Engineering Commons^™

A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced By Grey Wolf Optimization For Dc–Dc Boost Converters With Hardware-In-Loop Validation, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

This paper proposes a universal hybrid model-free quantum–transfer learning controller with enhanced online grey wolf optimization algorithm (GWO–QTL) for DC–DC boost converter. This system has the characteristics of non-minimum phase behavior, parasitic effects, and fractional-order dynamics because of high frequency operation. These characteristics make analytical modeling complicated and make it difficult to have a single traditional controller that will operate reliably over different converter types. This motivates the creation of a unified model-free control framework that is able to learn directly from the behavior of the converter without relying on the topology specific models. Reinforcement learning, where an agent interacts …

A Novel Deep Transfer Learning-Based Adaptive Cascade Pi Controller Enhanced By Reinforcement Learning Algorithm And Snake Optimization For Robust Speed Regulation Of Brushless Dc Motors, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) are common in electric cars, industrial automation, and robotics because of their high efficiency, high torque control, and compact size. Nevertheless, strong speed and current regulation is not easily attained because of system variation, load variations and the shortcomings of traditional fixed-gain proportional-integral (PI) controllers. In this paper, a new snake optimization-assisted deep transfer learning-based reinforcement learning (SOA-DTL-RL)-based adaptive cascade PI controller is proposed that combines transfer learning with fast adaptation, Reinforcement learning with real-time optimization, and snake optimization with optimal initial gain selection to guarantee the robust speed and current regulation in BLDC motors. The proposed …

Ai-Enhanced Smart Sensors For Heavy Metal Detection In Water Treatment, Fatemeh Noorisafa, Amir Razmjou, Asghar Taheri-Kafrani, Fatemeh Ejeian, Mohsen Asadnia, Hamidreza Akbari Ghavamabadi

Research outputs 2022 to 2026

Artificial intelligence (AI) enhances biosensor design by efficiently processing and modeling environmental data. This study employs machine learning algorithms to optimize biosensor parameters for the detection of trace-level heavy metals in aquatic environments, utilizing enzymes, DNAzymes, and aptamers as recognition elements. Machine learning models, including decision trees, random forests, gradient boosting, ensemble neural networks, and GLMM,were trained on extensive laboratory datasets. Among these, the random forest model exhibited the highest predictive accuracy, achieving 71 % for the limit of detection (LOD), 75 % for the minimum concentration of linearity, and 62 % for the maximum concentration of linearity. The AI-driven …

Enhanced Strength And Corrosion Resistance Of Ti-13nb-12ta-10zr-4sn Alloy By Aging Treatment, Yuhua Li, Rong Zhao, Qian Zhang, Haojie Wang, Yujing Liu, Lai Chang Zhang

Research outputs 2022 to 2026

This work investigates the effect of aging treatment on the mechanical properties and corrosion resistance of Ti-13Nb-12Ta-10Zr-4Sn (TNTZS) alloy prepared by vacuum arc melting. The as-cast TNTZS alloy was solution-treated and aged at 450°C (HT450) and 550°C (HT550). Microstructural characterization revealed significant changes in the phase proportion (in vol%), which transformed from 64% β, 19% α, and 17% α″ in as-cast condition to 54% β, 13% α, and 33% α″ for HT450 and 55% β, 25% α, and 20% α″ for HT550, respectively. The tensile strength and yield strength increased from 608 and 424 MPa in as-cast condition to 1192 …

Influence Of Surface Features On Heat Transfer During Dropwise Condensation Over Superhydrophobic Surfaces In Shear Flow, Shaur Humayun, R. Daniel Maynes, Julie Crocket, Brian D. Iverson

Faculty Publications

This study investigates heat transfer during dropwise condensation (DWC) on superhydrophobic (SH) surfaces in humid air shear flow, emphasizing the effect of increased drop mobility and the influence of surface micro/nanostructure on heat transfer rates. Experiments were conducted on smooth hydrophobic, microstructured SH, nanostructured carbon-infiltrated carbon nanotube (CICNT) surfaces, and two-tiered SH surfaces with both micro and nanostructures. Heat transfer rates were measured under humid air flow rates in the range of 2–4 CFM. Experimental results demonstrate that surfaces with nanostructure (including two-tiered structures) exhibit increased drop mobility and coalescence-induced drop jumping, enhancing drop removal rates and overall heat transfer …

