Engineering Commons^™

Designing Technology For Different Scales Of Irrigation Scheduling, Paolo Alexander Consalvo

Undergraduate Honors Capstone Projects

Uncertainty in water availability is a significant challenge to the agriculture industry. Farmers and irrigators depend on novel uses of sensors and data to maximize water efficiency. Documented studies have demonstrated scheduling irrigation is a straightforward, deterministic means of achieving water efficiency. Irrigation scheduling uses several parameters to determine the moment of crop water stress due to available water in the soil. However, sensors and data for soil moisture and matric potential, a parameter describing water available to plants, have the potential to train machine learning algorithms to forecast water irrigation needs based on previous measurements. Satellite remote-sensing is another …

Advanced Techniques To Detect Complex Android Malware, Zhiqiang Li

School of Computing: Dissertations, Theses, and Student Research

Android is currently the most popular operating system for mobile devices in the world. However, its openness is the main reason for the majority of malware to be targeting Android devices. Various approaches have been developed to detect malware.

Unfortunately, new breeds of malware utilize sophisticated techniques to defeat malware detectors. For example, to defeat signature-based detectors, malware authors change the malware’s signatures to avoid detection. As such, a more effective approach to detect malware is by leveraging malware’s behavioral characteristics. However, if a behavior-based detector is based on static analysis, its reported results may contain a large number of …

Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh

Faculty & Staff Scholarship

Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …

Drone Proximity Detection Via Air Disturbance Analysis, Qian Zhao, Jason Hughes

Faculty Publications

The use of unmanned aerial vehicles (drones) is expanding to commercial, scientific, and agriculture applications, including surveillance, product deliveries and aerial photography. One challenge for applications of drones is detecting obstacles and avoiding collisions. A typical solution to this issue is the use of camera sensors or ultrasonic sensors for obstacle detection or sometimes just manual control (teleoperation). However, these solutions have costs in battery lifetime, payload, operator skill. We note that there will be an air disturbance in the vicinity of the drone when it’s moving close to obstacles or other drones. Our objective is to detect obstacles from …

Modeling, Designing And Applying Machine Learning Algorithms For Driver Drowsiness Detection, Mohsen Babaeian

CGU Theses & Dissertations

Driver drowsiness has been a significant hazard resulting in various traffic accidents. Therefore, monitoring this condition is crucial not only in alerting drivers, but also in avoiding fatal accidents. Many research studies propose new systems to reduce the number of drowsiness-related injuries and fatalities. The ultimate goal for a drowsiness detection system is to detect the drowsiness on time and minimize the system or environment errors to avoid false readings, such as studying physiological signal processing patterns. These potentially life-saving systems must operate in a timely manner with the highest precision. Researchers proposed various methods based on driving pattern changes, …

Systemic Analysis Of The Use Of Artificial Intelligence (Ai) In Regulating Terrorist Content On Social Media Ecosystem Using Functional Dependency Network Analysis (Fdna), Alaina Roman, C. Ariel Pinto

OUR Journal: ODU Undergraduate Research Journal

This research is a systemic analysis of emerging risks to the use Artificial Intelligence (AI) in regulating terrorist content on social media ecosystems using Functional Dependency Network Analysis (FDNA), a proven system-design-and-analysis tool). The research has three phases: 1) framing the problem by identifying and describing AI ecosystem elements as intended, implied and explicit objectives, discernible attributes, and performance indictors; 2) describing the idealized problem-solved scenario, which includes detailing ‘success’ states of the ecosystem; and 3) systemic risk analysis including identifying failure scenarios for each element and establishing causalities among elemental attributes leading to failure scenarios. This research contributes toward …

Time- And Space-Dependent Uncertainty Analysis And Its Application In Lunar Plasma Environment Modeling, Xinpeng Wei

Doctoral Dissertations

”During an engineering system design, engineers usually encounter uncertainties that ubiquitously exist, such as material properties, dimensions of components, and random loads. Some of these parameters do not change with time or space and hence are time- and space-independent. However, in many engineering applications, the more general time- and space-dependent uncertainty is frequently encountered. Consequently, the system exhibits random time- and space-dependent behaviors, which may result in a higher probability of failure, lower average lifetime, and/or worse robustness. Therefore, it is critical to quantify uncertainty and predict how the system behaves under time- and space- dependent uncertainty. The objective of …

Exploration Of Approaches To Arabic Named Entity Recognition, Husamelddin Balla, Sarah Jane Delany

Conference papers

Abstract. The Named Entity Recognition (NER) task has attracted significant attention in Natural Language Processing (NLP) as it can enhance the performance of many NLP applications. In this paper, we compare English NER with Arabic NER in an experimental way to investigate the impact of using different classifiers and sets of features including language-independent and language-specific features. We explore the features and classifiers on five different datasets. We compare deep neural network architectures for NER with more traditional machine learning approaches to NER. We discover that most of the techniques and features used for English NER perform well on Arabic …

Internet Of Things Based Wireless Sensor Network And Advanced Machine Learning Models For Precision Agriculture, Ahmed El-Magrous

Electronic Theses and Dissertations

Recent studies assumed that the world population would reach 10.3 billion by 2070. This will require more land for housing; simultaneously resulting in a loss of land for agricultural purposes. However, the new generations also need food, and the lack of new agrarian land is a critical reason that leads researchers and producers to improve daily agriculture practices by using precision agriculture concepts and technologies to increase yield and crop quality. This work represents the design, development, and testing of a customizable and cost-effective Weather-Soil Sensor Station (W-SSS) for use in Precision Agriculture based on high accuracy sensors, wireless communication, …

Process Data Analytics Using Deep Learning Techniques, Majid Moradi Aliabadi

Wayne State University Theses

In chemical manufacturing plants, numerous types of data are accessible, which could be process operational data (historical or real-time), process design and product quality data, economic and environmental (including process safety, waste emission and health impact) data. Effective knowledge extraction from raw data has always been a very challenging task, especially the data needed for a type of study is huge. Other characteristics of process data such as noise, dynamics, and highly correlated process parameters make this more challenging.

In this study, we introduce an attention-based RNN for multi-step-ahead prediction that can have applications in model predictive control, fault diagnosis, …

Data Driven Sensor Fusion For Cycle-Cycle Imep Estimation, Cooper Heyne Minehart

Dissertations, Master's Theses and Master's Reports

As the world searches for ways to reduce humanity’s impact on the environment, the automotive industry looks to extend the viable use of the gasoline engine by improving efficiency. One way to improve engine efficiency is through more effective control – effective control systems require a feedback signal. Indicated mean effective pressure (IMEP) is a useful feedback signal for automotive control but is costly to measure directly.

Successful machine learning based sensor fusion requires effective feature extraction and model creation. Through a multistage application of machine learning to both the feature extraction process and the IMEP estimation process we are …

Characterizing And Predicting The Antimicrobial Properties Of Lignin Derivatives, Ryan Kalinoski

Theses and Dissertations--Biosystems and Agricultural Engineering

Due to the overuse of antibiotics in our society, there has been a steady rise in highly antimicrobial-resistant bacteria in the last decade. This has created a renewed interest in natural phenolic compounds for antimicrobial discovery amongst the scientific community. To this end, lignin is the most abundant naturally occurring phenolic polymer on earth and has already been known to have antimicrobial properties due to its polyphenolic structure. In addition, lignin is considered a major waste product for lignocellulosic biorefineries, and its valorization into value-added products will generate extra profit for a biorefinery, making biofuels less expensive, increasing their marketability …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Automatic Chest X-Rays Analysis Using Statistical Machine Learning Strategies, Hermann Yepdjio Nkouanga

All Master's Theses

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, …

Thermodynamic Investigations Of Pure And Blended Cement Mixtures, Jonathan Lapeyre

