Computer Sciences Commons^™

Method For Dynamic Coalition Formation Of Wargame Agent For Force Cooperation, Changhua Yao, Shanning Bi, Rufei Ma, Xiaohan Yu, Jiaqiang Li, Jinli Chen

Journal of System Simulation

Abstract: Regarding the issue of cooperative task alliance formation and adjustment in multi-agent dynamic confrontation scenarios at the tactical level, this method comprehensively considers factors such as target value, task allocation, and operator characteristics, as well as the benefits and costs of executing different types of tasks. we propose a targeted force coordination adjustment for dynamic task alliance formation based on behavioral constraints. The “MiaoSuan-Wise Winning Instant Strategy Human-Computer Confrontation Platform” of Chinese Academy of Sciences (CAS) is used as an experimental platform to conduct confrontation experiments. The experiment demonstrates that the proposed method improves the dynamic coordination ability of …

Modeling And Simulation Of Intelligent Underwater Acoustic Countermeasure Based On The Matrix Game, Huijin Zhao, Yu Chen

Journal of System Simulation

Abstract: Due to the great threat of torpedo against surface warships, an efficient hydroacoustic countermeasure system must output real-time strategies to accommodate varied antagonizing scenarios. Aiming at the decision-making problem in the anti-torpedo hydroacoustic countermeasure cases, an intelligent adversarial strategy is proposed based on the game theory. By discretizing the strategy space of both sides, a matrix game model is established where the payoff is characterised by the capture probability of attacking torpedo. An improved simplex algorithm is then developed to get the mixedstrategy Nash equilibrium of the game model, which can be used to obtain preferred avoidance strategies to …

Why Commencement Will Be The One Ritual Ai Will Never Replace, Essraa Nawar

Library Articles and Research

"We are entering an era of unimaginable change.

Artificial Intelligence is transforming how we work, learn, create, and think. It’s reshaping higher education—and pushing many to ask: Is college still worth it? Are degrees still necessary?

Those questions are real. And I welcome them.

But I also know this: no machine can replace the moment a family claps through tears when their loved one walks the stage."

Hierarchical Optimal Scheduling Of Integrated Energy System With Electric Vehicles Based On Empc, Miaomiao Ma, Zijuan Long, Zhiwei Ren, Yongqiang Cheng

Journal of System Simulation

Abstract: A hierarchical real-time optimization (HRTO) based on economic model predictive control is designed to address the issues of randomness and uncertainty of renewable energy and demand-side in integrated energy systems (IES) with electric vehicles. The optimization problem of the entire system is divided into three sub-problems: day-ahead rolling optimization, real-time rolling optimization, and tracking control. The day-ahead optimization strategy based on economic model predictive control is constructed to ensure that the operational units can meet users' demands. The optimal steady-state operating points of the entire IES are obtained through the real-time optimization layer. The tracking model predictive controller is …

Optimal Scheduling Of Virtual Power Plant With Coupled Operation Of Ccs-P2g Considering Wind And Photovoltaic Uncertainty, Xurong Jin, Jiang Yin, Guohua Yang, Wei Li, Guobin Wang, Lele Wang, Na Yang, Xuenian Zhou

Journal of System Simulation

Abstract: In order to solve the problem that the uncertainty of wind power and photovoltaic power generation output easily affects the scheduling of virtual power plant, a new optimal scheduling model of virtual power plant is proposed based on information gap decision theory (IGDT) . In order to reduce the carbon emission of the system, carbon capture and storage (CCS) is installed on the combined heat and power units; in order to improve the utilization rate of renewable energy, the power to gas (P2G) device is introduced into the system, and the operation mode of CCS-P2G coupling is proposed; based …

A Quadrotor Trajectory Tracking Control Method Based On Deep Reinforcement Learning, Guohua Wu, Jiaheng Zeng, Dezhi Wang, Long Zheng, Wei Zou

Journal of System Simulation

Abstract: Traditional quadrotor controllers, constrained by fixed model equation structures, encounter challenges in addressing control errors stemming from variations in parameters and environmental disturbances. This paper proposes a deep reinforcement learning solution for the quadrotor trajectory following control problem. We present the PPO-SAG algorithm incorporated into the PPO framework, utilizing adaptive mechanisms and PID expert knowledge to enhance training convergence and stability. Target functions incorporating distance constraint penalties and entropy policies are designed in alignment with the characteristics of the given problem. We also devise innovative disturbance-adaptive structures and trajectory feature selection mechanisms to augment control error information and extract …

Improved Hybrid Optimization Algorithm For Multi-Objective Ipps Problem, Wenbin Gu, Jiexia Qing, Jie Fang, Siqi Liu

Journal of System Simulation

Abstract: For the problem of multi-objective integrated process planning and scheduling (MOIPPS), an improved hybrid optimization algorithm considering global and local optimum is proposed to optimize two objectives about minimum makespan and energy consumption. A multi-objective problem model and solution framework are established by analyzing the difference and connection between process planning and scheduling in integrated system. A hybrid optimization algorithm is proposed for the two-stage integration problem. In the process planning stage, global search algorithm is employed to provide a variety of process schemes for the integrated system and to ensure the global search performance of the integrated algorithm. …

Optimization Of Cargo Location Allocation In Four-Way Shuttle Warehousing System Based On Two-Stage Hybrid Algorithm, Zisong Wu, Daofang Chang, Yuchun Gai

Journal of System Simulation

Abstract: To address issues such as the dense distribution of storage locations and the potential congestion of shuttle vehicles in the four-way shuttle dense storage system, a grid-based approach to the storage location distribution is developed. A location allocation model is then constructed with the goals of ensuring shelf stability, improving warehousing efficiency, and balancing equipment utilization. A twostage hybrid algorithm is designed for the model. In the first stage, the local search strategy of nondominant sequencing genetic algorithm(NSGA-II) is enhanced by incorporating the hill climbing algorithm to address a set of Pareto front sets. In the second stage, the …

Research On Decision-Making Of Autonomous Driving In Highway Environment Based On Knowledge And Large Language Model, Xiang Wang, Guozhen Tan

Journal of System Simulation

Abstract: Aiming at the lack of continuous learning and interpretability of current autonomous driving system, a decision model with cognition, generalization and learning ability is proposed. The model utilizes large language model (LLM) and attention mechanisms to understand and explain driving scenes. the system can accumulate and learn from driving experiences, continuously improving its decisionmaking ability. In a simulation environment, the closed-loop test decision model is applied in high-speed scenarios.The simulation results show that the success rate of the knowledge-driven model is 7% and 4% higher than those of the rule-based and data-driven methods. Additionally, the model exhibits generalization and …

Robot Path Planning Based On Ant Colony Algorithm With Dual Heuristic Information, Xiaohui Zhou, Yanqiang Li, Yong Wang, Decai Zhao, Xiaoyao Yang

Journal of System Simulation

Abstract: The traditional ant colony algorithm is characterized by a slow convergence speed, numerous turning points, and a tendency to fall into local minima. These characteristics make the algorithm less effective for path planning research in mobile robotics. Therefore, this paper proposes an improved ant colony algorithm and applies it to global path planning for robots. The A* algorithm is used to quickly plan a path and increase the initial pheromone of that path, so that the improved algorithm is guided by the global path during the local search, preventing excessive ants from entering dead ends, and reducing the randomness …

A Simulating Framework Construction Method About New Distributed Confrontation, Zhipeng Liu, Yanyang Gu, Baojie Hu, Fangjun He, Zhipeng Fan

Journal of System Simulation

Abstract: The new distributed confrontation in future emphasizes systematic combat and flexible use of power in the resistance environment. To study and analyze the potential threats of such new forms of distributed confrontation, this paper proposes a system confrontation strategy focused on decisionmaking confrontation from the perspective of network and electromagnetic space. In addition, a highly flexible and saleable behavior model adversarial framework is proposed. By parameterizing the network and electrical countermeasure strategy, an adversarial model based on typical airspace scenarios is designed, and the confrontation effectiveness of traditional multifunctional platforms and distributed units is quantitatively compared and analyzed. The …

Research On A Credibility Information Management Method Of Simulation Modeling, Qiming Gu, Jiazhi Chen, Guoyan Cao, Chenchu Zhou, Dengxiu Yu, Haifeng Hu

Journal of System Simulation

Abstract: A proposed method for managing credibility information in simulation modeling draws insights from the NASA-STD-7009 standard for modeling and simulation. From the perspective of credibility information recording, definition, and assessment, this method aims to address the core issue of whether the credibility requirements are met in simulation modeling research. A simulation system credibility information extraction tool is designed to extract three types of information including modeling credibility information, data credibility information, and development process credibility information, enabling recording, definition, and traceability of credibility information in simulation models. Taking a servo system credibility evaluation as an example, a simulation system …

Towards Multi-Modal Multi-Document Understanding Capabilities In Foundation Models, Chuhan Li

Computer Science Theses

Contemporary foundation models are predominantly evaluated on isolated documentor image-understanding tasks, thereby overlooking the inherent multimodal multi-document reasoning that characterizes scientific inquiry. To bridge this gap, M3SCIQA is introduced, aMulti-Modal,Multi-document Scientific Question Answering benchmark crafted to test foundation models in practical scientific research settings. A comprehensive evaluation of 18 leading foundation models shows a substantial performance gap between models and human experts. Detailed error analysis reveals persistent deficiencies in both scientific visual reasoning tasks and long-range retrieval. Addressing the former, SPACECUE offers a concise yet effective visual prompting that overlays grid coordinates and Semantic-SAM masks …

Towards Practical And Real-Time Decoding Of Quantum Hypergraph Codes, Binghong (Leo) Li

Computer Science Theses

Quantum error correction (QEC) enables scalable quantum computation by detecting and correcting physical errors. However, decoding remains a key bottleneck—particularly for quantum low-density parity-check (qLDPC) codes, whose hypergraph structures demand complex reasoning. Most existing decoders are either too slow for real-time use or lack formal guarantees, and often struggle to generalize across diverse quantum hardware.

This thesis introduces the Minimum-Weight Parity Factor (MWPF) algorithm, a unified and certifiable decoding formulation that extends minimum-weight perfect matching to general hypergraph-based stabilizer codes. We focus on making MWPF practical and performant through a two-phase decoding architecture, consisting of a fast search phase and …

B-Spline Representations For Hyperspectral Inverse Rendering, Rachel Liang

Computer Science Theses

This work explores the use of a B-spline-based approach for hyperspectral inverse rendering from RGB images, experimenting on both spectral and geometric reconstruction. While the B-spline method is less accurate than brute-force optimization, it offers significant improvements in computational efficiency- reducing both runtime and memory usage.

Our experiments show that the B-spline representation can approximate smooth spectral data effectively but struggles with sharper spectral features unless more knots are introduced. Notably, wavelengths near the edges of the visible spectrum (around 400 nm and 700 nm) were less stable during optimization, reflecting lower convergence reliability. Despite these challenges, the final RGB …

Programming A More Efficient Onboarding Process For New Employees, Long H. Pham

Undergraduate Honors Theses

The current onboarding process for new hires in the University of San Diego’s Shiley-Marcos School of Engineering is inefficient. There is no central location where new hires and administrators can track onboarding progress. Both parties have to manage multiple email chains and write their own reminders to keep track of everything. This leads to delays, missing deadlines, and confusion for both parties. A web-based onboarding application has been developed recently to address these issues and streamline the onboarding process for new hires. However, this application contains several accessibility issues and does not follow all of the standards for effective employee …

Simulating 3d Humanoid Ragdoll Physics Using Velocity Verlet Integration, Pin Constraints, And Rigid Body Collision Systems, Son D. Nguyen

Programming Theses and Dissertations

Ragdoll physics simulates realistic character collapse with physical realism by responding to environmental forces rather than using predefined animations.

Data Encoding, Compilation, And Algorithms For Quantum Machine Learning, Aviraj Sinha

Computer Science and Engineering Theses and Dissertations

Quantum computing enables new approaches to data processing, especially in quantum machine learning. Unlike classical systems, quantum data must be synthesized through operations and can exist in superposition. Encoding choices affect efficiency, noise resilience, and trainability—key factors in quantum machine learning models. This dissertation enhances quantum data encodings by extending quantum read-only memory (QROM) beyond binary representations, improving efficiency and parallelism. It introduces new compilation methods for quantum random number generators (QRNGs), supporting non-parametric distributions for post-quantum cryptography. Additionally, it explores Cayley graph-based encodings to extract spectral features for quantum machine learning.

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

Noise-Embedded Image Processing Based On Quantum Data Encodings, Yayu Mo

Multidisciplinary Studies Theses and Dissertations

Advancements in quantum information have significantly impacted the field of image processing, although challenges remain. Especially in the edge detection and image encoding area, distorted feature and noises would affect the further classification or super resolution tasks. In our work, we conduct researches on two stages to both evaluate the potential of Quantum-based Convolutional Structure in extracting distorted feature and further explore the effects of quantum noise channels on quantum image encodings.

In the first stage, we propose a method to extract distorted edge features by applying shallow layers in quantum convolutional neural networks (QCNN). By combining the advantages of …

A Computational Method For Detecting Compound Promiscuity In Early-Stage Pharmaceutical Discovery, John Allen Ringer

Computer Science ETDs

Modern drug discovery and chemical biology research relies heavily on analyzing bioassay data. One of the many challenges in bioassay data analysis is identifying false trails, i.e., chemical compounds which initially appear to have desirable activity but are found to be problematic upon further investigation. Badapple (the BioAssay-Data Associative Promiscuity Pattern Learning Engine) was created over ten years ago to help researchers identify promiscuous compounds and thus avoid a common source of these false trails. Through an effort involving software engineering, cheminformatics, and biomedical data science we have developed Badapple 2.0, which incorporates updated assay records and expanded data semantics. …

Aerial Robotic Studies Of Volcanic Co2 Emissions, John Ericksen

Computer Science ETDs

Volcanic systems are inherently complex, involving dynamic interactions among magma flow, gas emissions, and atmospheric dispersion. This dissertation focuses on developing and analyzing autonomous UAS algorithms for efficiently surveying volcanic CO2 plumes, introducing several novel methods: the LoCUS algorithm, a swarm coordination and self-healing algorithm that supports gradient-based plume tracking, a transect-based technique that employs a 2D Gaussian fit to calculate CO2 plume flux, and the Sketch algorithm for rapid plume boundary tracing. By treating multiple UAS as a single scientific instrument, these methods leverage swarm algorithms to use in-situ data in ways impossible with individual drones. Validated through simulations …

First Annual Advances In Business Education Conference 2025 Proceedings, Kelsey Metz, Joshua Ray

Advances in Business Education (ABE) Conference Proceedings

Conference Overview: The First Annual Advances in Business Education (ABE) Conference was held on May 16, 2025, at Lincoln Memorial University in Harrogate, Tennessee. Hosted by the LMU School of Business, the ABE Conference was established to promote teaching excellence through innovation and collaboration in business education. With a focus on fostering meaningful dialogue among educators, researchers, and students, the conference welcomed participants from across disciplines and institutions. The event was structured around three key tracks:

Pedagogy & Teaching Excellence: Showcasing innovative teaching methods and strategies for enhancing student learning and engagement.

Business Research: Presenting research focused on advancing knowledge …

Cyber Security Threat Recognition And Preparedness Of Undergraduate Students, Litany Hope Lineberry

Theses and Dissertations

Cybersecurity awareness and preparedness are critical competencies for individuals across academic disciplines and professional sectors. However, undergraduate students often lack sufficient knowledge and skills to recognize and mitigate cybersecurity threats. This dissertation examines cybersecurity threat recognition and preparedness among undergraduate students through a three-phase research approach. Study 1 explores faculty perspectives on students' cybersecurity awareness, identifying gaps in knowledge and preparedness across various fields of study. Study 2 investigates industry professionals' perceptions of new hires’ cybersecurity readiness, assessing the alignment between academic training and industry expectations. Study 3 evaluates the effectiveness of an online intervention designed to enhance students' cybersecurity …

Ai Enabled Autonomic, Safe, And Interactive Intrusion Response System, Damodar Panigrahi

Theses and Dissertations

The exponential rise in internet usage has precipitated a corresponding surge in cyber threats, underscoring the urgent need for advanced cybersecurity solutions. While traditional intrusion detection systems (IDS) can identify these threats, their inability to self-recover leaves systems vulnerable. Intrusion response systems (IRS) have been developed to address this, aiming to auto- matically restore systems to their desired state post-security breach. However, current IRSs often necessitate manual intervention and may not be su!ciently robust against sophisticated threats. To overcome these limitations, we propose an AI-powered Autonomic, Safe, and Interactive Intrusion Response System called ‘Intrusion Response System Digital Assistant (IRSDA)’. IRSDA …

Synthetic Data Augmentation For Retinoblastoma Using Diffusion, Andrew Thompson

Theses and Dissertations

Many AI models rely on large and high quality datasets for optimal training. In certain cases, data can be difficult or expensive to obtain, making training difficult. Rare medical conditions are one of these cases. Datasets for retinoblastoma are severely lacking in quantity. Diffusion has been used to create synthetic data in the industrial, medical, and financial domains. By applying the latest Diffusion methods to retinoblastoma, this work seeks to improve predictive model performance on identifying retinoblastoma.

Zeroizing Trust In A Naïve Federated Zero Trust Environment, Keith E. Strandell

Theses and Dissertations

The position of the United States on the global stage is predicated on information dominance and the ability to project power through cooperative engagements with mission partners in both wartime and peacetime. Recent cyber-attacks highlighted the need for a more robust cybersecurity posture. As the United States progresses toward the adoption of Zero Trust, it is incumbent on the Department of Defense to assess the impact to the ability to share data across strategic partnerships while securing the data of both the United States and its partners. This paper proposes research into ensuring an environment rooted in Zero Trust and …

Promoting Collaboration And Multi-Directional Reliance By Sharing Mental Model Information For Effective Multi-Agent Teaming, Audrey L. Aldridge

Theses and Dissertations

Successful human-agent teaming requires teammates to form and maintain a shared or common understanding of several attributes regarding taskwork and teamwork. With enhanced information sharing, mental model development, and team functionality, teammates (human, autonomous) can learn to anticipate each others' behaviors, preferences, and needs as well as understand their capabilities and limitations. In designing a framework to support this type of cooperative teaming, there is a need to determine how sharing knowledge, mental models, and common understandings impacts teaming dynamics and performance. By incorporating each individual's understanding into a human-agent interface, this research enables better team coordination and performance through …

A Framework For Modular Knowledge Composition In Network Intrusion Detection Systems, Patrick L. Day

Theses and Dissertations

Autonomic Intrusion Detection Systems (AIDS) are sophisticated software systems designed to autonomously and adaptively identify and respond to security threats and intrusions in computer networks or systems. One of the fundamental challenges in intrusion detection research lies in the limited availability and scope of publicly available datasets. The proposed research aims to address data-related gaps with autonomic and traditional intrusion detection systems by describing a comprehensive approach to investigate the impact and potential of data augmentation. The goal is to explore various data augmentation techniques, assess their effectiveness in introducing variability, and evaluate their impact on the performance of neural-based …

Optimizing Mars Terrain Segmentation With Weakly Supervised Learning: A Focus On Weighted Loss From Annotation Metadata, Malika Dutta

Theses and Dissertations

The study of planetary surfaces heavily depends upon space rovers that gather detailed images of terrain needed for analysis and navigation. Deep neural networks and other sophisticated machine learning techniques are necessary for autonomous navigation in challenging terrain. However, the inconsistent annotations by citizen scientists frequently hinder the performance of these models. This study seeks to optimize terrain segmentation to improve the autonomous capabilities of future Mars rovers by presenting a novel weakly supervised learning framework to handle noise and unreliability in datasets. Using factors like number of clicks, pixel accuracy, and annotator dependability, the method utilizes annotation metadata in …