Engineering Commons^™

Using Predictive Analytics To Reduce Small Business Cost Estimation Error, Diana Solt

Journal of Applied Packaging Research

Small and medium packaging companies generally employ the use of custom-developed quoting programs to bid goods and services. Custom bid programs (e.g. Excel) are used to capture the company-specific costs of production. The inputs of variable costs, such as machine rate and scrap rate, are critical to get correct; however, companies often rely on educated guesses and industry expertise to quote packaging products to end-users. Due to the guesswork involved there can be a financial difference between the quoted costs and actual costs. This variance is often the cause of significant lost dollars. Price, if not determined correctly, could negatively …

Bronconest, Jake Esperson, Andrew Collins, Anish Katragadda

Computer Science and Engineering Senior Theses

Selecting suitable housing is a critical yet often overwhelming aspect of the college experience, particularly for incoming students who lack access to transparent and meaningful information about residential life. Existing platforms, such as official university housing portals or external real estate websites, fail to capture the nuanced, day-to-day experiences that influence student well-being, including cleanliness, social atmosphere, and sense of community. This issue is further compounded by the growing prevalence of remote decision-making and scattered, unverified sources of housing feedback.

To address this challenge, we developed BroncoNest, a scalable, cross-platform mobile application designed to centralize and personalize the student housing …

A Causal-Comparative Study Between The Aeronautical Decision-Making Skills Of Collegiately And Non-Collegiately Trained Private Pilots, Karl P. Winters

Doctoral Dissertations and Projects

The purpose of this quantitative causal-comparative design study was to examine the relationship between private pilots’ flight training background (collegiate or non-collegiate) and aeronautical decision-making (ADM) skills among recently certificated private pilots enrolled in a commercial pilot flight training course at a Part 141 flight training program in a large, private, mid-Atlantic university. The problem is that although deficient ADM is a known major contributor to fatal general aviation (GA) accidents, it is unknown if there is a difference in the ADM skills of collegiately and non-collegiately trained private pilots. This study, built on the framework of dual-process theory and …

The Electrostatic Charge On Exuded Liquid Drops, Schuyler Arn, Pablo Illing, Joshua Mendéz Harper, Justin C. Burton

Electrical and Computer Engineering Faculty Publications and Presentations

Fluid triboelectrification, also known as flow electrification, remains an under-explored yet ubiquitous phenomenon with potential applications from material science to planetary evolution. Building upon previous efforts to position water within the triboelectric series, we investigate the charge on individual, millimetric water drops falling through air. Our experiments measured the charge and mass of each drop using a Faraday cup mounted on a mass balance, and connected to an electrometer. For pure water in a glass syringe with a grounded metal tip, we find the charge per drop (Δ/Δ) was approximately -5 pC g to -1 pC g. This was independent …

Design And Optimization Of Low-Loss, High-Gain Metamaterial-Based Log-Periodic Dipole Array Antenna For Full Ka-Band Coverage, Mohamed El Moniar, Ahmed Abd El Hady, Yasser Ismail, Nihal Areed

Turkish Journal of Electrical Engineering and Computer Sciences

This work describes a microstrip log-periodic dipole array (MLPDA) antenna that uses metamaterials and operates across the whole Ka-band. The suggested MLPDA antenna layout provides a wide bandwidth with fewer dipole elements than traditional MLPDA antennas while maintaining the same resonance frequencies. To reduce size while covering a wide operational spectrum, the antenna design includes bending dipoles as radiating elements, as well as an incomplete ground plane. Furthermore, the proposed MLPDA antenna’s energy loss has been reduced while boosting its signal strength (gain) by inserting a metamaterial-based structure in front of it at a certain distance and on the same …

Design And Performance Evaluation Of Pv-Powered Ev Charging Stations With Grid Integration And Comparative Analysis Of Dc-Dc Converters For Offboard Chargers, Mohammad Abidur Rahman

Theses and Dissertations

Grid-integrated photovoltaic (PV)-powered electric vehicle (EV) charging stations offer a sustainable solution for reducing grid dependency and fossil fuel consumption in workplace environments. This study proposes a PV-powered EV charging system with an AC bus configuration and grid support to ensure stable and efficient operation. A comparative analysis of four DC-DC converter topologies—Dual Active Bridge (DAB), LLC Resonant, Interleaved Buck-Boost, and Interleaved Buck—is conducted for offboard charging applications. Performance evaluation is carried out using MATLAB/Simulink, considering voltage and current ripple, power density, stress levels, bidirectional capability, and control complexity. Component-level reliability is assessed using MIL-HDBK-217 standards to estimate failure rates …

A Joint Geometric Topological Analysis Network (Jgta-Net) For Detecting And Segmenting Intracranial Aneurysms, Xinyue Zhang, Zonghan Lyu, Yang Wang, Bo Peng, Jingfeng Jiang

Michigan Tech Publications

Objective: The rupture of intracranial aneurysms leads to subarachnoid hemorrhage. Detecting intracranial aneurysms before rupture and stratifying their risk is critical in guiding preventive measures. Point-based aneurysm segmentation provides a plausible pathway for automatic aneurysm detection. However, challenges in existing segmentation methods motivate the proposed work. Methods: We propose a dual-branch network model (JGTANet) for accurately detecting aneurysms. JGTA-Net employs a hierarchical geometric feature learning framework to extract local contextual geometric information from the point cloud representing intracranial vessels. Building on this, we integrated a topological analysis module that leverages persistent homology to capture complex structural details of 3D objects, …

Emerging Investigator Series: Are We Undervaluing Septage? Rethinking Septage Management For Nutrient Recovery And Environmental Protection, Kevin Orner, Stetson Rowles, Sara F. Heger, Ben Howard

Civil Engineering & Construction: Faculty Publications

An estimated 20–25% percent of households in the US rely on on-site sanitation via septic tanks to manage their wastewater. Septage management strategies such as land application, treatment at wastewater treatment plants, and treatment at independent septage treatment plants are common regulated and protective processes for managing septage. There can, however, be potentially negative environmental impacts such as groundwater contamination if septic systems are failing or improperly designed. In this perspective, we reimagine septage management at each step of the septage value chain, identify barriers to change, and propose solutions to overcome these existing barriers. Reimagined septage management can take …

Statistical Approach To Turbulent Dispersal Of Aerosols For Accurate Prediction Of Concentration And Associated Uncertainties, K. A. Krishnaprasad, ‪Nadim Zgheib, S. Balachandar

Mechanical Engineering Faculty Publications

In the context of turbulent dispersal of aerosol pollutants from a source within a ventilated indoor space, the present work addresses the importance of going beyond accurate prediction of the mean ensemble-averaged exposure, by evaluating the expected level of variability in individual realizations. This uncertainty quantification requires a statistical description of the inherent stochastic turbulent dispersal process and the inhomogeneous nature of the indoor flow. We leverage large datasets from turbulence-resolving EulerLagrange simulations of aerosol dispersal in varying indoor geometries. The datasets provide time-resolved concentrations of pollutants emitted by a source located anywhere in a room and reaching a sink …

Skin Cancer Diagnosis Utilizing Hybrid Discrete Cosine Transform And High-Performance Convolutional Neural Networks, Mohammed M. Abo-Zahhad, Mohammed Abo-Zahhad

Mansoura Engineering Journal

Detecting skin cancer early and accurately is crucial for successfully treating this potentially fatal disease. Enhancing the accuracy of visual inspection techniques is often necessary to improve clinical decision-making and increase the chance of successful treatment outcomes. In this research, high-performance deep learning (DL) models for automated skin cancer categorization and early skin cancer diagnosis screening are developed and evaluated in conjunction with the discrete cosine transform (DCT). For this purpose, features are extracted from medical images using both techniques. The DCT is used for feature extraction and dimensionality reduction, while deep learning (DL) trains fully connected models for classification …

Engineering The Immune Response To Biomaterials, Abolfazl Salehi Moghaddam, Mehran Bahrami, Einollah Sarikhani, Rumeysa Tutar, Yavuz Nuri Ertas, Faleh Tamimi, Ali Hedayatnia, Clotilde Jugie, Houman Savoji, Asma Talib Qureshi, Muhammad Rizwan, Chima V. Maduka, Nureddin Ashammakhi

Michigan Tech Publications

Biomaterials are increasingly used as implants in the body, but they often elicit tissue reactions due to the immune system recognizing them as foreign bodies. These reactions typically involve the activation of innate immunity and the initiation of an inflammatory response, which can persist as chronic inflammation, causing implant failure. To reduce these risks, various strategies have been developed to modify the material composition, surface characteristics, or mechanical properties of biomaterials. Moreover, bioactive materials have emerged as a new class of biomaterials that can induce desirable tissue responses and form a strong bond between the implant and the host tissue. …

