Computer Sciences Commons^™

Using Automotive Lidar To Reduce The Energy Consumption Of An Ego Autonomous Vehicle, Logan P. Schexnaydre

Dissertations, Master's Theses and Master's Reports

There is significant potential to reduce the energy consumption of the transportation sector through autonomous vehicles. Prior work on autonomous vehicle energy efficiency focuses on the whole system or the control subsystem. Yet, the sensing and processing components, which have direct and indirect effects on net energy use, are less explored. This dissertation fills this gap by modeling and evaluating these effects for lidar sensors, which provide high-resolution spatial data at the cost of high power and processing demands. I apply lidar to the energy-saving tasks of automated vehicle following and road surface profiling. For automated vehicle following, I model …

A Specification-Driven Framework For Self-Supervised Learning In Specialized Vision Domains, Mahmut S. Gokmen

Theses and Dissertations--Computer Science

Self-supervised learning (SSL) has emerged as a principled approach to visual representation learning that derives supervisory signal directly from unlabeled data, enabling foundation models to be trained at scale without manual annotation. Deployments in medical imaging and biometric recognition have demonstrated the potential of this paradigm, yet the assumptions that make SSL effective on natural image benchmarks fail systematically in specialized domains. Generic SSL pipelines encode a tacit assumption that the most informative correspondence is spatial proximity within a single acquisition. In specialized domains this assumption breaks at the level of the data-generating process: the signal that carries domain-specific information …

A Web-Based Wizard-Of-Oz Platform For Collaborative And Reproducible Human-Robot Interaction Research, Sean O'Connor

Honors Theses

The Wizard-of-Oz (WoZ) technique is widely used in Human-Robot Interaction (HRI) research, but two persistent problems limit its effectiveness: existing tools impose technical barriers that exclude non-engineering domain experts (the Accessibility Problem), and the fragmented landscape of robot-specific implementations makes interaction scripts difficult to port across platforms (the Reproducibility Problem- concerning execution consistency and portability, not third-party replication). Through a literature review, I identified three design principles to address both: a hierarchical specification model, an event-driven execution model, and a plugin architecture that decouples experiment logic from robot-specific implementations. I realized these principles in HRIStudio, an open-source, web-based platform providing …

A 1d Symmetric Interior Penalty Discontinuous Galerkin Solver In Rust, William Aey

Williams Honors College, Honors Research Projects

This honors project will build a 1D Symmetric Interior Discontinuous Galerkin (SIPDG) solver in Rust for Stum-Liouville type problems such as the Poisson equation, with Robin, Dirichlet, and Neumann boundary conditions. The work will cover the full pipeline: starting from the strong form of the PDE, deriving the DG weak form, implementing element and interface operators, and assembling or apply the discrete operator. Rust's safety and concurrency (e.g, via Rayon) will be used to explore serial and parallel performance. A test-driven development approach will be used to maintain a strong suite of tests. The project will result in a documented …

Analyzing Network Traffic And Data Exfiltration Via Smb In Post-Vm Escape Scenarios, Noah M. Disanza

Williams Honors College, Honors Research Projects

Virtual machines (VMs) play a crucial role in modern IT infrastructure environments by providing isolation and enhanced security, among other things, for both personal and corporate systems. VMs are heavily rely upon to safely test malware, manage infrastructure, and reduce risk to host systems. This reliance is so substantial that the idea of reducing risk to the host system is believed to be erasing risk entirely. However, this mindset has shown to be challenged time and time again by the emergence of exploits known as virtual machine escapes. These exploits allow malicious actors to break out of the virtualized environment …

Proof Of Recovery: A Model To Enhance Data Integrity In Data Management Systems, Gustaf Barkstrom

Theses and Dissertations

Businesses lose millions of dollars every year when they can’t restore data from backups. Research shows that Disaster Recovery Plan (DRP) testing is not conducted frequently enough, nor are records maintained that demonstrate full data recovery from backups. This work introduces a design science artifact called PRTOK that aims to increase DRP testing. The design science artifact is a software solution that integrates with Data Management Systems (DMS)

such as iRODS and DSpace, and can work with formats such as HDF5 and BagIt. Proof-of- recovery records, or tokens, are recorded in a replicated, resilient, and indelible proof-of- authority blockchain data …

The Excess Path Length Distribution: A Stochastic Model For Sample-Based Path Planners, Chaz B. Cornwall

Dissertations, Master's Theses and Master's Reports

Through random sampling, sample-based path planners enable autonomous agents to quickly find paths without human intervention. However, due to the paths' randomness, sample-based path planners currently require additional verification, partially nullifying agents' ability to act autonomously. I set out to characterize this uncertainty so humans know what to expect from these path planners and know how to alter the path planner to desired specifications. To ensure the results are theoretical as well as practical, I first create a stochastic model of path length uncertainty using the trade-off between sampling time and optimality. By leveraging this model, my proposed algorithm reduces …

Designing Narrative-Based Ai Assistance For Sensemaking In Collaborative Environments: Case Studies In Education And Dementia Care, Dylan Edward Moore

Dartmouth College Ph.D Dissertations

This thesis addresses a gap in the human-computer interaction literature regarding the design, development, and evaluation of narrative-based AI assistance for collaborative, complex problem solving. I explore this design space through three case studies across the domains of education and dementia care. This work encompasses multi-year industry partnerships and longitudinal fieldwork, user-centered design, dataset curation, model training, and system evaluation.

Specifically, the first case study considers a story-based web platform for teaching AI literacy through peer-generated, personalized narrative scaffolding. Learners on the platform showed significant knowledge gains and other learning-related outcomes. To describe the novel design of this system, I …

Handwriting Recognition In Vr, Dominique Mosley

EWU Masters Thesis Collection

Virtual Reality (VR) is slowly becoming more popular for more than just entertainment. VR can be found in educational, office, and even healthcare settings to help discover more intuitive ways to teach, collaborate, and treat patients. Outside of the virtual world, these environments typically rely on writing for communicating or note-taking. Currently, VR input forces users to rely on clunky on-screen keyboards which disrupts the user’s immersion and breaks the flow of natural interaction. This thesis explores the potential of VR as a learning platform by combining it with artificial intelligence (AI). It aims to develop a VR-enhanced handwriting practicing …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

Supply Chain Network Based On Blockchain And Intelligent Agent, Hiba Hamdi Hassan, Rana Fareed Ghani

Journal of Soft Computing and Computer Applications

In agricultural supply chains, the complexity and indeterminacy pose serious challenges to traceability, reliability and confidence today. This challenge is especially acute in the olive oil industry where adulteration, wrong labeling, and uneven chemical quality threaten the actual well-being of producers and consumers. The project aims to design a blockchain-based hybrid architecture with intelligent agents (FNNs) to enhance transparency, reliability and responsiveness in the olive oil supply chain. The Blockchain component enables a completely open, tamper-proof ledger to be built in a very decentralized way and preserved as an archive of every account of its transactions. The intelligent agents contribute …

Intelligent Extensible Markup Language Encryption Using Type-2 Fuzzy Logic, Faiez Musa Lahmood Alrufaye, Seham Ahmed Hashem

Journal of Soft Computing and Computer Applications

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic …

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

Review Of Video Steganography By Using Deep Learning Methods: Datasets, Techniques, And Evaluations, Noor Fahem Sahib, Soukaena Hassan Hashem, Ekhlas Falih Naser

Journal of Soft Computing and Computer Applications

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a …

Real-Time Hand Gesture Recognition System For Abductees Rescue Using Deep Learning Techniques, Aws Saood Mohamed, Nidaa Flaih Hassan, Abeer Salim Jamil

Journal of Soft Computing and Computer Applications

Hand gesture recognition is a challenging problem in computer vision, particularly in terms of security surveillance applications. This study presents the first efficient system for abduction-related hand gesture real-time detection based on deep learning. The most critical problem is to detect and recognize hand gestures in real surveillance conditions and to be computationally effective for real-time multi-hand tracking in various lighting situations while allowing reliable surveillance beyond the 1–4 meters limitation. The proposed system consists of three main parts: The adaptive hand tracking algorithm, which has been used to create the Abductees-Rescue dataset. Introduced pose estimation You Only Look Once …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

Measurement Of Luminous Intensity Distribution For Film And Television Led Light Sources And Its Simulation Research In Game Engines, Jingyi Suo, Baihong Lu, Che Qu

Journal of System Simulation

Abstract: To address the issues of mismatched photometric characteristics between light sources in virtual environments and real-world lighting during film and television lighting design and lighting preview using game engines, a testing solution for measuring the luminous intensity distribution for film and television LED light sources was proposed, building upon existing luminaire light intensity distribution testing systems. Based on the obtained data, a light source calibration process was constructed in the UE5 to correctly simulate the photometric characteristics of light sources in the virtual environment. Simulation results have shown that the calibration process can accurately and efficiently reproduce the …

Improved Pid Search Algorithm For Uav Path Planning In Mountainous Environments, Yi Peng, Yunkui Lei, Qingqing Yang, Hui Li, Jianming Wang

Journal of System Simulation

Abstract: To address the challenges of UAV path planning in mountainous environments, including high computational complexity and suboptimal optimization performance, and the disadvantages of the PIDbased search algorithm, such as low optimization accuracy and slow convergence rate, this paper proposed an improved PID search algorithm (IPSA). The method introduced a good point set to ensure a more uniform population distribution, thereby enhancing population diversity and global search capability. The Q-learning algorithm was employed to adapt PID parameter adjustments, incorporating an exploration rate factor to further improve the algorithm's exploration and computational capabilities. A lens imaging opposition-based learning mechanism was also …

Dynamic Characteristic Simulation And Optimization Of Ground Test System For Airborne Launch Rack, Yuguang Bai, Sheng Zhang, Yushun Cao, Xiaoshi Zhang, Hu Huang

Journal of System Simulation

Abstract: To solve the ground equivalent test problem of the airborne launch system, an optimization method for the dynamic characteristics of the ground launch rack test system based on a multi-variable optimization approach was proposed. Through the discussion on the boundary conditions of the foundation, an effective dynamic simulation model of the ground launch test system was established. By comparing the dynamic characteristics of the launch rack structure in the airborne state and the ground test state, the objectives and constraints of the optimization design were determined. The dynamic characteristics of the ground test system were optimized and designed. …

Optimization Of Dynamic Weapon Target Assignment Considering Random Disturbances, Zhenzu Bai, Yizhi Hou, Zhangming He, Juhui Wei, Haiyin Zhou, Jiongqi Wang

Journal of System Simulation

Abstract: The impact of various random disturbances in the actual command and control environment of unmanned systems on problem modeling and solving of weapon target assignment was considered, and three types of uncertainty disturbance constraints were investigated. A multi-objective dynamic sensor weapon target assignment model was established. By considering the issues of model property changes caused by disturbances and insufficient robustness of the traditional single-operator solving algorithm, a multi-operator constrained multi-objective evolutionary framework based on the deep Q-network was proposed. The algorithm described the convergence, diversity, and feasibility of the population in both the objective and decision spaces. It established …

Fault Diagnosis Method For Photovoltaic Systems Based On Multi-Strategy Fusion, Bin Li, Yuchuo Wang

Journal of System Simulation

Abstract: To address the problem of frequent PV system faults, a multimodal fusion fault diagnosis model based on the optimization of the improved lemming algorithm was proposed. The one-dimensional time series signals of PV currents and voltages were converted into two-dimensional images by Markov transformation field, and the spatial features of the original waveforms were mined by using multiscale CNN (MCCNN); BiGRU was used to extract the temporal dynamic features of the original waveforms, and complementary enhancement of the temporal and spatial features was realized by the feature fusion layer. The improved lemming algorithm was innovatively introduced to adaptively optimize …

Survey Of Cooperative Multi-Agent Path Finding, Jun Xiong, Wenbo Zhang, Zhi Xiong, Feng Zhou, Bo Yang

Journal of System Simulation

Abstract: Cooperative multi-agent path finding (Co-MAPF) has been widely applied in fields such as UAV formation and multi-agent systems, which enhances the overall system efficiency through task collaboration, path planning, and task execution among multiple agents. This paper introduced three main system architectures, namely centralized, distributed, and hybrid, along with their advantages and disadvantages based on the definition of the Co-MAPF problem, categorized, and reviewed mainstream Co-MAPF algorithms, including those based on sampling, search, intelligent optimization, and learning. Furthermore, this paper analyzed the main current challenges faced by Co-MAPF algorithms on the basis of summarizing existing research and outlined the …

Numerical Simulations Of Ship Liquid Tank Sloshing Based On Graph Neural Networks, Wenkang Zhang, Xiaofeng Sun, Yiping Zhong, Yong Yin

Journal of System Simulation

Abstract: To address the high consumption of computational resources in simulating ship liquid tank sloshing using computational fluid dynamics simulation methods, a data-driven numerical simulation model was proposed based on graph neural networks. An encoder-processor-decoder framework was employed in the proposed model. The encoder extracted features of fluid particles from the first five time steps. The processor learnt latent motion patterns of fluid and updated features, and the decoder predicted features of particles at subsequent time steps. The processor incorporated a self-attention mechanism to enable dynamic adjacency weight allocation and emphasize the influence of irregular tank wall regions. Training …

Optimization Of Order Picking And Sorting Coordintion In “Goods-To-Person” System, Liang Ren, Zerong Zhou, Yunfeng Ma

Journal of System Simulation

Abstract: To improve the order picking and sorting collaboration with time windows in the "goods-to-person" system, a mathematical model aiming to minimize the number of sorting batches was established. With the characteristics of this issue considered, a hybrid variable neighborhood search (HVNS) algorithm based on the "classified loading" strategy was proposed for solutions. The numerical experimental results show that the HVNS algorithm can obtain high-quality solutions while shortening the solution time; different order structures have varying effects on the utilization of the loading capacity of sorting automated guided vehicles (AGVs); under the tested experimental conditions, the collaborative operation mode …

Lightweight Assembly Workpiece Detection Algorithm Based On Improved Yolov8, Shuheng Wu, Yongkui Liu, Lin Zhang, Yingying Xiao, Lihui Wang

Journal of System Simulation

Abstract: To address the issues of low recognition accuracy and slow detection speed with existing deep learning-based object detection algorithms for robotic automatic assembly tasks, a lightweight assembly workpiece object detection algorithm based on YOLOv8 was proposed. The PConv was introduced to improve the C2f module, and a new Faster_C2f module was designed to enhance the detection speed of the model. The SIoU loss function was employed to optimize the location prediction accuracy of the CIoU loss function and improve the localization accuracy of small targets. The high-level screening-feature fusion pyramid networks (HS-FPN) structure was used to improve the Neck …

Interoperability Model And Application Of Military Training System For Combination Of Virtuality And Reality, Jianxing Gong, Hai Hu, Haihui Ren, Ruixiang Wu

Journal of System Simulation

Abstract: With the development of AI technology, VR technology, and combat simulation technology, in order to achieve the practical training effect of "how to fight and how to train soldiers", virtual and real training has become a widely popular military training mode. It has become a trend to integrate digital systems, virtual equipment, semi-physical models, physical models, and other heterogeneous systems to carry out training in the same training environment. Therefore, it is necessary to study the interoperability model and application of training systems for the combination of virtuality and reality. This paper proposed the definition of interoperability between virtuality …

Research On Infrared And Visible Light Fusion Method Based On Resnet-50 And Laplacian Filtering, Xiao Wang, Xiangyang Li, Feng Liang, Zhili Zhang

Journal of System Simulation

Abstract: In order to solve the problem that existing infrared and visible light image fusion techniques often suffer from artifacts caused by insufficient contrast, spectral distortion, and high computational complexity, a fusion framework based on ResNet-50 and Laplacian filtering was proposed. ResNet-50 was used to extract shallow and deep features, followed by multi-scale feature fusion. Laplacian filtering was applied to optimize feature information, and an automatic discriminator was introduced to further improve the fusion effect. Simulation results show that, compared with comparison algorithms, the proposed method achieves an average increase of 2.71% and 2.16% in information entropy, 5.98% and …

Analysis Of Optimal Spectral Bands For Thermal Infrared Hyperspectral Image Reconstruction Driven By Physical Simulation Model, Yonghao Yang, Xiaoyu He

Journal of System Simulation

Abstract: To achieve accurate reconstruction of thermal infrared hyperspectral images under limited spectral bands, this paper proposed a reconstruction method based on physical modeling and simulation. Semi-global decomposition algorithm was adopted to invert the thermophysical properties of the scenario based on the physical model of thermal radiation, simulating and generating full-band hyperspectral data. An optimal spectral band selection strategy driven by a physical model was proposed, which integrated the sensitivity of temperature inversion and the separability of material spectra. Experiments were conducted on both simulated and measured datasets to evaluate the performance of material identification, temperature inversion, and spectral …

Research On Pac-Bayes-Based A2c Algorithm For Multi-Objective Reinforcement Learning, Xiang Liu, Qiankun Jin

Journal of System Simulation

Abstract: To address the theoretical challenges of exploration and exploitation trade-offs and uncertainty modeling in multi-objective reinforcement learning (MORL), this study developed a learning framework, MO-PAC, based on PAC-Bayes theory. By introducing a multi-objective stochastic Critic network and a dynamic preference mechanism, the framework extended the conventional A2C architecture, enabling adaptive and efficient approximation of complex Pareto fronts. Experimental results demonstrate that in multi-objective MuJoCo environments, MO-PAC outperforms baseline algorithms, achieving approximately 20% improvement in hypervolume and 60% increase in expected utility, while exhibiting superior convergence efficiency and robustness. It verifies both theoretical value and practical performance advantages in …

Robust Emergency Dispatch Method Considering Dynamic Frequency Security And N-K Contingency, Tao Huang, Zhi Zhang, Yujie Ding, Yanbo Chen, Jing Wang, Wenqian Zhang

Journal of System Simulation

Abstract: To address the risk of system inertia loss and frequency instability caused by grid integration of high-proportioned new energy and unit failures, an N-k robust emergency dispatch method considering dynamic frequency security constraints was proposed. With the consideration of the frequency response characteristics of variable-speed pumped storage, a dynamic frequency response model incorporating variable-speed pumped storage was constructed, and the nadir frequency constraint was established through second-order cone transformation. Information entropy theory was employed to quantify the uncertainty of unit failures, and an uncertainty set considering N-k unit failures was developed. A twostage robust emergency dispatch model considering N-k …