Machine-Learning-Guided Design Of A Biomedical High-Entropy Alloy For Additive Manufacturing: Cast-State Benchmark And Preliminary Lpbf Feasibility Assessment, Deyu Jiang, Lai Chang Zhang, Kuaishe Wang, Wen Wang, Chenyuan Zhu, Yuanfei Fu, Kai Wang, Wei Zhai, Ching Chiuan Yen, Weijie Lu, Di Zhang, Liqiang Wang

Research outputs 2022 to 2026

Additive manufacturing of biomedical high-entropy alloys (BioHEAs) demands a combination of low elastic modulus, high strength, and damage tolerance, yet composition discovery remains largely empirical. Here, we establish a machine-learning framework that couples virtual screening with physical prototyping to link composition, deformation mechanism, and properties. Ensemble models for strength, elongation, and modulus were applied to screen Ti–Zr–Nb–Ta–Mo-centered quinary-to-septenary spaces (∼15 million compositions), revealing discrete performance islands anchored by a Ti–Zr backbone. A Zr-rich BCC alloy (Zr₃₉.₃Ti₁₉.₅Nb₁₇.₉Ta₁₆.₈Mo₆.₅) was identified and validated. In the as-cast state, it delivers ∼1.0 GPa yield strength, 22.3% elongation, and an 88 GPa elastic modulus; ductility originates …

Multi-Objective Hydro-Thermal Optimization Of Spiral Conformal Cooling Channels Using A Kriging-Cfd Framework, Soroush Masoudi, Barun K. Das, Majid Tolouei-Rad

Research outputs 2022 to 2026

This study presents a surrogate-based computational framework for the thermo-hydraulic optimization of spiral conformal cooling channels (CCCs) used in injection moulding applications. Sixteen design configurations were generated using Latin Hypercube Sampling and evaluated through CFD simulations to determine cooling time and pressure drop. Based on the simulation data, a Universal Kriging surrogate model was developed to describe the nonlinear relationships between channel diameter, helix pitch, channel-to-surface distance, and the resulting thermo-fluid performance. The predictive capability of the surrogate model was evaluated using leave-one-out cross-validation. The model showed excellent agreement for pressure drop prediction (R² ≈ 0.99) and good predictive accuracy …

Optimizing The Size And Siting Of Distributed Generation In Unbalanced Distribution Systems With Multi-Objective Reptile Search Algorithm, Pema Dorji, Chimi Pelden Dorji, Stefan Lachowicz, Octavian Bass

Research outputs 2022 to 2026

This paper presents the Multi-Objective Reptile Search algorithm for identifying ideal rating and location of distributed generation in unbalanced grids, focusing on minimizing power losses, total costs, and carbon emissions. The proposed methodology integrates DIgSILENT PowerFactory and Python platforms to evaluate unbalanced IEEE distribution feeders under varying power factor conditions. The results demonstrate that optimal DG configurations involve strategically positioning multiple units to enable both active and reactive power injection, significantly improving overall system performance across multiple objectives and voltage deviation index. The analysis identifies an optimal PF range between 0.75 and 0.89, with unity power factor operations yielding suboptimal …

Mission-Focused Multidisciplinary Design Optimization Of Tilt-Rotor Evtol Propulsion System, Tyler Critchfield, Andrew Ning

Faculty Publications

Tilt-rotor propulsion system design requires a multidisciplinary approach to tackle important challenges and competing tradeoffs between disciplines. In this paper, we model rotor aerodynamics, blade structures, vehicle drag, electric propulsion, and tonal/broadband acoustics for a tilt-rotor, electric vertical takeoff and landing aircraft using low-to-mid fidelity tools. We use gradient-based design optimization with automatic differentiation and parameter sensitivity analyses to explore the design space and complex tradeoffs of tilt-rotor distributed electric propulsion systems, exploring effects of variations in payload/empty weight, battery specific energy, and blade tip speed. This framework models multiple operating points with a mission-focused objective to account for the …

A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Enhanced For Speed Regulation Of Brushless Dc Motor, Seyyed Morteza Ghamari, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better …

Performance Investigation Of Microchannel Heat Sink With Biomimetic Trefoil Cavities On Different Walls, Sadiq Ali, Numan Habib, Faraz Ahmad, Aamer Sharif, Ana Vafadar

Research outputs 2022 to 2026

The modern world is shifting towards digitalization and miniaturization, leading to higher flux densities in electronic components and machines. However, conventional cooling methods, such as air-cooled smooth channels, are proving inadequate for removing the huge amounts of heat generated, thereby compromising the reliability and operational lifespan of electronic systems. This necessitates urgently exploring and analyzing modern techniques like microchannel cooling to improve its efficiency. This work uses ANSYS to conduct numerical simulations and investigates the heat transfer and flow behavior in a microchannel heat sink. The smooth channel is used to investigate and validate the flow behavior with the available …

Covert Transmission For Active Ris-Aided Full-Duplex Uav Integrated Sensing, Communication, And Computation Systems, Qi Zhang, Wei Gao, Chuan Liu, Yu Yao, Shihao Yan, Feng Shu, Shi Jin

Research outputs 2022 to 2026

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone …

Design Of Artificial Interference Signal Waveforms For Covert Communication Aided By Multiple Friendly Nodes, Xuyang Zhao, Wei Guo, Yongchao Wang, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider a covert communication scenario with multiple friendly interference nodes. The goal is to hide a legitimate communication link from a transmitter to a receiver under a warden’s surveillance. Firstly, we propose a novel strategy for generating artificial noise (AN) signals and formulate a corresponding design problem, aiming to minimize the adverse effects of AN on the legitimate receiver while enhancing communication covertness. Specifically, we optimize the basis matrix for AN signal space using statistical information of the involved channel coefficients, when precise channel state information are unavailable. Secondly, we analyze the geometric structure of the …

Sensing-Then-Beamforming: Robust Transmission Design For Ris-Empowered Integrated Sensing And Covert Communication, Xingyu Zhao, Min Li, Ming Min Zhao, Shihao Yan, Min Jian Zhao

Research outputs 2022 to 2026

Traditional covert communication often relies on the knowledge of the warden's channel state information, which is inherently challenging to obtain due to the non-cooperative nature and potential mobility of the warden. The integration of sensing and communication technology provides a promising solution by enabling the legitimate transmitter to sense and track the warden, thereby enhancing transmission covertness. In this paper, we develop a framework for sensing-then-beamforming in reconfigurable intelligent surface (RIS)-empowered integrated sensing and covert communication (ISACC) systems, where the transmitter (Alice) estimates and tracks the mobile aerial warden's channel using sensing echo signals while simultaneously sending covert information to …

Sustainable Textiles Through Electrospinning: Addressing Environmental Challenges, Nicole Sharples, Omid Doustdar, Miguel Carmona‐Cabello, Shayan Abrishami, Jose M. Herreros, Ali Sadaghiani, Karl D. Dearn

Research outputs 2022 to 2026

The textile industry, known for its significant environmental footprint, faces growing pressure to adopt sustainable practices. This review identifies the textile industry’s sustainability challenges and examines electrospinning as a promising technology to mitigate its environmental impact. Electrospinning enables the production of nanofibers with enhanced properties suitable for various textile applications, offering potential solutions to reduce resource consumption and waste generation. This review discusses the current uses of electrospinning in textile manufacturing and analyses emerging trends in textile recycling, emphasising the role of electrospinning in transforming waste textiles into value-added products. Prospects for advancing electrospinning techniques to achieve efficient textile recycling …

A Review On Underwater Beamforming: Techniques, Challenges, And Future Directions, Ruba Zaheer, Quoc Viet Phung, Iftekhar Ahmad, Asma Aziz, Daryoush Habibi, Yue Rong, Walid K. Hasan

