Engineering Commons^™

Coevolution Of Machine Learning And Process-Based Modelling To Revolutionize Earth And Environmental Sciences: A Perspective, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in …

A Self-Learning Intersection Control System For Connected And Automated Vehicles, Ardeshir Mirbakhsh

Dissertations

This study proposes a Decentralized Sparse Coordination Learning System (DSCLS) based on Deep Reinforcement Learning (DRL) to control intersections under the Connected and Automated Vehicles (CAVs) environment. In this approach, roadway sections are divided into small areas; vehicles try to reserve their desired area ahead of time, based on having a common desired area with other CAVs; the vehicles would be in an independent or coordinated state. Individual CAVs are set accountable for decision-making at each step in both coordinated and independent states. In the training process, CAVs learn to minimize the overall delay at the intersection. Due to the …

Artificial Intelligence, Controls, And Sensor Fusion For Optimization And Modeling Of Space Missions And Particle Accelerators, Reza Pirayeshshirazinezhad

Mechanical Engineering ETDs

This PhD dissertation is devoted to developing artificial intelligence (AI) applications for space missions and particle accelerators considering constraints on the computational resources. The space mission studied in this research, the Virtual Telescope for X-ray Observations (VTXO), is the mission exploiting 2 6U-CubeSats operating in a precision formation. The goal of the VTXO project is to develop a space-based, X-ray imaging telescope with high angular resolution precision. VTXO space mission is designed and the mission is optimized to increase the performance of the mission. Trajectory optimization with AI, hybrid control, control algorithms, and high performance computing are all used to …

Leveraging Ai And Supply Chain Technologies With Thermal Imaging And Telemedicine For Early Detection And Prevention Of Covid-19 And Respiratory Infections In Urm Communities, Gohar Azeem

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

The underserved population could be at risk during the times of crisis, unless there is strong involvement from government agencies such as local and state Health departments and federal Center for Disease Control (CDC). The COVID-19 pandemic was a crisis of different proportion, creating a different type of burden on government agencies. Vulnerable communities including the elderly populations and communities of color have been especially hard hit by this pandemic. This forced these agencies to change their strategies and supply chains to support all populations receiving therapeutics. The National Science Foundation (NSF Award # 2028612) funded this research to help …

Ai-Synthesized Speech : Generation And Detection, Ehab Alsayed Albadawy Abdrabuh

Legacy Theses & Dissertations (2009 - 2024)

From speech to images, and videos, advances in machine learning have led to dramatic improvements in the quality and realism of so-called AI-synthesized content. While there are many exciting and interesting applications, this type of content can also be used to create convincing and dangerous fakes. We seek to develop forensic techniques that can distinguish a real human voice from a synthesized voice. We observe that deep neural networks used to synthesize speech introduce specific and unusual artifacts not typically found in human speech. Although not necessarily audible, we develop various detection algorithms to measure these artifacts and be able …

Affordable Autonomous Vehicles For Deployment After Disastrous Events, Shannon Abolmaali

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

In disastrous events such as hurricanes and tornadoes, it has been observed that people get stranded and helpless without a feasible way to escape during those emergency situations. This became very evident during hurricanes, such as Katrina and Ida affecting millions of people seeking immediate rescue efforts. With the use of artificial intelligence and machine learning, we envision an autonomous vehicle, AV, which is able to find the most optimal and safest way to help those who are stranded to get them to a safe location. Electric vehicles, EV, and Autonomous Vehicles, AV, is becoming the future; minimizing the carbon …

Volume Introduction, I. Glenn Cohen, Timo Minssen, W. Nicholson Price Ii, Christopher Robertson, Carmel Shachar

Other Publications

Medical devices have historically been less regulated than their drug and biologic counterparts. A benefit of this less demanding regulatory regime is facilitating innovation by making new devices available to consumers in a timely fashion. Nevertheless, there is increasing concern that this approach raises serious public health and safety concerns. The Institute of Medicine in 2011 published a critique of the American pathway allowing moderate-risk devices to be brought to the market through the less-rigorous 501(k) pathway,1 flagging a need for increased postmarket review and surveillance. High-profile recalls of medical devices, such as vaginal mesh products, along with reports globally …

Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb

Architecture and Planning Journal (APJ)

Natural and human-made disasters have significant impacts on monumental buildings, threatening them from being deteriorated. If no rapid consolidations took into consideration traumatic accidents would endanger the existence of precious sites. In this context, Beirut's enormous 4th of August 2020 explosion damaged an estimated 640 historical monuments, many volunteers assess damages for more than a year to prevent the more crucial risk of demolitions. This research aims to assist the collaboration ability among photogrammetry science, Artificial Intelligence Model (AIM) and Architectural Coding to optimize the process for better coverage and scientific approach of data specific to the crack disorders to …

