Operations Research, Systems Engineering and Industrial Engineering Commons^™

Fetal Acidosis Prediction Using Attention Enhanced Convolutional Neural Networks, Anusha Adhikari

Masters Theses

This study explores the integration of spectral mixtures of fetal heart rate (FHR) and uterine contraction (UC) signals to enhance the prediction of fetal acidosis, utilizing the CTU-CHB dataset. Several classification models were trained using two distinct oversampling techniques and inputs, demonstrating that models incorporating spectral mixtures significantly outperform those using raw signals. These models, particularly when combined with convolutional neural networks (CNNs) and attention mechanisms, achieved a notable F1-score of 0.98, with the highest model achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.95. The research employs a variety of techniques including short-time Fourier transform and …

Leveraging Synthetic Data For Efficient Training Of Ai Models For Real-World Object Detection, Reinaldo A. Moraga

Graduate Research Theses & Dissertations

Modern computer vision (CV) systems largely depend on real-world data for training, which is costly in terms of time, materials, and resources. As industries push toward automation and Artificial Intelligence (AI) -driven solutions, the need for enabling more efficient model training is growing. The primary aim of this work is to explore a framework tailored for industrial applications that uses synthetic images generated from 3D models to train a CV model capable of real-world object detection. This approach seeks to reduce the time, cost, and resources typically required for training AI models with real-world data. This work presents a method …

Order Acceptance And Detailed Scheduling In A Make-To-Order Job Shop With Discrete And Batch-Processing Machines, Dheeban Kumar Srinivasan Sampathi

Graduate Research Theses & Dissertations

In today's ever-evolving production landscape, characterized by a growing demand for personalized products to enhance consumer satisfaction, the strategy of pursuing high-mix, low-volume manufacturing has gained importance. More than ever, manufacturers are adopting the Make-To-Order (MTO) approach, aiming to balance efficient cost management by meeting strict customer deadlines. This research explores the complex state of job shop scheduling, a significant challenge faced by manufacturing entities trying to optimize production time and costs while making the best use of their machinery and resources. The primary concern is the dynamic relationship between order acceptance and scheduling within a job shop environment, which …

Suas Agricultural Aerial Application Operational Field Test, David Thirtyacre, Joseph Cerreta, Scott S. Burgess

International Journal of Aviation, Aeronautics, and Aerospace

One of the most promising uses of aerial applications by a sUAS is on small farms where traditional crewed aerial applicators were not practical due to the limited size of the operations. This controlled field test aimed to assess the feasibility and cost-effectiveness of using a sUAS spreading system compared to the traditional manual application of weed killer and fertilizer on cranberry bogs in Western Washington that were less than 10 acres. The aerial application took place over two separate days as scheduled by the farmer for maximum product efficiency. A total of sixty-three flights were necessary to apply the …

An Operational Field Study: A Comparison Of Piloting Uncrewed Underwater Vehicles And Uncrewed Aircraft Systems, David Thirtyacre, Joseph Cerreta, Pete Miller, Kimberly Luthi, Jolee Thirtyacre

Publications

and operations, the ability to cross-train personnel in both Uncrewed Underwater Vehicles and Small Uncrewed Aircraft System operations has become a focal point for efficiency and workforce optimization. This study presents a comparative analysis of the operational and human factor considerations involved in piloting mini UUV and sUASs, highlighting the key similarities and differences in control methods, environmental influences, navigation, emergency procedures, and situational awareness. A qualitative experimental field study was conducted between July 2024 and October 2024, involving real-world deployments of both systems in maritime and aerial environments. Findings indicated that while UUV and sUAS operators relied on remote …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

Enhancing The Mechanical Properties Of Sla 3d-Printed Bio-Resin From Linseed Oil Using Cellulose And Lignin Biopolymers, Chukwunonso A. Ikedionu

College of Graduate Studies: Theses & Dissertations

Abstract

This research explores the development of bio resin from linseed oil by the process of epoxidation and acrylation to produce acrylated epoxidized linseed oil (AELO). Lignin and cellulose biopolymers were added as reinforcement at varying weight fractions of 1 wt.%, 2.5 wt.%, and 5 wt.%, and their mechanical performance was compared against pure AELO. The formulations were printed using an Elegoo SLA 3D printer to create standardized test specimens like tensile bars, compression cylinders, and hardness blocks, which were all prepared according to the ASTM testing standard.

The results indicate that reinforcement effects depend on both filler type and …

How Do Human And Ai Gender Bias Interact In Hiring Decisions?, Eyuel Getahun, Daniel Burton Shank, Casey I. Canfield, Jessica L. Cundiff, Jenny L. Davis, Celia Freed

Psychological Science Faculty Research & Creative Works

The hiring process is crucial for organizational success but has long been troubled by human biases. Many organizations now include AI in their hiring protocols to mitigate these biases and increase efficiency. However, AI itself can have biases baked-in. Human biases and AI biases are distinct but related; here, we examine how human and AI biases interact to affect hiring outcomes. Through an online experiment, we examine this question in the context of gendered hiring for a male-dominated leadership position in electrical engineering. The study tests how elevated and depressed AI recommendations for male and female job candidates affect participant …

Ease Of Product Disassembly Through A Systematic Structured Time-Based Design For Disassembly Methodology, Emeka S. Igwe

College of Graduate Studies: Theses & Dissertations

This research introduces a systematic time-based design for disassembly (DfD) framework aimed at optimizing product disassembly by addressing important features like liaisons between components in product, component accessibility and the overall modularity of the product. This study specifically covers electromechanical and mechatronic systems in both household and industrial setup, identifying their disassembly challenges and high value pointers for improvement. The methodology involves using a design for disassembly framework called LeanDfD in carrying out a holistic disassembly process and evaluating quantitative metrics like disassembly time and complexity and suggesting further redesign strategies to minimize disassembly time and cost. Adopting this systematic …

Insider Threat Agent: A Behavioral Based Zero Trust Access Control Using Machine Learning Agent, Michael Fojude

College of Graduate Studies: Theses & Dissertations

Hybrid work, cloud adoption, and freely available AI‑enabled attack tools have exposed critical weaknesses in perimeter‑centric security. Current breach reports attribute more than one‑third of incidents to insider misuse or credential compromise, yet many organizations still depend on static Role‑ or Attribute‑Based Access Control that neither verifies intent continuously nor adapts to subtle behavioral change. This research addresses that gap by designing and validating a behavioral based Zero Trust Access Control (ZTAC) Agent. A five‑year enterprise log Dataset was extracted and cleansed to establish a high‑fidelity baseline of normal user behavior. Feature engineering captured temporal regularity (login sequence, session duration), …

Out Of Order And Causally Correct: Ready-Event Discovery Through Data-Dependence Analysis, Erik John Jensen, James Leathrum Jr., Christopher Lynch, Katherine Smith, Ross Gore

Electrical & Computer Engineering Faculty Publications

Data-dependence analysis can identify causally-unordered events in a pending event set. The execution of these events is independent from all other scheduled events, making them ready for execution. These events can be executed out of order or in parallel. This approach may find and utilize more parallelism than spatial-decomposition parallelization methods, which are limited by the number of subdomains and by synchronization methods. This work provides formal definitions that use data-dependence analysis to find causally-unordered events and uses these definitions to measure parallelism in several discrete-event simulation models. A variant of the event-graph formalism is proposed, which assists with identifying …

The Effect Of Need For Cognition & Need For Affect On Human Reliance And Artificial Intelligence Interactions, Aliyah Mcgowan

Doctoral Dissertations and Master's Theses

Abstract

With the increased use of Artificial Intelligence (AI) automations in fields like medical diagnoses and mental health queries, there are concerns regarding an individual’s trust and reliance on the technology. Reliance on AI output may lead an individual to accept inaccurate or incorrect information without further analysis. Trust may influence reliance and trust formation may be a product of affective processing. This study investigated the relationship between Need for Affect (NFA), Need for Cognition (NFC), and trust and reliance on AI interactions. Participants were assessed on the NFA scale for willingness to approach or avoid emotional stimuli, the NFC …

Input Independent Observers For Bilinear Systems Using Convex Optimization, M. Aminul Haq, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

An asymptotic state variable observer is proposed for a continuous-time bilinear dynamical system with the distinguishing feature that the error dynamics are globally asymptotically stable and independent of the applied input. Only the speed of convergence of the error dynamics may be input dependent. The approach is to apply classical Lyapunov stability theory using a convex optimization algorithm and linear matrix inequality (LMI) tools to in effect isolate the stability property of the error dynamics from the input. The LMIs are used to turn the nonconvex problem into a convex problem. The method is demonstrated on an induction motor drive.

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

Cognitive Fatigue Detection Using Photoplethysmography (Ppg) And Reaction Time Data, Anhar Sami Mohammed, Prajoy Podder, Maciej Jan Zawodniok, Cihan Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a framework for real-time cognitive fatigue detection among shift workers using an integrated approach that combines photoplethysmography (PPG) data and reaction time analysis with advanced deep learning models, including Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FNNs). The system leverages heart rate variability (HRV) and reaction time data to identify fatigue indicators. The results demonstrate significant performance, with the first FNN model achieving a test accuracy of 98.94% and a loss of 0.2928, while the second FNN model achieved the same accuracy with a slightly higher loss of 0.3089. The LSTM model, designed for sequential …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Bi-Objective Dynamic Tugboat Scheduling With Speed Optimization Under Stochastic And Time-Varying Service Demands, Xiaoyang Wei, Hoong Chuin Lau, Zhe Xiao, Xiuju Fu, Xiaocai Zhang, Zheng Qin

Research Collection School Of Computing and Information Systems

With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage …

Application Of Artificial Intelligence Techniques To Improve Leadership Decision Making With Uncertainty, Michael David Parrish

Doctoral Dissertations

"Every good leader is a good manager, but not every good manager is a good leader. The difference between the leader and the manager is critical decision-making. Today’s decision-making environment is characterized as Volatile, Uncertain, Complex, and Ambiguous (VUCA). With the exponential increase in the technical capabilities of systems, the human has become the weakest link in the use of such systems. To remain relevant, good leaders must continuously adapt to new advances in technology and processes.

The research contributions of this work provide several unique and novel solutions for leaders to utilize artificial intelligence tools to improve and optimize …

A Comprehensive Analysis Of Climate Resilience Strategies For Small Island Developing States, Ashley-Ann Davis

Doctoral Dissertations

Small Island Developing States (SIDS) face unique and disproportionate challenges in their efforts to achieve climate resilience due to their geographic vulnerabilities, limited resources, and systemic economic and social constraints. This dissertation provides a comprehensive analysis of climate resilience strategies tailored to SIDS, addressing critical systemic issues and exploring interdisciplinary solutions. It synthesizes findings across four interconnected studies, including an analysis of disaster preparedness and response mechanisms in developing countries to identify gaps and opportunities for improving resilience in extreme events. The research investigates economic factors influencing energy portfolio transitions in the Caribbean, emphasizing the complexities of renewable energy adoption. …

Deep Learning Architecture Design For Nano-Satellite Image Super-Resolution, William Everette Symolon

Doctoral Dissertations

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. NanoSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation, and research. They also have the potential to enhance satellite constellation resilience. However, the inherent size, weight, and power limitations of NanoSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution, which limits the utility of CubeSat images for military planning purposes and national intelligence applications. This research proposed a deep learning architecture capable of enhancing …

Improving The Availability Of Aluminium Sheath Extrusion Machines In The Cable Industry, Jirathana Vasinyont

Chulalongkorn University Theses and Dissertations (Chula ETD)

Aluminium sheath extrusion machines (AL Seamless) play a critical role in the production of high-voltage cables, ensuring the integrity and quality of electrical transmission infrastructure. However, after commissioning such machines at PD Company was fraught with challenges, including frequent breakdowns, limited machine availability, and production inefficiencies. The underlying issues stemmed from inadequate standard operating procedures (SOPs), insufficient knowledge transfer during commissioning, and suboptimal maintenance practices. To address these challenges, this research applied structured methodologies, including Failure Mode and Effects Analysis (FMEA) to identify critical failure points and root causes, and Total Productive Maintenance (TPM) to improve operational reliability. Knowledge transfer …

Risk Management For Irrigation Water Pumping Project In Myin Gyan District, Myanmar, Ye Lin Aung

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study evaluates risk and performance management in the Si Mee Khon irrigation water pumping project in Myin Gyan District, Myanmar. Although intended to supply irrigation to 5,000 acres of farmland, the project has fallen short of planned targets. By Q3 2025, 53.62% of the budget had been spent while only 44% of the intended coverage had been completed, signalling significant delays and cost inefficiencies. The research identifies and prioritises key risks, assesses their effects on schedule and cost performance, and proposes mitigation strategies. A mixed-method approach was used, combining interviews with twenty MOALI officials and quantitative analysis based on …

Reliability Of An Extended Version Of The 3m™ Eargage Tool To Assess Earcanal Size And Assist Earplug Selection, Bastien Poissenot-Arrigoni, Laurence Martin, Alessia Negrini, Djamal Berbiche, Olivier Doutres, Franck Sgard

Études primaires

Objective: Evaluate the ability of an extended version of the 3 MTM Eargage to estimate the earcanal size and assess the likelihood that a particular earplug can fit an individual’s earcanal, ultimately serving as a tool for selecting earplugs in the field. Design: Earcanal morphology, assessed through earcanal earmolds scans, is compared to earcanal size assessed with the extended eargage (EE) via box plots and Pearson linear correlations coefficients. Relations between attenuation measured on participants (for 6 different earplugs) and their earcanal size assessed with the EE are established via comparison tests. Study sample: 121 participants exposed to occupational noise …

การวิเคราะห์ความสามารถในการขับขี่และพฤติกรรมจากความเหนื่อยล้าโดยใช้สัญญาณภาพ, ภาสวิชญ์ บุญนุช

Chulalongkorn University Theses and Dissertations (Chula ETD)

อาการเหนื่อยล้ายังคงเป็นประเด็นด้านความปลอดภัยที่สำคัญในกลุ่มผู้ขับรถบรรทุก ซึ่งมักจะส่งผลให้ระดับความสามารถในการขับขี่ลดลงและเพิ่มความเสี่ยงต่อการเกิดอุบัติเหตุ การศึกษาในครั้งนี้นำเสนอแนวทางการใช้แบบจำลองการเรียนรู้ของเครื่อง (Machine Learning) โดยมีการรวมข้อมูลเหตุการณ์การเกิดพฤติกรรมของผู้ขับขี่ที่ตรวจจับได้จากระบบ AI Camera และข้อมูลด้านประชากรศาสตร์และลักษณะการทำงาน เพื่อทำนายระดับความสามารถในการขับขี่ที่ลดลงที่มีสาเหตุมาจากอาการเหนื่อยล้า โดยทำการศึกษาจากข้อมูลจำนวน 600 วัน ซึ่งประกอบไปด้วยผู้ขับขี่จำนวน 105 คน โดยแบ่งระดับความสามารถในการขับขี่ออกเป็น 3 ระดับ ได้แก่ ความสามารถปกติ ความสามารถลดลง และความสามารถที่มีความเสี่ยงสูง โดยสังเกตจากพฤติกรรมการตอบสนองขณะขับขี่ การวิเคราะห์จะพิจารณาการรวมข้อมูลด้านประชากรศาสตร์และลักษณะการทำงาน วิธีการพัฒนาเกณฑ์ในการระบุระดับความสามารถในการขับขี่ กรอบเวลาในการสังเกตอาการเหนื่อยล้า และคุณภาพของข้อมูลเชิงพฤติกรรม โดยใช้แบบจำลองการเรียนรู้ของเครื่องภายใต้กรอบเวลาในการสังเกตที่แตกต่างกันก่อนเกิดที่ระดับความสามารถในการขับขี่ลดลง ผลการศึกษาแสดงให้เห็นว่าปัจจัยด้านประชากรศาสตร์และปัจจัยที่แสดงลักษณะการทำงานช่วยเพิ่มความแม่นยำในการทำนาย โดยสำหรับการศึกษานี้ แบบจำลองชนิด LightGBM ให้ผลการทำนายที่ดีที่สุดที่ค่าความแม่นยำ 90% ภายใต้เงื่อนไขเหตุการณ์ที่ผ่านการตรวจสอบและใช้กรอบเวลา 8 นาที ซึ่งสะท้อนให้เห็นถึงประโยชน์ของการใช้กรอบเวลาสั้นในการตรวจจับอาการเหนื่อยล้า การวิเคราะห์ความสำคัญของแต่ละปัจจัย (Feature Importance) พบว่า การหาว การปฏิบัติงานต่อเนื่องเกิน 4 ชม. และการหลับตา เป็นตัวบ่งชี้ทางพฤติกรรมที่สำคัญที่สุดตามลำดับ ในขณะที่อายุ การยกของก่อนขับรถ ช่วงเวลาในการทำงาน ประสบการณ์ในการทำงาน เป็นปัจจัยด้านประชากรศาสตร์และการทำงานที่มีอิทธิพลต่อการลดลงของระดับความสามารถในการขับขี่ ผลการศึกษานี้ชี้ให้เห็นถึงความสำคัญของการรวมข้อมูลเหตุการณ์ที่ผ่านการตรวจสอบเข้ากับลักษณะเฉพาะของผู้ขับขี่ และกรอบเวลาในการสังเกตอาการเหนื่อยล้าที่เหมาะสม เพื่อใช้ในการประเมินระดับความสามารถที่ลดลงที่มีสาเหตุจากอาการเหนื่อยล้าได้อย่างมีประสิทธิภาพในงานขนส่ง

การเปรียบเทียบขนาดของรอยฝ่าเท้าที่ได้จากเครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพาและโฟมพิมพ์เท้า, วรัญญา รัตนสุมาวงศ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ความแม่นยำของเครื่องมือใหม่ที่ถูกพัฒนามาใช้ทดแทนเครื่องมือเดิม เป็นปัจจัยสำคัญต่อความน่าเชื่อถือ ได้มีการพัฒนาเครื่องพิมพ์รอยฝ่าเท้า 3 มิติแบบพกพาเพื่อทดแทนการใช้โฟมพิมพ์เท้า และใช้ iPhone 12 mini เป็นอุปกรณ์สแกนรอยฝ่าเท้าเพื่อทดแทนเครื่อง 3D scanner เพื่อประเมินให้เห็นว่าสามารถนำเครื่องพิมพ์รอยฝ่าเท้า 3 มิติแบบพกพาและ iPhone 12 mini มาใช้ทดแทนได้ จึงได้มีการออกแบบการทดลองแบบ Randomized Complete Block Design (RCBD) โดยศึกษาปัจจัยของประเภทเครื่องมือที่ใช้พิมพ์รอยฝ่าเท้า ได้แก่ โฟมพิมพ์เท้า และ เครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพา และประเภทเครื่องสแกน 3 มิติ ได้แก่ 3D scanner และ iPhone 12 mini การทดสอบได้เก็บข้อมูลเท้า 18 ข้าง จากผู้เข้าร่วมวิจัย 9 คน โดยพิมพ์ลงบนเครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพาและโฟมพิมพ์เท้า และทำซ้ำ 3 ครั้ง รอยฝ่าเท้าที่ได้ถูกสร้างรูปร่างรอยฝ่าเท้าดิจิตอลด้วย iPhone 12 mini และ 3D scanner เพื่อเก็บข้อมูลตัวชี้วัดทั้ง 7 ตัว ได้แก่ ความกว้างรอยฝ่าเท้า ความยาวรอยฝ่าเท้า ความสูงรอยฝ่าเท้าในพื้นที่บริเวณอุ้งเท้า 4 ตำแหน่ง และความสูง Metatarsal head ที่ 3 จากผลการวิเคราะห์ทางสถิติแบบ Randomized Complete Block Design (RCBD) ที่ระดับความเชื่อมั่น 95% พบว่าตัวชี้วัดทั้ง 7 ตัว ของเครื่องพิมพ์รอยฝ่าเท้า 3 มิติ แบบพกพากับโฟมพิมพ์เท้าไม่มีความแตกต่างกันอย่างมีนัยสำคัญ และการใช้ iPhone 12 กับ 3D scanner ไม่มีความแตกต่างกันอย่างมีนัยสำคัญเช่นกัน ผลการวิจัยสรุปได้ว่า …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …