Engineering Commons^™

A Novel Joint Training Simulation Evaluation Framework And Its Key Techniques, Rusheng Ju, Dongdong Chen, Yunxiu Zeng, Jiyuan Liu, Sihang Qiu, Peng Zhou

Journal of System Simulation

Abstract: To address the challenges of traditional evaluation systems, such as internal module coupling, lack of reusability, and poor adaptability to multi-domain evaluation needs, a three-tier decoupled technical evaluation framework of "data + service + application" was designed. A strategy was proposed for extracting high-value information from massive audio and video data based on key events, resolving the problem of unstructured evaluation data processing. A design method combining general and dedicated evaluation model templates was proposed, improving the general applicability of the evaluation model. An expert knowledge-driven comprehensive integrated discussion and evaluation environment was constructed using qualitative and …

Distributed Heterogeneous Hybrid Flow-Shop Scheduling Considering Combined Buffer, Hua Xuan, Lin Lü, Bing Li

Journal of System Simulation

Abstract: In order to reduce cost losses caused by delivery delays, distributed heterogeneous hybrid flowshop scheduling problems under combined buffer conditions of finite buffer and zero-wait were studied. A hybrid estimation of distribution algorithm based on Q-learning was proposed to minimize total weighted earliness and tardiness. For the combined buffer, dynamic decoding was designed based on the average factory allocation strategy and the shortest path method. The initial job group was optimized by reverse learning. Q-learning was embedded in the probabilistic model for intelligent searching and updating based on the group state. Reconstruction of the job group was completed using …

Reliability Targeted Snow Loads And Winter Wind Parameters For Locations Outside Of The Conterminous United States, Brennan L. Bean, Nicholas Brimhall, Bikram Bhusal, Marc Maguire, Maha Moussa

Browse all Datasets

The national building standard ASCE 7 moved to reliability-targeted snow loads (RTSLs) in the 2022 version. This necessitates the development of RTSLs for international locations. This repository contains the data and code needed to produce RTSLs and Winter Wind Parameters for locations outside of the Conterminous United States (OCONUS). It relies on annual maximum snow loads provided in a separate data release (see Brimhall et al. 2025).

Insights On Ai-Supported Uncrewed And Autonomous Systems Education, Brent A. Terwilliger Ph. D, John Faraca

Publications

Artificial Intelligence (AI) related technology is reshaping the educational experience in programs focused on uncrewed and autonomous systems, aviation, robotics, and aerospace, with growing implications for workforce readiness and cross-sector innovation. Early survey data, capturing student, educator, and employer perspectives, reveals that AI-supported tools are notably changing student engagement, communication, and skills development. Initial indications underscores the importance of AI proficiency and technological familiarity in hiring and workforce development, particularly in technical and operational roles. Key areas of focus include the use of AI to strengthen outreach and interactivity; enrich instruction through intelligent simulations; inform curricular improvements using data analytics; …

Intrinsic Defects (Vacancies And Antisites) In Neutron Irradiated Cdsip2 Crystals, Timothy D. Gustafson, Elizabeth M. Scherrer, Nancy C. Giles, Kevin T. Zawilski, Peter G. Schunemann, Jonathan E. Slagle, Kent L. Averett, Larry E. Halliburton

Faculty Publications

Cadmium silicon phosphide (CdSiP₂) is a nonlinear optical material widely used in optical parametric oscillators. Intrinsic defects (vacancies and antisites) are responsible for unwanted broad optical absorption bands in these crystals that degrade the performance of the devices. In the present work, optical absorption and electron paramagnetic resonance (EPR) spectra are acquired (at room temperature and 12 K, respectively) from a neutron-irradiated CdSiP₂ crystal. After the irradiation, the crystal is highly absorbing from the band edge near 600 nm to beyond 1.3 μm because of overlapping defect-related absorption bands. Heating to 550 °C removes nearly all the …

Tableau Part Ii - October 2025, Rubab Shahzad

Day Family Research Lab Workshop Series

Part Two of Introduction to Tableau. Learn to make cool visualizations using Tableau. A hands-on opportunity where we will go over calculated fields, hierarchies, unions, dashboards, and stories.

Prior experience with Tableau is recommended

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

10.13.2025 Ored Connect, Liz Williamson

ORED Newsletter

• ORED Small Grants Program RFP
• Mississippi Impact Grants RFP
• Contest to Promote Lab Practices
• Field Fest
• AAAS Membership for Faculty, Staff, and Students 

Empirical Methods For Support System Design In Deep Serpentinite Excavations: A Critical Evaluation, Anas Driouch, Redouane Oubah, Abdelaziz Lahmili, Latifa Ouadif

Journal of Sustainable Mining

Underground mining excavations in fragile serpentinite rock masses represent a significant challenge today. The complicated features of these rock formations, especially at great depths, make it difficult to classify them using commonly employed methods. This paper focuses on the application of empirical methods for the classification of serpentinite rock masses and the design of support systems in underground mining excavations. Specifically, it concerns an access tunnel excavated entirely in serpentinite at a depth of 460 m. Geomechanical classification systems indicate that the Tunnelling Quality Index (Q-system) and Rock Mass Rating (RMR) evaluate serpentinites as rock masses of exceptionally poor to …

Improved Streamflow Forecasting Through Swe-Augmented Spatio-Temporal Graph Neural Networks, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 …

Day-Night Shifts In Water-Soluble Ions Of Size-Resolved Aerosols Before And After The Covid-19 Lockdown In A Coastal Megacity: Metro Manila, Philippines, Grace Betito, Paola Angela Bañaga, Rachel A. Braun, Maria Obiminda Cambaliza, Melliza Templonuevo Cruz, Alexander B. Macdonald, James Bernard Simpas, Connor Stahl, Armin Sooroshian

SOSE Affiliate: Manila Observatory

The COVID-19 pandemic-driven lockdowns offer a unique opportunity to examine how reductions in anthropogenic emissions impacted atmospheric aerosol composition in urban environments. This study investigates the day-night variability of size-resolved water-soluble ions in ambient particulate matter (PM) collected in Metro Manila before (November 2019–February 2020) and after (November 2020–February 2021) lockdown implementation. Using tandem Micro-Orifice Uniform Deposit Impactors (MOUDIs), aerosol samples were collected during daytime (06:00–18:00) and nighttime (18:00–06:00) periods and analyzed for key ionic species (sulfate, ammonium, nitrate, oxalate, sodium, chloride, calcium, and magnesium) via ion chromatography. Submicrometer water-soluble mass declined post-lockdown, particularly during daytime, reflecting suppressed secondary formation …

Maximum Likelihood Symbol Timing Algorithm Based On Cyclic Prefix For Ofdm Systems, Kwame S. Ibwe

Tanzania Journal of Engineering and Technology (TJET)

In this paper, a blind symbol synchronization algorithm is presented for orthogonal frequency-division multiplexing (OFDM) systems, and a timing function based on the redundancy of the cyclic prefix (CP) is introduced. The existing algorithms rely on the prior knowledge of the channel energy distribution i.e. channel power profile. In practical environment the channel power profile is unknown to the receiver and its statistics are expected to be highly changing. Nevertheless, the use of pilot symbols in channel profile estimation reduces efficiency as data subcarriers are used to carry pilots instead of payload. In this paper a timing function that accounts …

Cnn-Based Hybrid Model For Detecting Blight Diseases In Potato Crops With Advanced Image Processing Techniques, Farian S. Ishengoma

Tanzania Journal of Engineering and Technology (TJET)

Potato production plays a vital role in global agriculture as a major food source for large populations. However, potato crops are highly susceptible to diseases, particularly Early Blight and Late Blight, which result in substantial yield losses. Timely detection and effective control of these diseases are essential for maintaining stable crop output. This study explores the integration of Convolutional Neural Networks (CNNs) and advanced image processing techniques to differentiate between diseased and healthy potato plants accurately. Two datasets comprising original and enhanced images were used to train four CNN models: InceptionV3, Xception, Densenet201, and Resnet152V2. The original images underwent background …

Optimal Location And Sizing Of Facts Devices To Improve Voltage Profiles On A 132 Kv Mwanza-Musoma-Nyamongo Transmission Line, Peter Makolo

Tanzania Journal of Engineering and Technology (TJET)

Recently, utility companies have desired to supply quality, stable and reliable power to customers, and ensure they meet the demand. Flexible AC Transmission Systems (FACTS) dynamic compensator devices such as Static Synchronous Compensator (STATCOM), Static VAr Compensator (SVC) and Unified Power Flow Controller (UPFC) are an impeccable choice, however, cost is one of the limiting factors following these technologies. In addition, using FACTS devices in the system requires a detailed steady state, dynamic and optimisation analysis to effectively meet the purpose and ensure reduced cost. This paper proposes using an optimised FACTS device to improve voltage profile, power transfer, system …

10.06.2025 Ored Connect, Liz Williamson

ORED Newsletter

• Field Fest
• AAAS Membership
• MIG Application Opportunity
• Contest to Promote Safe Lab Practices

Detecting Polar Ring Galaxies Via Deep Learning, Fawad Kirmani, Anathavishnu S. Unnii, Varsha P. Kulkarni, Kyle Lackey, John R. Rose

Faculty Publications

Polar ring galaxies (PRGs) are peculiar galaxies that show a ring of stars, gas, and dust oriented roughly over the poles of the central ‘host’ galaxy (i.e. roughly orthogonal to the disc of the host galaxy). The formation models for these rings involve mergers or tidal interactions of the host galaxy with another galaxy. Although the identified PRGs look different from each other, they all have a ring that is not in the same plane as the disc of the host galaxy. Unlike in galaxies such as our Milky Way, where stars form in spiral arms, the rings exemplify an …

Phosphate Removal And Recovery From Aqueous Solutions Using Iron-Coated Steel Slag, Siavash Ebrahimzadeh, Guanghui Hua, Christopher Schmit

Civil and Environmental Engineering Faculty and Student Publications

Phosphate pollution from human activities significantly contributes to the eutrophication of aquatic ecosystems. Additionally, phosphorus is a finite, irreplaceable resource, making its management and recovery critical. This study explores the use of iron-coated steel slag (ICS) as a cost-effective material for phosphate removal and recovery from water. Ferric chloride was used to coat electric arc furnace slag via oven drying under various conditions. Batch adsorption tests evaluated phosphate adsorption, and the optimum coating conditions were identified as 0.5–1 mm slag, 0.5 M FeCl₃, and coating pH 1. The effect of solution pH and coexisting ions on phosphate removal were also …

Engr 597: Special Projects In Engineering Science - Data Analytics, Ali Behnood Ph.D., P.E., M., Asce

GMAS Course Syllabi

No abstract provided.

Insect-Foundation: A Foundation Model And Large Multimodal Dataset For Vision-Language Insect Understanding, Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation …

Evaluation Of Towed Tem Potential For Rapid Characterization Of Levee Foundations, Kolawole Arowoogun, Katherine Grote, Jeremy Maurer

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Assessing the geologic conditions of levee foundation soils is a challenging task owing to the extensive length of most levees and the heterogeneity of many alluvial deposits. Traditional investigation techniques (such as boring and cone penetrometer testing [CPT]) are expensive, invasive, and provide spatially-limited information. As a result, they are restricted to pre-identified problematic zones in the levee. To overcome these challenges, geophysical instruments capable of better spatial coverage are proposed for rapid geoelectrical characterization of levees. In this study, we presented a field-based application of the towed time-domain electromagnetic method (tTEM) system in characterizing the subsurface geology adjacent to …

Laboratory Investigation Of High-Temperature Preformed Particle Gels For Fluid Control In Granite Cores For Geothermal Applications, K. Caleb Darko, Yanbo Liu, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

To understand the applicability of high-temperature preformed particle gel (HT-PPG) for control of short-circuiting in enhanced geothermal systems (EGSs), core flooding experiments were conducted on fractured granite cores under varying fracture widths, gel particle sizes and swelling ratios. Key parameters such as injection pressure, water breakthrough pressure, and residual resistance factor were measured to evaluate HT-PPG performance. The gel exhibited strong injectability, entering granite fractures at pressure gradients as low as 0.656 MPa/m; HT-PPG yields a superior sealing performance by significantly reducing the permeability; and dehydration occurs during HT-PPG propagation, with a dehydration ratio ranging from 4.71% to 11.36%. This …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Meshless Discrete Velocity Boltzmann Model For Porous Media Flow, Amandine Maidenberg

Doctoral Dissertations and Master's Theses

This dissertation explores the combination of two sophisticated techniques for addressing computational fluid dynamics: the discrete velocity Boltzmann equation (DVBE) and the localized collocation meshless model with upwinding (U-LCMM). The DVBE is a high-level model that describes the foundations of transport phenomena by addressing the microscale motions of particles themselves and the effect of their aggregate behaviors on continuum principles. This equation integrates multiple scales of phenomena; while it can be used for fluid flow at Navier-Stokes scales, it can also resolve fine features that can only be described at the molecular level. This type of model is necessary for …

Unresolved Image Simulation For Space Situational Awareness Applications, Fox Coniglario

Doctoral Dissertations and Master's Theses

The knowledge of what lies in orbit around Earth is at best a guess. Decades of spaceflight, debris buildup, and vehicle collisions have contributed to a large number of objects that are simply not able to be catalogued. Ongoing efforts to catalog debris in orbit have reached limits by conventional measures and as such, research is active in the field of in-orbit space situational awareness. This thesis intends to help fill a hole in the development of such orbital platforms by assisting the development of image processing software pipelines though the simulation of unresolved space imagery. The simulation uses accurate …

Active Measurement Of A Micron-Order Gap Under High-Speed And High-Temperature Conditions, Andrew Becker

Doctoral Dissertations and Master's Theses

The hypersonic regime poses numerous challenges that researchers face in the development of hypersonic flight vehicles. Due to their excellent thermomechanical properties, ultra-high-temperature ceramics (UHTCs) have risen as a promising solution to act as a protective barrier between the harsh environment and surface materials of these flight bodies. The mechanical operation of a portable hypersonic simulation device was developed in-house and tested at Argonne National Laboratories (ANL) to gather in-situ material response of prospective UHTC samples when exposed to a hypersonic regime. An edge detection-based algorithm was developed and used in LabVIEW to monitor the health and operation of the …

Implicit Neural Representation For Image Reconstruction, Canyu Zhang

Theses and Dissertations

Image reconstruction seeks to restore corrupted images and recover visual content that has been lost or degraded. Such degradation may result from low resolution, occlusion, masking, or shadow interference. This problem has become an increasingly significant research topic, as visual information plays a central role in almost every aspect of modern life. Neural network based approaches have recently emerged as highly effective solutions for this task. In particular, convolutional neural networks and transformer based architectures have demonstrated remarkable success in producing visually convincing reconstructions. However, these models remain constrained in several important ways, one of the most critical being that …

Multi-Period Risk-Aware Procurement Optimization Under Covid-19 Disruption, Jonathan Chase, Hoong Chuin Lau, Jinfeng Yang, Lu Liu

Research Collection School Of Computing and Information Systems

Supply chain resilience has been a topic of active research in the operations research and AI communities for several years, but the COVID-19 pandemic threw the frailties of global supply chains into sharp relief. Disruptions and delays caused by fresh outbreaks leading to lockdowns, put severe strain on supply chains in many industries. In this work we develop lockdown-resilient procurement capabilities for a global technology company. First, through analysis of lockdown data from China we develop a logarithmic regression-based lockdown prediction method to complement a supplier risk metric for conventional risks. Second, we develop a multi-period stochastic optimization model that …

Lightweight Population-Based Policy Optimization For Pickup And Delivery Problems, Yizhou Liu, Li Li, Yixin Xu, Tang Liu, Rong Cheng, Die Wu, Jilin Yang, Jingwen Li

Research Collection School Of Computing and Information Systems

In recent years, applying deep models to automatically learn construction heuristics for vehicle routing problems has achieved remarkable advancements. However, they are less effective in searching solutions due to two primary limitations: relying on deterministic probability distributions and overlooking the strategic advantage of prioritizing nearby unvisited nodes during the route construction process, resulting in suboptimal policies In this paper, we propose a novel lightweight population-based policy optimization (LPPO) framework that learns a diverse population of solution strategies through the utilization of innovative perturbation factors, in order to facilitate search exploration. Moreover, we design a localized attention synthesis (LAS) network to …

Exploring Object Status Recognition For Recipe Progress Tracking In Non-Visual Cooking, Franklin Mingzhe Li, Kaitlyn Ng, Bin Zhu, Patrick Carrington

Research Collection School Of Computing and Information Systems

Cooking plays a vital role in everyday independence and well-being, yet remains challenging for people with vision impairments due to limited support for tracking progress and receiving contextual feedback. Object status — the condition or transformation of ingredients and tools — offers a promising but underexplored foundation for context-aware cooking support. In this paper, we present OSCAR (Object Status Context Awareness for Recipes), a technical pipeline that explores the use of object status recognition to enable recipe progress tracking in non-visual cooking. OSCAR integrates recipe parsing, object status extraction, visual alignment with cooking steps, and time-causal modeling to support real-time …