Deep Learning Approach To Multi-Phenomenological Nuclear Fuel Cycle Signals For Nonproliferation Applications, Preston J. Dicks

Theses and Dissertations

In order to reduce the time required for data analysis and decision-making relevant to nuclear proliferation detection, Artificial Intelligence (AI) techniques are applied to multi-phenomenological signals emitted from nuclear fuel cycle facilities to identify non-human readable characteristic signatures of operations for use in detecting proliferation activities. Seismic and magnetic emanations were collected in the vicinity of the High Flux Isotope Reactor (HFIR) and the McClellan Nuclear Research Center (MNRC). A novel bi-phenomenology DL network is designed to test the viability of transfer learning between nuclear reactor facilities. It is found that the network produces an 84.1% accuracy (99.4% without transient …

Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice

Theses and Dissertations

We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …

Cbct In Clinical Practice, Tarunjeet Pabla Bds, Dmd, Ms, Dip. Abomr, Hugo C. Campos Dds, Dmd, Mds, Dip. Abomr, Aruna Ramesh Bds, Dmd, Ms, Dip. Abomr

The Journal of the Michigan Dental Association

This feature explores the integration of Cone Beam Computed Tomography (CBCT) into dental practice, offering guidelines for best practices. Introduced in 2001, CBCT revolutionized dental radiography, impacting various clinical areas. The article emphasizes the need for clinicians to comprehend CBCT technology, its benefits, and potential risks. It delves into CBCT imaging considerations, technical parameters (Field of View, Voxel Size, Spatial and Contrast Resolution), image viewing, artifacts, machine calibration, and service. Addressing radiation dose, risks, and protection, the article outlines decision-making for 2D vs. 3D imaging. It underscores the responsibility of interpreting CBCT images, legal considerations, return on investment, and the …

State Of Industry 5.0—Analysis And Identification Of Current Research Trends, Aditya Akundi, Daniel Euresti, Sergio Luna, Wilma Ankobiah, Amit Lopes, Immanuel Edinbarough

Manufacturing & Industrial Engineering Faculty Publications

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing …

An Artificial Intelligence Approach To Fatigue Crack Length Estimation From Acoustic Emission Waves In Thin Metallic Plates, Joseph Chandler Garrett, Hanfei Mei, Victor Giurgiutiu

Faculty Publications

The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to be effective in yielding fatigue crack-related information. However, not much research has been performed regarding (i) the correlation between the fatigue crack length and AE signal signatures and (ii) artificial intelligence (AI) methodologies to automate the AE waveform analysis. In this paper, this crack length correlation is investigated along with the development of a novel AE signal …

Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage

Legacy Theses & Dissertations (2009 - 2024)

In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that are most informative for each test instance individually may improve not only the quality of prediction but also the overall interpretability of the model. To this end, in this dissertation, we study the problem of optimizing the trade-off between instance-level sparsity and the quality of prediction using a dynamic instance-wise decision-making approach. Specifically, this approach sequentially reviews features one at a time for each data instance given …

Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control Of Wheeled Mobile Robot: An Event-Triggered Design, Krishanu Nath, Manas Kumar Bera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number …

Remotely Piloted Aircraft C2 Latency During Air-To- Air Combat, David L. Thirtyacre

Publications

Remotely piloted aircraft command-and-control latency could play a significant role during beyond-line-of-sight engagements in future conflicts. As the Air Force prepares to use these systems and artificial intelligence in within-visual-range combat, it must understand the effects of latency, or missing sensor data, during a dogfight. Research indicates technology-based latency influences the engagement outcome geometry similar to a slow decision-making cycle foundational to the understanding of Boyd’s Observe, Orient, Decide, Act (OODA) Loop. This study adds depth to the theory illustrating technology-induced latency has a similar effect as slow human decision making resulting in lower performance. Therefore, when combined with the …

Barriers To Technology Adoption In The Water Treatment Industry, Ian Adams

Williams Honors College, Honors Research Projects

This honors project focuses on barriers to technology adoption in the drinking water treatment industry. The scope of this project considers these barriers as they relate to general technology and with a specific focus on artificial intelligence (AI) and digital twins. To better understand these barriers, prior research and case studies are analyzed. The case studies include reports provided by Fontus Blue, a water treatment software provider based in Akron, Ohio. The case studies exhibit the benefits and challenges of integrating new technology, including AI systems. With the context of these case studies in mind, a survey was distributed to …

Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris

Browse all Theses and Dissertations

A method for the beam forming control of an array of reconfigurable antennas is presented. The method consists of using two parallel convolutional neural networks (CNNs) to analyze a desired radiation pattern image, or mask, and provide a suggestion for the reconfigurable element state, array shape, and steering weights necessary to obtain the radiation pattern. This research compares beam forming systems designed for three distinct element types: a patch antenna, a reconfigurable square spiral antenna restricted to a single reconfigurable state, and the fully reconfigurable square spiral. The parametric sweeps for the design of the CNNs are presented along with …

Emissivity Prediction Of Functionalized Surfaces Using Artificial Intelligence, Greg Acosta, Andrew Reicks, Miguel Moreno, Alireza Borjali, Craig Zuhlke, Mohammad Ghashami

Department of Mechanical and Materials Engineering: Faculty Publications

Tuning surface emissivity has been of great interest in thermal radiation applications, such as thermophotovoltaics and passive radiative cooling. As a low-cost and scalable technique for manufacturing surfaces with desired emissivities, femtosecond laser surface processing (FLSP) has recently drawn enormous attention. Despite the versatility offered by FLSP, there is a knowledge gap in accurately predicting the outcome emissivity prior to fabrication. In this work, we demonstrate the immense advantage of employing artificial intelligence (AI) techniques to predict the emissivity of complex surfaces. For this aim, we used FLSP to fabricate 116 different aluminum samples. A comprehensive dataset was established by …

Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …

Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those …

Ai-Driven Automated Medical Imaging Analysis, Jingya Liu

Dissertations and Theses

Medical imaging has been applied widely in many clinical diagnoses to detect and differentiate abnormalities by revealing the internal structure of the human body at normal anatomical and physiological levels. Manual analyzing medical images demands attention and is time-consuming, requiring well-trained expertise. The speed, fatigue, and experience may limit the diagnostic performance, leading to delays and even false diagnoses that significantly impact patient treatment. Therefore, accurate systematic systems based on medical image analysis are crucial for timely clinical diagnosis.

This dissertation focuses on advancing automatic computer-aided diagnosis systems to detect cancer, assisting radiologists with early intervention to improve survival rates. …

Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii

Other Publications

It may seem counterintuitive to open a book on medical devices with chapters on software and data, but these are the frontiers of new medical device regulation and law. Physical devices are still crucial to medicine, but they – and medical practice as a whole – are embedded in and permeated by networks of software and caches of data. Those software systems are often mindbogglingly complex and largely inscrutable, involving artificial intelligence and machine learning. Ensuring that such software works effectively and safely remains a substantial challenge for regulators and policymakers. Each of the three chapters in this part examines …

Artificial Intelligence-Based Material Discovery For Clean Energy Future, Reza Maleki, Mohsen Asadnia, Amir Razmjou

Research outputs 2022 to 2026

Artificial intelligence (AI)-assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low-carbon economy that is material-intensive. AI-assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI-assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This …

Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …

Robotic Olfactory-Based Navigation With Mobile Robots, Lingxiao Wang

Doctoral Dissertations and Master's Theses

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods.

A. …

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol

Theses and Dissertations

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …

Airfoil Flow Optimized Control With An Upstream Cylinder, Nicole Steiner

Fall Student Research Symposium 2021

The purpose of this research is to optimize the aerodynamic performance of an airfoil with an upstream cylinder by neural network artificial intelligence. The effects of an upstream oscillating cylinder on the aerodynamic performance of an airfoil are also studied. This paper reports the effects oscillating frequency of the cylinder and the Reynolds number on the lift/drag ratio of the airfoil. The frequency has a complicated correlation with the lift/drag ratio, while the Reynolds number is found to have a positive correlation with the lift/drag ratio. The optimized case is found to have a lift/drag ratio of 1.7319, which is …

Trajectory Generation For A Multibody Robotic System: Modern Methods Based On Product Of Exponentials, Aryslan Malik

Doctoral Dissertations and Master's Theses

This work presents several trajectory generation algorithms for multibody robotic systems based on the Product of Exponentials (PoE) formulation, also known as screw theory. A PoE formulation is first developed to model the kinematics and dynamics of a multibody robotic manipulator (Sawyer Robot) with 7 revolute joints and an end-effector.

In the first method, an Inverse Kinematics (IK) algorithm based on the Newton-Raphson iterative method is applied to generate constrained joint-space trajectories corresponding to straight-line and curvilinear motions of the end effector in Cartesian space with finite jerk. The second approach describes Constant Screw Axis (CSA) trajectories which are generated …

Modeling Driver Distraction Mechanism And Its Safety Impact In Automated Vehicle Environment., Song Wang

Electronic Theses and Dissertations

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly …