacity, space, and funds to expand parking facilities, there is a dire need to better understand parking behavior on a university campus so that universities can better utilize the limited resources available. One methodology that has been used by Metropolitan Planning Organizations to predict traveler behavior is known as travel demand modeling, where the most common modeling technique is a four-step procedure that utilizes socioeconomic data to predict current and future traffic volumes in a network (e.g., a city). This study …

Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue

Michigan Tech Publications

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Assessing And Predicting The Students’ Systems Thinking Preference: Multi-Criteria Decision Making And Machine Learning, Siham Tazzit

Theses and Dissertations

The 21st century is marked by a technological revolution that features digital implementation and high interconnectivity between systems across different domains, such as transportation, agriculture, education, and health. Although these technological changes resulted in modern systems capable of easing individuals’ lives, these systems are increasingly complex, and that increased complexity is only expected to continue. The increased system complexity is due to the rapid exchange of information between subsystems, which creates high interconnectivity and interdependence between the subsystems and their elements. Workforce skill sets, as a result, must be modified appropriately to ensure the systems’ success. Systems Thinking is an …

Comparison Of Condition Prediction Models To Prioritize Sewer Pipe Inspections, Madhuri Arjun

Civil Engineering Dissertations - Archive

Over time, wastewater collection systems deteriorate, necessitating ongoing adjustments and the development of asset management frameworks by utility proprietors to maintain the performance of their assets. Any asset management framework should emphasize the importance of asset inspection and condition assessment for system-efficient operation and maintenance. In the United States, closed-circuit television (CCTV) is the most common method for inspecting the interior of sewer pipelines. This procedure is expensive and time-consuming due to a city's extensive inventory of pipes. Due to the immense quantity of these pipes, every municipality can only inspect some sections of sanitary sewer pipes promptly. Therefore, the …

Estimation Of Suspended Sediment Concentration Along The Lower Brazos River Using Satellite Imagery And Machine Learning, Trevor Stull

Civil Engineering Theses - Archive

ABSTRACT: Suspended sediment transport in river basins is important for many water management planning activities to maintain safe drinking water for the community and maintenance of water quality and waterways for the ecosystem. Currently, the traditional way to measure suspended sediment effectively and reliably is by collecting field samples in the river body, which is very time consuming and only provide a point value of suspended sediment within the waterbody at the instant the sample was taken. This thesis focuses on developing models that estimate suspended sediment concentrations for the lower Brazos River using satellite imagery from publicly available data …

Seek And Classify: End-To-End Joint Multi-Signal Detection And Classification Using Deep Learning, Prashant Subedi

School of Computing: Dissertations, Theses, and Student Research

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of Internet of Things (IoT) requires innovative solutions that maximize the use of available spectrum to support the increasing number of connected devices. This thesis tackles two significant problems in wireless communication: the need for efficient spectrum sensing techniques and the scarcity of large, diverse raw in-phase (I) and quadrature (Q) datasets.

The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real time and utilize unused …

Model Optimization And Applications In Deep Learning, Chengchen Mao

Electrical Engineering Dissertations - Archive

ABSTRACT: Machine learning refers to a machine or an algorithm that draws experience from data. A certain pattern is found to build a model, which is used to solve real problems. Deep learning, an important branch and extension of machine learning, employs a neural network structure containing multiple hidden layers. It learns critical features of the data by combining lower-level features to form more abstract higher-level representations of attribute categories or features. In this dissertation, deep learning network models were applied to sense-through-foliage target detection and extended with Rake structure. The deep learning network models had a large number of …

Novel Breath Collection Techniques For Detection Of Covid-19., James Morris

Electronic Theses and Dissertations

Volatile Organic Compounds (VOC) generated endogenously in the human body can be used to detect diseases that induce oxidative stress and inflammation. Breath analysis has been used for the detection of diseases such as COPD, Depression, Lung Cancer and most recently COVID-19. Methods such as Exhaled Breath Condensate (EBC), Sorbent Tubes, Solid Phase Microextraction (SPME), and silicon microreactors have shown considerable capability in extracting and concentrating trace VOC’s present in human breath. Silicon microreactors functionalized with capture agents to derivatize carbonyl compounds are effective but the overall maximum flow rate of breath sample possible during analysis is low, making direct …

Robust Control Of Contact-Rich Robots Via Neural Bayesian Inference, Nardos Ayele Ashenafi

Boise State University Theses and Dissertations

We provide several data-driven control design frameworks for contact-rich robotic systems. These systems exhibit continuous state flows and discrete state transitions, which are governed by distinct equations of motion. Hence, it is difficult to design a single policy that can control the system in all modes. Typically, hybrid systems are controlled by multi-modal policies, each manually triggered based on observed states. However, as the number of potential contacts increase, the number of policies can grow exponentially and the control-switching scheme becomes too complicated to parameterize. To address this issue, we design contact-aware data-driven controllers given by deep-net mixture of experts. …

Achieving High Renewable Energy Integration In Smart Grids With Machine Learning, Yaze Li

Graduate Theses and Dissertations

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. …

Serum Metabolomic Profiling For Colorectal Cancer Using Machine Learning, Ria Nur Puspa Sari, Diah Balqis Ikfi Hidayati, Arleni Bustami

Indonesian Journal of Medical Chemistry and Bioinformatics

Background: Colorectal cancer is one of the deadliest diseases with a high prevalence worldwide and is characterized by the appearance of adenomatous polyps in the colon mucosa which are at high risk of developing into colorectal cancer. This study aims to use serum metabolites as promising non-invasive biomarkers for colorectal cancer detection and prognostication. Differences in serum metabolites in patients with adenomatous polyps, colorectal cancer, and healthy controls are considered to be able to support the prognosis of colorectal cancer. Methods: Metabolite dataset is taken from the Metabolomic Workbench. Analysis and validation are carried out in silico using machine learning …

Using Machine Learning To Assist Auditory Processing Evaluation, Hasitha Wimalarathna, Sangamanatha Veeranna, Minh Vu Duong, Chris Allan Prof, Sumit K. Agrawal, Prudence Allen, Jagath Samarabandu, Hanif M. Ladak

Electrical and Computer Engineering Publications

Introduction: Approximately 0.2–5% of school-age children complain of listening difficulties in the absence of hearing loss. These children are often referred to an audiologist for an auditory processing disorder (APD) assessment. Adequate experience and training is necessary to arrive at an accurate diagnosis due to the heterogeneity of the disorder.

Objectives: The main goal of the study was to determine if machine learning (ML) can be used to analyze data from the APD clinical test battery to accurately categorize children with suspected APD into clinical sub-groups, similar to expert labels.

Methods: The study retrospectively collected data from 134 children referred …