A Data-Driven Approach To Smart Shopping: Optimizing Grocery Trips Using Geolocation And Store Inventory Data, Kien T. Giang

Honors Theses

This project presents a data-driven web-based application, Smart Shopping, designed to help customers optimize their grocery purchases based on location and store inventory information. The application allows users to add items to a shopping list and either enter an address or use their browser’s location services to identify nearby stores. It then retrieves product availability and prices from a mock database representing stores at user’s selected locations. The program compares prices across stores to provide users with two optimized options: the cheapest shopping bill from a single store, and the lowest individual item prices across multiple stores. Additionally, the …

Probabilistic Cash Flow Analysis Considering Risk Impacts By Integrating 5d-Building Information Modeling And Bayesian Belief Network, Mohammad Hosein Madihi, Mohammadsoroush Tafazzoli, Ali Akbar Shirzadi Javid, Farnad Nasirzadeh

Civil Engineering & Construction: Faculty Publications

Unrealistic cash flow forecasts negatively affect project stakeholders and are a common issue for construction practitioners. This study proposes a new method for predicting the probabilistic cash flow of a project that can automate the calculation process while considering the impact of risks and their inter-related structure. This research integrates a Bayesian Belief Network (BBN) and 5D-BIM to provide a new probabilistic cash flow analysis approach. Here, 5D-BIM is used to facilitate cash flow calculations and automate the process. The BBN has also been implemented to assess the impact of risk factors on project cash flow, considering their complex inter-related …

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …

Causation Analysis Of Crane-Related Accident Reports By Utilizing Chatgpt And Complex Networks, Yifan Wang, Junyu Chen, Bo Xiao, Shane T. Mueller, Jingjing Guo

Michigan Tech Publications

This study integrates ChatGPT and complex network (CN) techniques into an accident analysis framework designed to reduce manual effort in accident causation analysis. The proposed framework supports construction stakeholders in extracting causal factors (CFs) from accident reports and identifying both critical CFs and key causal paths. A multistep research design was adopted to develop and validate this novel framework for analyzing crane-related construction accident reports using ChatGPT and CN techniques. First, ChatGPT was prompted to extract CFs from a database of crane-related accident reports. Second, evaluation metrics and an expert questionnaire survey were developed to assess ChatGPT’s performance in CF …

New York City Misses The Exit To Traffic Safety, Joseph Caffrey

Capstones

New York City Misses the Exit to Traffic Safety investigates New York City’s mounting traffic violence crisis through the lens of a devastating crash that killed a Brooklyn mother and her two daughters. It examines the city’s inconsistent enforcement of reckless driving and the failure of the Dangerous Vehicle Abatement Program (DVAP), which aimed to reform recidivist speeders. The piece investigates the imperfections of Vision Zero, public backlash to automated enforcement, and the broader failure to prevent recidivist speeding. It also explores policy alternatives like Intelligent Speed Assistance (ISA), highlighting legislative efforts to revive accountability and save lives, while advocating …

Predicting Seizure Onset Zones From Interictal Intracranial Eeg Using Functional Connectivity And Machine Learning, Jared Pilet, Scott A. Beardsley, Chad Carlson, Christopher T. Anderson, Candida Ustine, Sean Lew, Wade Mueller, Manoj Raghavan

Biomedical Engineering Faculty Research and Publications

Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant focal epilepsy. However, it remains unclear whether FC-based metrics provide additional value beyond established epilepsy biomarkers such as epileptic spikes and high-frequency oscillations (HFOs). Using interictal iEEG data from 26 patients, we estimated FC across eight frequency bands (4–290 Hz) using amplitude envelope correlation (AEC) and phase locking value (PLV). From the resulting FC-matrices, we estimated two graph metrics each to derive 32 FC-based features. We also extracted features related to spikes, HFOs, and power spectral densities (PSD). A trained support …

Leveraging Reduced Order Models And High-Fidelity Simulations For Efficient Multifidelity Uncertainty Quantification Of Thermal Systems, Jakob G. Bates

Theses and Dissertations

Simulation-led design is becoming an important part of thermal systems design. Simulations of thermal systems continue to improve in their fidelity, but this comes with an increased computational cost. Additionally, uncertainty in simulation inputs, such as thermophysical properties, leads to uncertainty in simulation outputs. For simulations to be used rigorously for simulation-led design, this uncertainty must be quantified. Performing uncertainty quantification on thermal simulations is made difficult by their many uncertain parameters and high computational cost. Multifidelity uncertainty quantification is a set of methods that reduce the cost of uncertainty quantification by leveraging high-fidelity, high-cost simulations with low-fidelity, low-cost simulations. …

Experimental Characterisation Of Laser Cladding Of Crniw And Crnifealzr Powders On H13 Tool Steel And Optimisation Of Process Parameters, Martin Vinoth S

Theses and Dissertations

In this work, laser cladding on H13 steel substrate with two different powder compositions CrNiW and CrNiFeAlZr has been carried out. The laser power, powder feed rate, and scanning speed were varied and the clad dimensions, aspect ratio, and dilution percentage were measured. The microhardness found in the CrNiW clad is 834 ± 20 HV0.5, which is higher than that of the CrNiFeAlZr clad (780 ± 20 HV0.5) as well as the substrate (548 ± 20 HV0.5).

The X-ray Diffraction (XRD) patterns identified the common phase creation of Ni3C and Fe3C for CrNiFeAlZr and CrNiW coatings, while the oxide formation …

Sharc: Simulator For Hardware Architecture And Real-Time Control, Paul K. Wintz, Yasin Sonmez, Paul Griffioen, Mingsheng Xu, Surim Oh, Heiner Litz, Ricardo G. Sanfelice, Murat Arcak

Faculty Work Comprehensive List

Tight coupling between computation, communication, and control pervades the design and application of cyber-physical systems (CPSs). Due to the complexity of these systems, advanced design procedures that account for these tight interconnections are paramount to ensure the safe and reliable operation of control algorithms under computational constraints. This paper presents the Simulator for Hardware Architecture and Real-time Control (Sharc) to assist in the co-design of control algorithms and the computational hardware on which they are run. Sharc simulates the execution of a user-specified control algorithm on a given processor microarchitecture configuration, evaluating how computational constraints affect the dynamical properties of …

The Influence Of Extrusion Geometry And Ratio On Extrudate Mechanical Properties For A 6005a Alloy Containing Either Sc And Zr Or Cr And Mn Dispersoid Formers, Eli A. Harma, Paul Sanders, Thomas Wood, Timothy Langan

Michigan Tech Publications

There is a demand for a 6005A series extrusion alloy with improved strength that maintains good extrudability. Replacing Mn and Cr dispersoid formers with Sc and Zr is expected to increase the room temperature mechanical properties while not affecting extrudability. Al3X dispersoids with a Sc core surrounded by a Zr shell are stable at higher temperatures and enhance recrystallization resistance and precipitation strengthening. However, there is little information on how the Sc and Zr additions affect the properties of an extrudate as a function of extrusion geometry and ratio. A 6005A series alloy with Cr and Mn additions is compared …

Thermaltrack Dataset - Training Labels - Sequence 1-11, Yiming Yang, Jeremy P. Bos

ThermalTrack

We present a wheel track detection system that leverages RGB-Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow - tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable.

Research On Grey-Box Modeling Method Of Digital Twins For Cantilever Structure, Wenjia Zhang, Heming Zhang

Journal of System Simulation

Abstract: The construction of accurate and highly real-time digital twin models in complex industrial setting presents several challenges. Traditional model construction approaches based only on mechanism or data show certain limitations. Therefore, this study is based on the idea of grey-box modeling, taking the cantilever structure within a boom-type roadheader as the object, and proposes a novel modeling approach that combines the characteristics of the mechanism model and introduces a self-attention mechanism. This method performs grayscale transformation on the original input and splices it with physical features to achieve organic fusion of mechanism information, which not only enhances the expressiveness …

An Extended Image Features Based Uncalibrated Visual Servoing Method, Shuzhen Zhang, Yukun Cheng, Yangbo Liu, Fusheng Zha

Journal of System Simulation

Abstract: Aiming at the traditional uncalibrated visual servo relying on the estimation of image Jacobi matrix and the coupling of the motion of each degree of freedom of the camera, on the basis of imagebased uncalibrated visual servo, an extended image features based uncalibrated visual servo method is proposed. By analyzing the relationship between image features and camera frames change in the visual servoing process, the visual servoing process in the image space is decomposed into four basic processes: translation, stretching, rotation and scaling; by analyzing the changing of image features in the visual servoing process, extended image features are …

A Modeling And Simulation Method For Firepower Intelligent Decision-Making Of Directed Energy System Basedon Joint Dqn, Changhong Qu, Junjie Wang, Kun Wang, Qingyong Cui, Jiangyang Chen, Xinpeng Wang

Journal of System Simulation

Abstract: In order to solve the problem of dynamically addressing firepower intelligent decision-making in anti-UAV cluster combat using a directed energy system, a deep reinforcement learning model is established. Based on the high multi-agent state and action space dimensions of this model, a modeling and simulation method of firepower intelligent decision-making of directed energy system based on joint deep Q network (DQN) is proposed. The state space is constructed from the state of directed energy system, UAV cluster and the directed energy system deployment area. The joint mechanism is used to share the state information of each equipment and the …

Research On Modeling, Optimization And Application Of Aeroengine Oil System, Shijie Huang, Zhensheng Zhang, Jing Cai, Rui Zhang

Journal of System Simulation

Abstract: In response to the high cost and long cycle of using experimental methods for monitoring, diagnosing, and predicting lubricating oil system, a simulation model for oil system is constructed and optimized, and the application of the model in health management of oil system is proposed. Based on the physical characteristics of the components in the oil system, subsystem models for ventilation, oil supply, thermodynamics, and oil return are constructed using a certain engine oil system as an example, and the whole oil system model is constructed and solved iteratively. The model is optimized by combining particle swarm optimization and …

Experimentally Driven Numerical Model Of Carbon/Polyaniline-Based Glucose Monitoring Sensors: An Evaluation Using A New Figure Of Merit, Kyrillos Selim, Ziad Khalifa, Amira Ali, Sameh O. Abdellatif

Electrical Engineering

This study presents an experimentally driven numerical model for evaluating carbon/polyaniline (PANI)-based glucose monitoring sensors (GMSs), focusing on innovative configurations using graphene-PANI and carbon nanotube (CNT)-PANI composites. We performed a thorough analysis of the morphological, electrophysical, and electrical properties of these materials, ultimately leading to the extraction of key electrical parameters for integration into a finite element model (FEM). This model simulates the entire sensor, enabling the estimation of critical performance metrics such as sensitivity, limit of detection (LOD), linearity, and power consumption. Our findings demonstrate that the CNT-PANI configuration significantly outperforms the laser-induced graphene (LIG)-PANI electrode, achieving a figure …

Adaptive Multi-Scale Feature Pyramid Network For Occlusion Pedestrian Detection, Huaping Zhou, Tao Wu, Kelei Sun

Journal of System Simulation

Abstract: To address the issue of current pedestrian detectors, which struggle to extract complete features in occlusion-heavy environments and consequently have low detection accuracy. A novel adaptive multiscale feature pyramid network is proposed. A multi-scale feature enhancement module (MFEM) is developed. It captures the visible area of pedestrians at different scales through a multi-branch network with different receptive fields. An AFM (adaptive fusion module) is proposed. It calculates the importance of different pixels by optimizing the mean variance at the spatial and feature levels. It enhances the texture and semantic features of pedestrians and fuses the features of different scales …

Design And Realization Of Integrated Energy System Dynamic Stability Simulation And Steady-State Simulation System, Guixiong He, Xiaoqiang Jia, Shufeng Dong, Yonglu Han, Yonghua Chen, Yiming Zheng

Journal of System Simulation

Abstract: Aiming for“carbon peak”and“carbon neutrality”, the energy sector is undergoing significant reform. To address energy flow and planning optimization in integrated energy systems, a comprehensive simulation platform is developed. This platform combines physical and digital simulations with real-world validation and is modular in design, It includes an integrated energy model library, energy flow optimization, modeling management, real-time simulation, and energy monitoring. The platform enhances system safety, stability, and economic efficiency, While also improving planning and energy management. The paper analyzes the platform′s functional and physical architecture, introduces key modules, establishes dynamic and steady-state model libraries, and optimizes energy flow using …

Research On Modeling Methods For Industrial Core Capability Architecture Based On The Dodaf Framework, Xiaoqiang Dou, Yan Liu, Zhilong Zhao, Chao Fu, Fulin Zhang, Shanshan Zou

Journal of System Simulation

Abstract: Against the backdrop of the industrial sector actively pursuing digital capability building, this paper describes the necessity and current status of architecture theory methods guiding industrial core capability construction. It proposes the conceptual connotation of industrial core capability architecture and four key modeling elements. Based on DoDAF, it conducts the overall design of industrial core capability architecture. By integrating systems engineering principles, it establishes a five-stage process model for capability-building activities, embedding critical elements such as capability/business/ application/data/technology architecture viewpoint, and explains data model design, and logical compositions of various viewpoints. By selecting a capability building project in a …