Doctoral Dissertations

” This research is made up of several studies. The first study focused on understanding the reaction kinetics of Ca₃SiO₅ and metakaolin (MK) mixtures compared to Ca₃SiO₅ and silica fume (SF) mixtures. It was shown that MK was a more effective additive than SF at small replacement levels (i.e. ≥ 10% by mass) while higher replacement levels of MK became a detriment due to excess (Al(OH)₄¯) ions preventing the nucleation and growth of C-S-H. In a follow-up study where the MK particle size distribution (PSD) was modified, similar effects were observed but …

A Neural Network Approach To Estimate Buoy Mooring Line Sensor Deflection, Tom Price

Dissertations, Master's Theses and Master's Reports

Instrumented moorings are often used to measure characteristics, such as temperature and current, over the water column. However, the moorings deflect from the effects of currents and waves, which could lead to innacurate measurements. In this work, a computationally efficient method to compensate for mooring sensor position errors is developed. The two-step process first uses a hydrodynamic model of the buoy and mooring line system to create estimated mooring line deflections in a steady current. A neural network model is trained to approximate the hydrodynamic model’s mooring line displacement given the spatial location of the buoy and current profile measurements. …

Evaluating Flow Features For Network Application Classification, Carlos Alcantara

Open Access Theses & Dissertations

Communication networks provide the foundational services on which our modern economy depends. These services include data storage and transfer, video and voice telephony, gaming, multimedia streaming, remote invocation, and the world wide web. Communication networks are large-scale distributed systems composed of heterogeneous equipment. As a result of scale and heterogeneity, communication networks are cumbersome to manage (e.g., configure, assess performance, detect faults) by human operators. With the emergence of easily accessible network data and machine learning algorithms, there is a great opportunity to move network management towards increasing automation. Network management automation will allow for a reduced likelihood of human …

A Comparison Of Data-Driven And Process-Based Modeling For Nutrient Estimation In A Eutrophic Reservoir, Yohtaro Kobayashi

Open Access Theses & Dissertations

As land use around bodies of water changes, the need to model the body of water increases. Models help to educate, understand, and predict the state of water. Process-based models are commonly used in modelling bodies of water, but there are challenges with these kinds of models. They require data which can be difficult for certain communities to obtain due to logistics or cost, are computationally intensive, technically complicated, and require calibration. In contrast, a data-driven model simply connect relationships from the data, are not as computationally intensive nor technically complicated, and do not require calibration. This research compared a …

Uav Parameter Estimation Through Machine Learning, Andres Enriquez Fernandez

Open Access Theses & Dissertations

Parameter identification of Unmanned Aerial Vehicles (UAV) is very helpful for understanding cause-effect relationships of physical phenomenon, investigating system performance and characteristics, fault diagnostics, control development/tuning, and more. Traditional ways of performing parameter identification involve establishing a mathematical model that describes the system's behavior. The equations in the model contain parameters that are estimated indirectly from measured flight data. This parameter identification process requires knowledge of the physics involved. Also, it necessitates a careful consideration of the aircraft instrumentation for accurate measurements. It also requires careful design of the flight maneuvers to ensure thorough excitation of the flight dynamics involved. …

An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies, Swetha Kondapalli

Browse all Theses and Dissertations

Emergency Medical Service (EMS) providers are the first responders for an injured patient on the field. Their assessment of patient injuries and determination of an appropriate hospital play a critical role in patient outcomes. A majority of states in the US have established a state-level governing body (e.g., EMS Division) that is responsible for developing and maintaining a robust EMS system throughout the state. Such divisions develop standards, accredit EMS agencies, oversee the trauma system, and support new initiatives through grants and training. But to do so, these divisions require data to enable them to first understand the similarities between …

Developing Innovative Spectral And Machine Learning Methods For Mineral And Lithological Classification Using Multi-Sensor Datasets, Chandan Kumar

Dissertations, Master's Theses and Master's Reports

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived …

Optimization Of Home Mortgage Mover Predictive Model Applying Geo-Spatial Analysis And Machine Learning Techniques, Natalia Riscovaia

Dissertations

In the last decade digital innovations and online banking services have significantly changed customers banking preferences and behaviour. Banking industry is going through the changes and developments in the provision of banking services that are affecting the structure and the organization of the bank network. However, private home loan, referred as Home Mortgage hereinafter, continue to remain among the products, that customers prefer to have personal interaction about with professional advisors prior making the decision to apply for the loan with financial institution.

Toward Closing The Urban Surface Energy Balance Using Satellite Remote Sensing, Joshua Hrisko

Dissertations and Theses

The energy exchanges at the Earth’s surface are responsible for many of the processes that govern weather, climate, human health, and energy use. This exchange, commonly known as the surface energy balance (SEB), determines the near-surface thermodynamic state by partitioning the available energy into surface fluxes. The net all-wave radiation is often the primary energy source, while the heat storage and sensible and latent heat fluxes account for the majority of energy distributed elsewhere. While the SEB of various natural environments(trees, crops, soils) has been well-observed and modeled, the urban surface energy balance remains elusive. This is due to the …

Using Statistical Learning Approaches To Understand Trends And Variability Of Tornadoes Across The Continental United States, Niloufar Nouri

Dissertations and Theses

The annual frequency of tornadoes during 1950-2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other …

Top-Down Model Development Using Data Generated From A Complex Numerical Reservoir Simulation With Water Injection, Yvon Andrea Martinez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Numerical simulation and data-driven modeling are two current approaches in engineering reservoir modeling. Numerical reservoir simulation attempts to match past production history by modifying reservoir properties of the model. After multiple computationally intensive trial and error efforts, accurate history matches are identified. These history matches are used by project management for production forecasting purposes. Data-driven reservoir modeling utilizes measured data and is, therefore, free of assumptions that are often included in numerical reservoir simulations. Artificial intelligence and machine learning algorithms are technologies implemented in the development of a data-driven reservoir model with efforts to learn fluid flow through porous media …

Utilization Of A Numerical Reservoir Simulation With Water And Gas Injection For Verification Of Top Down Modeling, Ashley Konya

Graduate Theses, Dissertations, and Problem Reports (ETD)

The primary purpose of this thesis was to confirm the capabilities of artificial intelligence and machine learning through Top Down Modeling in history matching and predicting the oil, gas, and water production rates, reservoir pressure, and water saturation, of one limb of an anticline with water and gas injection. Several other characteristics were also applied to make the model more realistic to industry standards. The second purpose of this thesis was to determine the minimum amount of training and calibration data required in order to obtain good results for this particular dataset by increasing the blind validation in one year …

Predicting Complex System Behavior Using Hybrid Modeling And Computational Intelligence, Vinayaka Gude

Doctoral Dissertations

“Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to …

Studying The Effects Of Various Process Parameters On Early Age Hydration Of Single- And Multi-Phase Cementitious Systems, Rachel Cook

Doctoral Dissertations

”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C₃S) and belite (ß-C₂S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling …

Automatic Gaze Classification For Aviators: Using Multi-Task Convolutional Networks As A Proxy For Flight Instructor Observation, Justin Wilson, Sandro Scielzo, Sukumaran Nair, Eric C. Larson

International Journal of Aviation, Aeronautics, and Aerospace

In this work, we investigate how flight instructors observe aviator scan patterns and assign quality to an aviator's gaze. We first establish the reliability of instructors to assign similar quality to an aviator's scan patterns, and then investigate methods to automate this quality using machine learning. In particular, we focus on the classification of gaze for aviators in a mixed-reality flight simulation. We create and evaluate two machine learning models for classifying gaze quality of aviators: a task-agnostic model and a multi-task model. Both models use deep convolutional neural networks to classify the quality of pilot gaze patterns for 40 …