Digital Commons Network^™

Dodaf-Opm-Sd Cross-Layer Automated Mapping Method Based On A Unified Semantic Bridge, Lei Cheng, Gang Xiao, Binbin Wang, Siming Peng, Haozhe Liang, Xiangwu Gong

Journal of System Simulation

Abstract: To address the problems of the inability of DoDAF views to directly drive simulations and the insufficient cross-layer semantic alignment and consistency verification, a DoDAF-OPM-SD cross-layer semantic automated/semi-automated mapping and verification method was proposed in this paper. Targeting tactical/operational-level problems dominated by "conservation+feedback+time delay", the proposed method reduced manual mapping under expert adjudication based on a "minimal executable view set". The object-process methodology served as a semantic bridge to map architectural elements into the stock-flow-feedback structures of system dynamics; semantic embedding disambiguation, integer programming harmonization, and K-nearest neighbor parameter completion were integrated; end-to-end traceability was connected through traceability identifiers, …

Design And Implementation Of Hdrt Real-Time Simulation System, Huiji Zheng, Guangsen Wang, Qing Liu, Kang Wang, Zhiwei Wang, Zhenyu Zhang, Shuo Wang, Zhu Liu

Journal of System Simulation

Abstract: In view of the real-time simulation requirements of large-scale complex systems such as power electronics, a hidden dragon real-time(HDRT) simulation system was developed. The strict time constraints of simulation tasks were guaranteed based on a resource-dedicated real-time scheme, supporting fixed-step and multi-rate simulations from the second level to the hundred-nanosecond level. A hybrid CPU-field programmable gate array(FPGA) architecture was adopted to accelerate computation. The system could utilize multiple simulators for parallel simulation and realized microsecond-level real-time data interaction between simulators through a dedicated PCIe switch. A single simulator could be expanded through I/O interface equipment, supporting a maximum input …

Numerical Simulation Of Water Tank Solidification In A Firefighting Aircraft Under High-Altitude Cold-Soak Conditions, Guanmian Liu, Zhihang Cheng, Hejun Qin, Kangzhi Yang, Qing Wen, Kun Gao

Journal of System Simulation

Abstract: A systematic numerical simulation study was conducted to address the issue of internal water tank solidification in firefighting aircraft under high-altitude low-temperature conditions. Based on computational fluid dynamics methods, a solidification-melting model considering fluid-structure interaction heat transfer and phase change processes was adopted. Through reasonable simplification of the complex geometric model, a quasi-three-dimensional computational model suitable for engineering analysis was developed. The influence laws of key parameters, including high-altitude cold-soak temperature, ground initial water temperature, and cold-soak time, on the freezing characteristics of the water tank were investigated. Combining with the parameter influence laws, a safety criterion using the …

Combat Effectiveness Evaluation Of Anti-Ship Missiles For Intelligent Autonomous Recognition, Long Zhang, Xuanming Feng, Zhen Lei, Bo Yang, Ying Wang

Journal of System Simulation

Abstract: To address the core issues of poor adaptability of static fusion strategies in existing recognition models, as well as the simplistic evaluation system and its disconnection from dynamic confrontation requirements, a practical four-dimensional evaluation system encompassing "recognition accuracy, antijamming stability, decision timeliness, and modal complementarity" was constructed, and an operational effectiveness composite index (OECI) capable of dynamically adapting to tactical scenarios was proposed. A multimodal dynamic attention fusion network (MDA-Net) for anti-ship missiles in complex confrontation environments was designed. Through heterogeneous feature decoupling, dynamic weighting of cross-modal attention, and a hierarchical gating decision mechanism, the autonomous evaluation and adaptive …

Infrared Image Generation Method Based On Improved Cyclegan, Qiqi Jin, Xiang Zhang, Li Gao, Lin Zhang, Junliang Yan, Peiyao Li

Journal of System Simulation

Abstract: To address the problems in current infrared image generation such as insufficient contrast between target and scene, excessively large discrepancies from real scenes, indistinct thermal source features, and great difficulty in constructing measured infrared image datasets, an improved CycleGAN-based infrared image generation method was proposed. By optimizing the network structure of the generator and adding a non-local module and a CBAM convolutional attention mechanism into the generator, the extraction capability of CycleGAN for infrared features was enhanced, enabling the network to capture the subtle features of targets more accurately; a perceptual loss function was introduced to improve the detail …

Counterfactual Explanations For Time Series Classification: From Localized Perturbations To Realistic Generation, Peiyu Li

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine learning models are often used to classify signals collected over time, such as heart rhythms, movement recordings, industrial sensor measurements, and scientific observations. These models can be accurate, but they are often difficult to understand. Users may need to know not only what a model predicted, but also what would have needed to change for the model to reach a different decision.

This dissertation studies counterfactual explanations for time series data. A counterfactual explanation answers a “what-if” question. For example, if a model classifies a signal as one activity instead of another, the explanation shows how the signal would …

Lessons From The Club Homeschool Capstone: Testing, Data Discipline, And The Computer Science Curriculum, Shane Brown

University Honors Theses

This thesis looks at the CLUB Homeschool Capstone project to argue that Portland State University's Computer Science curriculum should introduce testing and data quality discipline earlier and more intentionally than it does now. As team lead of a seven-person team, I coordinated sprint planning, communicated with the sponsor, and developed custom Discourse plugins that enhanced an existing forum platform instead of creating a separate application database, as requested by the sponsor. The project's requirements document called for a formal testing plan, but our team lacked the practical experience to implement one. This gap became evident through my internships as a …

Llm-Based Early Rumor Detection With Imitation Agent, Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao, Shih-Fen Cheng, Jing Ma, Cheng Niu

Research Collection School Of Computing and Information Systems

Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts. This is especially challenging in data-scarce settings. While Large Language Models (LLMs) perform well in few-shot NLP tasks, they are not well-suited for time-series data and are computationally expensive for both training and inference. In this work, we propose a novel EARD framework that combines an autonomous agent and an LLM-based detection model, where the agent acts as a reliable decision-maker for \textit{early time point determination}, while the LLM serves as a powerful \textit{rumor …

A Deep Reinforcement Learning Framework For Dynamic Order And Rack Scheduling In Robotic Mobile Fulfillment Systems, Lingjun Wang, Bayi Cheng, Zhiguang Cao, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Robotic mobile fulfillment systems play a vital role in modern e-commerce, but their efficiencydepends on overcoming a critical challenge: coordinating orders and racks effectively under dynamicorder arrivals, diverse delivery deadlines, and resource sharing among multiple stations. This papertackles the resulting dynamic order and rack scheduling problem and proposes an intelligentscheduling framework centred on deep reinforcement learning. At its core is TriNet-PPO, whichtrains a scheduling policy using TriNet, a tripartite graph neural network encoder tailored to capturethe many-to-many relationships among orders, racks, and items. Furthermore, we introduce IPCSA,an integer programming-based cooperative scheduling algorithm that maps station-level actionpreferences to conflict-free joint decisions …

Optimal Scheduling Of Virtual Power Plants Considering Photothermal Power Stations And Hydrogen Energy Utilization, Yousong Chen, Ruofa Cheng, Yi Liu, Yi Zhang, Zhihao Zuo

Journal of System Simulation

Abstract: To enhance the operational stability and low-carbon performance of virtual power plants (VPPs) with high shares of renewable energy, a coordinated dispatch model integrating concentrated solar power (CSP) plants with power-to-gas (P2G) and carbon capture is developed. An optimal VPP scheduling strategy is proposed, combining a stepped carbon trading mechanism with a compensation coefficient and dynamic hydrogen blending. To address multi-source uncertainties in wind power, CSP power, and loads, an envelope boundary model is used for simulation. Information gap decision theory (IGDT) is applied to provide customized solutions for decision-makers with different risk preferences. A bi-objective optimization model is …

Fault-Tolerant Control Method For All-Type Actuator Faults Of Evtol Aircraft, Juan Wang, Guorui Li, Zhiyong Fan, Huijie Chen

Journal of System Simulation

Abstract: To address all-type actuator faults, especially nonlinear distortion issues, in multi-rotor eVTOL aircraft, a novel fault-tolerant control method was proposed. A fault function was established at the rotor speed level, and a fault-tolerant control algorithm combining dynamic robust nonsingular fast integral terminal sliding mode with a high-order finite-time disturbance observer was designed to achieve fault-tolerant control through rotor redundancy allocation. The nonsingular fast integral terminal sliding mode algorithm was improved, and a dynamic system containing actuator faults and their derivatives was constructed via a dynamic surface to achieve system convergence within finite time, avoiding the singularity and chattering problems …

A Method For Assessing The Contribution Degree Of An Aviation Delivery System And Identifying Key Equipment, Xiaofeng Liu, Chengze Jiang, Xingyu Chen, Deyin Jiang, Bolin Shang, Bifeng Song

Journal of System Simulation

Abstract: Based on the delivery efficiency and delivery quality, a general aviation delivery system effectiveness evaluation model was constructed, and a calculation method of system contribution degree based on efficiency was given. By combining the system calculation experiment and simulation experiment based on agent-based modeling and simulation (ABMS), the design idea of the Monte Carlo simulation experiment for key equipment identification and equipment technology development trend analysis was sorted out, and the key equipment identification method based on ABMS and contribution evaluation was proposed. By taking the intercontinental long-range aviation delivery mission as an example, a variety of simulation experiments …

Optimization Of Node Deployment For Three-Dimensional Heterogeneous Wsn In Elongated Structural Space, Jiguang Yang, Jiuyuan Huo, Fang Cao, Cong Mu

Journal of System Simulation

Abstract: To achieve effective coverage of key monitoring points in an elongated structural space, a heterogeneous wireless sensor network(HWSN) deployment optimization method combining the virtual force algorithm(VFA) and multi-strategy improved whale optimization algorithm(MSIWOA), namely HVF-MSIWOA, was proposed. A dynamic adaptive weight mechanism and a t-distribution perturbation operator with heterogeneous degrees of freedom were designed, enabling the whale optimization algorithm(WOA) to balance global exploration and local exploitation and jump out of local optima; combining the topological characteristics of the elongated space and node density distribution, an adaptive virtual force distance threshold between heterogeneous nodes was constructed; the mapping relationship between network …

Three-Dimensional Gaussian Reconstruction Of Large-Scale Scenes Under Multi-View Geometry Constraints, Haohao Cui, Yanqiang Di, Qing Liu, Xianguo Meng

Journal of System Simulation

Abstract: To enhance the geometry reconstruction quality of the GS algorithm in large-scale scene reconstruction, an optimization method constrained by multi-view geometry reconstruction results was proposed. 2D Gaussian planes were used as geometric primitives to overcome depth anisotropy, and dense depth maps generated by DUSt3R and aligned by sparse point clouds were introduced as constraints. By designing a multi-stage optimization strategy that decouples geometry and rendering, the gradient conflict problem in multi-objective training was solved. Experiments on the MatrixCity dataset indicate that the method surpasses comparison methods in related indicators of geometry reconstruction quality and rendering quality in large-scale scenes. …

Parameter Dependent Chen-Fliess Series And Their Nonrecursive Interconnections, Natalie T. Pham

Electrical & Computer Engineering Theses & Dissertations

In control theory, a Chen-Fliess functional series is a weighted sum of iterated integrals constructed from a given set of input functions. Such series can be used to represent nonlinear input-output systems. In applications, they have been employed to characterize interconnected nonlinear systems, to solve system inversion and tracking problems, and to design predictive and adaptive controllers.

Distributed parameter systems exhibit spatial dependence along with temporal dependence. Such systems are typically represented in terms of partial differential equations. In control theory, there appears to be no existing method for representing the input-output map of a distributed system via a Chen-Fliess …

Predicting Student Belonging In Computing Education: A Multimodal Machine Learning Approach Using Eeg And Survey Data, Hannah Moshtaghi

Master's Theses

Measuring students’ sense of belonging, characterized by feelings of acceptance, inclusion, and encouragement from teachers, remains a significant challenge in computing education. Prior research has associated this multidimensional construct with positive academic outcomes and has identified instructors’ growth- and fixed-mindset messaging as a potential influence. However, belonging is a complex and deeply personal experience that is difficult to capture through direct observation alone. Current measurement methods rely on self-report surveys, which may not capture every aspect of an experience that can also involve emotional and cognitive responses.

This thesis investigates whether combining EEG data recorded during a belonging questionnaire with …

Painting A Scene: 3d Painterly Rendering From Curves To Rebelle, William M. Luttrell

All Theses

Stylized 3D rendering has seen much development and success over the past few years. From Spider-Man: Across the Spider-Verse to The Bad Guys, many studios have developed tools to incorporate stylistic elements from graphic novels, comic books, watercolor paintings, and more into their productions. This stylization process incorporates the pacing, visual style, and themes from the source medium into the animated work, allowing a much greater freedom of expression for artists and directors.

Inspired by these films as well as the needs of the short film Kate Shelley and the Bridge of Darkness currently in production, This paper presents …

Theory-Informed Generative Agents For Human Behavioral Modeling In Disasters, Liming Lu

All Dissertations

This dissertation develops a theory-informed generative-agent framework for modeling human behavioral decisions in disasters. Existing flood and disaster preparedness models often emphasize physical hazards, infrastructure exposure, or statistical correlations, but they struggle to capture the heterogeneous and evolving choices households make. This limitation is especially important for climate-related hazards, where future damage depends not only on changes in rainfall, inundation, and urban development, but also on decentralized protective actions such as house elevation, flood insurance, evacuation, and early preparedness. The dissertation integrates two empirical studies: a flood-risk study in Charleston, South Carolina, and a household disaster-preparedness study across hurricane contexts. …

Leveraging Deep Learning Recurrence And Attention Mechanisms For Flood Forecasting And Assessment, Elnaz Heidari

All Dissertations

Predicting how much water will flow in rivers and streams is important for managing floods, water supply, and the environment. Traditionally, government agencies have used complex models, such as the National Water Model (NWM), which simulate how much water moves through landscapes using physical laws and real-world data. However, recent advances in Artificial Intelligence (AI) have enabled new ways to make these predictions. This research explored whether AI-based models could predict river discharge more accurately. These AI models learn patterns from past data instead of relying only on physical rules. To find out how well they work, the AI models …

Technique-Level Normalization For Cybersecurity Intelligence: An Empirical Evaluation Of Att&Ck Attribution From Hids Alerts Using Fine-Tuned Transformers And Metadata Re-Ranking, Emad Sherif

International Journal of Cybersecurity Intelligence & Cybercrime

Cybercrime investigations increasingly depend on the ability to interpret large volumes of automated security events. For organizations without dedicated security operations centres, a situation common among small and medium enterprises, the manual translation of raw alerts into structured threat intelligence represents a critical bottleneck that slows investigative triage and limits cross-case comparability. This paper evaluates an automated enrichment pipeline designed to address this bottleneck by mapping security events to standardised adversary behaviour labels drawn from the MITRE ATT&CK framework, supporting both operational response and cybercrime investigation workflows. We compare three pipeline configurations, a general-purpose encoder model, a cybersecurity domain-adapted variant, …

A Patch-Level Framework For Urban Vegetation Water Demand Estimation Using Remote Sensing And Deep Learning, Jesus Daniel Pereyra Manriquez

Open Access Theses & Dissertations

Urban water management in semi-arid regions requires an improved understanding of how vegetation and climatic conditions influence landscape water demand. Existing approaches often lack an integrated, spatially consistent framework to quantify this relationship at fine scales. This study proposes a patch-level framework to estimate relative landscape water demand by integrating vegetation coverage, vegetation condition, and atmospheric demand. Vegetation coverage is derived from high-resolution imagery obtained from the National Agriculture Imagery Program (NAIP) using a U-Net segmentation model with a MobileNetV2 backbone. A patch-based representation is used to ensure spatial consistency across the study area. Seasonal vegetation dynamics are captured using …

Domain Adaptation Of Facial Age Estimation For Law Enforcement Mugshot Repositories, Jorge Alejandro Pacheco Roque

Open Access Theses & Dissertations

Facial age estimation supports law enforcement via image-based, age-filtered queries, age-progressive re-identification, and bulk record labeling, where prediction accuracy determines if the resulting decisions can be trusted. State-of-the-art models excel on web imagery but incur higher error on mugshots due to domain shift between the professionally lit, filtered, and posed web photographs used during pre-training and the uniform backgrounds, uncooperative expressions, and decades of evolving capture technology found in mugshot collections. We address this gap by adapting SwinFace - a state-of-the-art multi-task Swin Transformer with public code and pretrained weights, trained on color face imagery for face recognition, facial expression …

Herd: A Policy-Driven Elastic Resource Distribution Framework For Hpc Deep Learning, Alejandro Guerrero Rodriguez

Open Access Theses & Dissertations

Modern deep learning workloads increasingly rely on distributed computation, and High Performance Computing systems can provide the necessary resources through large GPU allocations across interconnected nodes. Despite this, most distributed training frameworks operate under static resource assignments once a job is deployed. Research on NERSC Perlmutter has shown that 50% of GPU-enabled jobs use 25% or less of available GPU memory, and elastic training can reduce this underutilization by dynamically adjusting active workers. However, existing elastic systems have been developed mainly for cloud environments where fault tolerance and cost optimization are the primary concerns. Applying elastic training to HPC environments …

Learning To Unlearn: Unlearning And Meta-Unlearning For Continually Adapting Cybersecurity Threat Detectors, Daniel Lucio

Open Access Theses & Dissertations

Machine learning (ML) models deployed in non-stationary environments must continually adapt to evolving data distributions. This challenge is particularly critical in cybersecurity, where malware, intrusion techniques, and adversarial behaviors evolve over time. Continual learning primarily enables incorporating new knowledge while preserving prior knowledge, however, indiscriminately retaining obsolete and harmful information can hinder future adaptation and consume limited model capacity. We argue that effective adaptation should not only acquire new knowledge, but also selectively discard obsolete and less useful historical knowledge before learning from a new distribution. In this work, we propose a meta-learning framework that learns what to forget to …

Towards A Multi-Framework Approach To Explainable Artificial Intelligence, Henry Salgado

Open Access Theses & Dissertations

Artificial Intelligence (AI) and machine learning (ML) models are increasingly being deployed to support decision-making in high-stakes domains such as healthcare, criminal justice, and education, where trust, accountability, and transparency are critical. However, increasing model complexity has made many modern systems insufficiently transparent. Existing approaches to explainable AI (XAI) typically emphasize either intrinsic model simplicity or post-hoc attribution methods that estimate feature importance for predictions. While these approaches provide valuable insights into model behavior, they do not necessarily establish whether the identified importance is grounded in the underlying data patterns or in the structural relationships that generate model behavior. Many …

Integrated Framework For Tsn-Enabled Ot Networks And Scalable Edge Computing To Enable Real-Time Feedback Loop, Taposh Kumer Sarker

Open Access Theses & Dissertations

The advent of Industry 5.0 envisions smart manufacturing characterized by human centricity, sustainability, and systemic resilience. Realizing this vision requires the seamless convergence of Information Technology (IT) and Operational Technology (OT) networks. However, integrating massive, stochastic IT edge computing workloads with deterministic physical control loops introduces severe architectural friction, inherently threatening the safety guarantees required by industrial machinery. To resolve this fundamental incompatibility, this dissertation proposes the Edge-Augmented Real-Time Industrial Control System (EA-RICS).

EA-RICS is a comprehensive, multi-layered architecture designed to dismantle systemic bottlenecks across the physical data plane, the centralized control plane, and the edge operating system. First, the …

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Continuous Authentication For Industrial System Access: Evaluating Bluetooth Low Energy Direction Finding And Channel Sounding For Tailgating And Relay Attacks, Mitchell Mennelle

LSU New Orleans Theses and Dissertations

In physical access control, authentication is often viewed as a one-time event, where, once an authorized user crosses a protected boundary, downstream systems assume the user remains physically present. Tailgating and relay attacks violate this assumption. In this thesis we propose a continuous authentication layer based on two Bluetooth Low Energy spatial signals. Angle of Arrival direction finding follows the trail of a worn credential to determine when an operator exits a work zone. Bluetooth Channel Sounding measures a physical property of the radio path and verifies distance during stationary periods. Limiting Relay Attacks with Event-Driven Distance Verification. A stream …

Towards More Realistic And Practical Graph Backdoor Attacks, Jiawei Chen

Computer Science Theses & Dissertations

Graph Neural Networks (GNNs) have demonstrated remarkable performance on graph-based learning tasks and are increasingly deployed in security-critical applications. However, recent studies have shown that they are highly vulnerable to graph backdoor attacks (GBAs), where adversaries implant malicious triggers to induce targeted misclassification during inference. Despite their effectiveness, existing GBAs are often developed under unrealistic assumptions, such as focusing exclusively on simple homogeneous graphs or assuming the adversary possesses privileged access to target nodes during inference. This dissertation aims to systematically investigate and design graph backdoor attacks under significantly more realistic graph settings and adversarial constraints.

First, we investigate the …

Exploring The Determinants Of User Discontinuance In Ai-Driven Usage-Based Insurance, Wenzhuo Li

Theses and Dissertations in Business Administration

While interest in algorithmic decision-making continues to grow, limited research has examined the post-adoption phase. This study examines how users evaluate their post-adoption experiences with algorithmic decision-making in the context of usage-based insurance (UBI), focusing on how expectation disconfirmation shapes satisfaction and the intention to discontinue use. It explores two key questions: What factors influence users’ discontinuance intention toward AI-based UBI systems? And how do specific algorithmic characteristics alter how users form these post-adoption evaluations? To investigate these questions, this study develops a comprehensive theoretical model that integrates the Expectation Confirmation Model and Reactance Theory, incorporating additional factors such as …