Research outputs 2022 to 2026

This paper comprehensively reviews recent advancements in Underwater Beamforming (UWB) systems, highlighting its pivotal role in underwater communication, sensing, and environmental monitoring. It explores the various beamforming applications, ranging from maritime surveillance to marine life monitoring, and indicates its significance in enhancing signal clarity, spatial resolution, and noise suppression in underwater acoustic environments. The unique challenges posed by the underwater environment that introduce complexities into the beamforming process such as non-stationary noise interference, severe signal attenuation, multipath propagation, and dynamic environmental variability are thoroughly discussed. The review systematically discusses and examines conventional, adaptive, and learning-based beamforming techniques, analyzing their strengths, …

Hybrid Learning And Optimization Methods For Solving Capacitated Vehicle Routing Problem, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose a hybrid quantum–classical framework for the Capacitated Vehicle Routing Problem (CVRP) that integrates the Augmented Lagrangian Method (ALM) with deep reinforcement learning (RL). Directly solving CVRP via Variational Quantum Eigensolver (VQE) requires a slack-based QUBO formulation, where converting inequalities to equalities greatly increases the qubit count. To circumvent this, we employ an ALM-based reformulation that enforces constraints through Lagrange terms instead of slack variables, drastically reducing quantum resource demands. An RL agent, trained with Soft Actor–Critic, adaptively tunes the Lagrange penalties to improve convergence and feasibility. Experiments show that RL-Q-ALM outperforms static-penalty and plain VQE baselines in both …

Forecasting Inland Waterway Container On Barge Volume: A Machine Learning Approach Using Economic Features, Fan Bu, Heather Nachtmann

Industrial Engineering Faculty Publications and Presentations

This study presents a machine learning approach to predict Container-on-Barge (COB) volume in Inland Waterway Transportation (IWT) systems, focusing exclusively on using economic features as predictors. Five machine learning models were trained using European economic features to forecast COB volume, while historical COB volume was used solely for validation and hyperparameter tuning. Among these models, the convolutional neural network combined with long short-term memory (CNN-LSTM) exhibited superior performance, achieving a mean absolute percentage error (MAPE) of 1.08% when forecasting eight consecutive quarters of COB volume in Europe. The results demonstrate the feasibility of accurately forecasting COB volume using economic features. …

Digital Twinning To Advance Effluent Water Treatment For Pulp And Paper Mills: A Data-Driven Approach For Process Optimization, Fatima Iqbal, Fatemeh Naeijian, Christopher Page, George Fu, Francisco Cubas-Suazo, Stetson Rowles

Civil Engineering & Construction: Faculty Publications

The pulp and paper industry is the third largest freshwater consumer and faces pressure to minimize water usage and environmental impact. Aerated stabilization basins (ASBs) play a crucial role in treating their wastewater, yet existing open-source models often fail to accurately predict treatment efficiency across varying conditions. This study addresses this gap by advancing ASB modeling for ultimate oxygen demand prediction. Specifically, we improved accuracy with variable temperature corrections and incorporating uncertainty to capture the variability in outputs. The models demonstrated strong predictive performance for ultimate oxygen demand (mainly carbonaceous and nitrogenous biochemical oxygen demand) with R2 values ranging from …

Structure Of Jupiter's High-Latitude Storms: Folded Filamentary Regions Revealed By Juno, L. N. Fletcher, Z. Zhang, S. Brown, F. A. Oyafuso, J. H. Rogers, M. H. Wong, S. Brueshaber, Et. Al

Michigan Tech Publications

Sprawling, turbulent cloud formations dominate the meteorology of Jupiter's mid‐to‐high latitudes, known as Folded Filamentary Regions (FFRs). A multi‐wavelength characterization by Juno reveals the spatial distribution, vertical structure, and energetics of the FFRs. The cloud tops display multiple lobes of stratiform aerosols, separated by darker, cloud‐free lanes, and embedded with smaller eddies and high‐altitude cumulus clouds. These cyclonic FFRs are microwave‐bright in shallow‐sounding wavelengths (p < 5 bars) and microwave‐dark in deep‐sounding wavelengths (p > 10 bars), with the transition potentially associated with the water condensation layer (6–7 bars). Associating microwave contrasts with temperature anomalies, this implies despinning of cyclonic eddies above/below their mid‐planes. Despite deep roots (being detectable in …

Pressure Field Estimation From 2d-Piv Measurements: A Case Study Of Fish Suction-Feeding, Jensine C. Coggin, Duvall Dickerson-Evans, Erin E. Hackett, Roi Gurka

Physics and Engineering Science

Particle image velocimetry (PIV) flow measurements are common practice in laboratory settings in a wide variety of fields involving fluid dynamics, including biology, physics, engineering, and medicine. Dynamic fluid pressure is a notoriously difficult property to measure non-intrusively, yet its variation is a driving flow force and critical to model correctly. Techniques have been developed to estimate the pressure from velocity and velocity gradient measurements. Here, we highlight a novel application of boundary conditions when applying such pressure estimation techniques based on two-dimensional PIV data; the novel method is especially relevant to problems with complex boundary conditions. As such, it …

Using Machine Learning To Predict Women At Risk Having A Child With Congenital Heart Defects, Amany M. Abdo Prof., Asmaa M. Mosallam Ms., Laila M. Abdelhamid Assoc.Prof.

Information Systems

Congenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and …

Virtual Process Modeling Of Metal Additive Manufacturing Basedon Direct Energy Deposition In-Situ Failure, Jachin J. Ramirez

2025 Fall Honors Capstones Projects - Archive

This study builds a mesoscale finite element simulation to examine how internal stresses form during additive metal manufacturing using directed energy deposition. The goal is to track how heat and stress develop layer by layer and determine where a failure criterion could appear during the print. The model uses temperature-dependent material properties for Inconel 718 and a moving heat source defined with custom G-code.

Thermal results are mapped into a mechanical simulation to watch stress accumulate as new layers are added. Different scan paths were tested to determine whether varying heat exposure could reduce the extent of a region exceeding …

Visualizing The Effect Of Process Pause On Virus Entrapment During Constant Flux Virus Filtration, Wenbo Xu, Xianghong Qian, Hironobu Shirataki, Daniel Straus, Sumith Ranil Wickramasinghe

Biomedical Engineering Faculty Publications and Presentations

Virus filtration is an essential unit operation used to validate clearance of adventitious virus during the manufacture of biopharmaceutical products such as monoclonal antibodies. Obtaining at least a 10,000-fold reduction in virus particles in the permeate is challenging as monoclonal antibodies are about half the size of the virus particles. Minute virus of mice, FDA-recommended model adventitious virus, was labeled with a fluorescent dye. Laser scanning confocal microscopy was used to determine the location of virus entrapment within the virus filtration membrane. Three different hollow fiber membranes made of regenerated cellulose and polyvinylidene fluoride were tested. Feed streams consisted of …

Vibrissae-Inspired Vision-Based Magnetic-Actuated Whisker, Zhixian Hu, Yi Cheng, Juan P. Wachs, Yu She

School of Industrial Engineering Faculty Publications

Tactile perception is significant for robotic operation in unstructured environments. Whisker-based sensors offer lightweight bio-inspired solutions, yet most rely on single-whisker sensing and are limited by passive interaction and constrained functionality. Here we present a circular array of eight independently actuated whiskers, each driven by a pulse-switchable permanent magnet and tracked by a camera. This design enables simultaneous multi-point sensing and coordinated actuation, supporting diverse functions. Quantitative analyses demonstrate accurate pixel-to-physical mapping, consistent pixel-to-force characterization, and long-term repeatability. In this work, we show that an vibrissae-inspired vision-based magnetic-actuated whisker array integrating distributed perception with active interaction achieves reliable physical mapping, …

Accurate Prediction Of Geometrical Parameters Of An Ultra-Broadband Metamaterial Absorber Using Machine Learning, Md. Rezwan Ahmed, Oishi Jyoti, Pritu Parna Sarkar, Mohammod Abdul Motin, Md. Selim Habib, Md. Samiul Habib

Electrical and Computer Engineering Faculty Publications

In this paper, we systematically demonstrate the design and analysis of a new type of ultra-broadband tunable metamaterial perfect absorber (MPA) comprising a top vanadium dioxide (VO2) based patterned resonating patch, a continuous metallic film at the bottom, and an intermediate dielectric substrate having a thickness of only 0.18 at the center working frequency. The simulation results reveal that the absorber achieves a bandwidth of 7.26 THz, ranging from 5.40 THz to 12.66 THz, with more than 90% absorptance and an average absorption of 98.21% under normal incidence of the incoming THz wave. Furthermore, absorptance exceeding 99% is achieved between …

Integrated Chitosan-Based Coagulation And Microbubble Pre-Treatment For Improved Microplastic Fibre Removal From Water, Nimesha Thathsarani, Mohadeseh Najafi, Mehdi Khiadani, Muhammad Rizwan Azhar, Masoumeh Zargar

Research outputs 2022 to 2026

Microplastic fibres (MPFs) dominate treatment plant influents but are difficult to remove due to their morphology, composition and low density, limiting the effectiveness of coagulation flocculation pre-treatment. Integrating coagulation-flocculation with microbubble introduction offers a sustainable route to improve MPF removal, yet conventional inorganic coagulants often require high dosages, show strong pH dependence, and pose environmental risks. Chitosan is a biodegradable and non-toxic green alternative, but its application is limited due to its low solubility and pH sensitivity. In this study, two amphoteric derivatives of Chitosan have been successfully developed: CMC-CTA, produced by modifying carboxymethyl Chitosan (CMC) with 3-chloro-2-hydroxypropyl trimethyl ammonium …

Thermoeconomic Optimization Of Climate-Adaptive Solar And Wind Multi-Generation Systems Using Artificial Intelligence And Thermal Energy Recovery, Ehsanolah Assareh, Nima Izadyar, Emad Tandis, Mehdi Khiadani, Amir Shahavand, Neha Agarwal, Arian Gerami, Ahmed Rezk, Minkyu Kim, Reza Kord, Tahereh Pirhoushyaran, Mehdi Hosseinzadeh, Saleh Mobayen

Research outputs 2022 to 2026

This study presents a hybrid multi-generation energy system designed to overcome solar intermittency while meeting the global demand for integrated delivery of electricity, water, cooling, and sustainable fuels in the transition to decarbonization. The engineering application integrates solar thermal and wind energy with a modified Brayton cycle, a Steam Rankine Cycle (SRC), and a Thermoelectric Generator (TEG) to simultaneously produce electricity, fresh water via Reverse Osmosis (RO), hydrogen and oxygen via Proton Exchange Membrane Electrolyzer (PEME), and cooling (via absorption chiller) within a unified optimization framework. The system was modeled using Engineering Equation Solver (EES) and optimized via Response Surface …

Control Techniques And Design Of Load-Side Controls For The Mitigation Of Late-Time High-Altitude Electromagnetic Pulse, Connor A. Lehman, Rush Robinett, Wayne Weaver, David G. Wilson

Michigan Tech Publications

This paper introduces a novel control archetype designed to mitigate high-altitude electromagnetic pulse (HEMP) (Formula presented.) disturbances on the power grid, as well as information on performance and specifications of different control laws for the controller archetype. This method of protection has been overlooked in the literature until now. A controlled voltage supply is placed on the load-side of a transformer, diverting unwanted power from the transformer core to prevent saturation. The controlled voltage source is modeled using four control laws: an integral controller (capacitor), Linear Quadratic Regulator (LQR), an energy storage minimized feedforward control law, and a Hamiltonian feedback …

Amp: Single-Shot Ultra-Wide Fisheye-To-Cubemap Pnp Pose Estimation, Ryan M. Raettig, Richard R. Nyquist, Scott L. Nykl, Clark N. Taylor, Christine M. Schubert Kabban

Faculty Publications

Estimating the position and orientation of a rigid object from an image is critical for situational awareness in robotics and autonomous systems. This study explores relative pose estimation using an ultra-wide fisheye camera for unmanned aircraft inspection vehicles. Ultra-wide fisheye lenses introduce radial distortion and capture features beyond the rectilinear image plane, rendering rectilinear Perspective-n-Point (PnP) algorithms inadequate. Designing a bespoke ultra-wide fisheye localization algorithm requires consideration of both the feature detection method and the pose estimator itself. This study proposes a novel method that combines (1) a fisheye-to-cubemap reprojection, (2) a You Only Look Once (YOLO) convolutional neural network …