Artificial Intelligence and Robotics Commons^™

Annotation-Free 6-Dof Grasp Detection Method Integrating Physical And Geometric Priors, Min Shi, Shisheng Guo, Suqin Wang, Zhaoxin Li, Dengming Zhu

Journal of System Simulation

To improve the stability and cross-category generalization capability of grasp pose estimation in complex stacked scenes, an annotation-free 6-DoF grasp detection method integrating physical rules and geometric structure priors was proposed. In the offline stage, a template library of feasible grasp poses was constructed based on multi-physical constraints, without relying on manual grasp annotations. In the network design, the modeling of structural symmetry of objects and spatial overlap relationships was introduced; a geometric guidance mechanism with occlusion perception and exposure modeling capabilities was designed, and robust pose alignment of target objects was achieved by combining keypoint regression. A multi-type stacked …

Detection Method For 3d Lanes Based On Graph Relationship Optimization Integrating Point And Lane Features, Yanji Jiang, Xingyi Xiao, Hao Dong, Miao Yu, Jinshan Huang, Daqian Liu, Bowen Fei

Journal of System Simulation

Under complex road conditions, the thin and elongated structure and small proportion of lanes lead to blurred visual features and insufficient positioning accuracy, which in turn threatens the road safety of autonomous driving. To address these issues, a 3D lane detection method or graph-based point and lane optimization network (GPLNet), based on graph relationship optimization integrating point and lane features, was proposed. Preliminary feature extraction was completed by the backbone network. 3D spatial positional coding with geometric constraints was obtained through a joint query embedding generation module. A graph relationship optimization network was utilized to perform graph relationship calculation and …

Multi-Level Digital Model Of Dynamic Earned Value Management For Complex Engineering Projects And Its Applications, Wei Wang, Dong Liu, Xinhao Cui, Bo Li, Yiyong Xiao, Yi Ren

Journal of System Simulation

The economic management of existing engineering projects is usually based on organizational structure, which presents problems such as complex processes and difficulty in clarifying main responsibilities when applied to complex engineering projects. In response to this limitation, a multi-level digital model of dynamic earned value management is proposed for complex engineering projects, which extends the traditional cost performance indicators to engineering resource utility indicators, thereby decomposing the earned value of costs into segmented earned values of different engineering resources. This enables managers to dynamically supervise projects based on traditional "schedule-cost" performance indicators and carry out more refined cost control …

Hierarchical Motion Planning Of Mobile Robot Based On Dynamic Corridor Inflation And Convex Optimization, Dingkun Zhang, Haizhao Liang

Journal of System Simulation

Motion planning for robots with Ackermann chassis in dynamic complex environments faces nonholonomic constraints and kinematic-dynamic coupling challenges. However, traditional methods suffer from path redundancy, random fluctuations, and local optimality. A hierarchical motion planning method based on dynamic corridor inflation and convex optimization is proposed. Topologically sparse paths are generated by fusing the Ramer-Douglas-Peucker (RDP) path compression operator with the A* algorithm to reduce redundant path points' interference with backend optimization. Dynamic corridor inflation strategies are designed considering Ackermann steering characteristics, and safe corridors satisfying kinematic constraints are constructed via convex decomposition. Corridor constraints are then transformed into linear inequalities …

Power Flow Calculation Based On Block-Encoded Adiabatic Quantum Newton-Raphson Method, Shengchao Jiang, Yunqing Pei, Hongying Zhai, Guojian Wu, Fang Gao

Journal of System Simulation

To overcome the efficiency bottleneck of the traditional Newton-Raphson (NR)method in high- dimensional power flow calculations for modern power systems and the constraints of variational quantum algorithm frameworks, this paper proposed a power flow calculation framework integrating block encoding technology and adiabatic quantum computing principles. Based on block encoding technology, adiabatic quantum theory, and the NR method, a block-encoded adiabatic quantum power flow calculation framework (BQ-NR) was constructed. The NR correction equations were mapped to a quantum system, and the quantum state encoding of the correction equations was realized by constructing an extended Hermitian matrix and a projection operator; a …

Hyperspectral Anomaly Detection Algorithm Based On Window Reconstruction And Collaborative Representation, Shuanghao Fan, Fang He, Jianwei Zhao, Haojie Hu, Fengchao Zhu, Xiangyang Li

Journal of System Simulation

Hyperspectral anomaly detection refers to identifying ground objects that deviate from normal background distributions and have low probability and small scales from scenes involving mixed multi- class ground objects, spectral feature overlaps, and noise interference. This technology has received extensive attention in recent years. Although collaborative representation-based anomaly detection algorithms demonstrate excellent performance in hyperspectral image anomaly detection, their time costs are too high to enable widespread application.To address this issue, this paper proposes a hyperspectral image anomaly detection algorithm based on window reconstruction and collaborative representation, which consists of two stages. Window reconstruction is performed on hyperspectral background …

Seizing Strategic High Ground Of Space Computing Power: Global Competition Landscape And China’S Path, Yan Chen, Wenbin Song, Ping Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The deep integration of artificial intelligence and commercial aerospace is accelerating the transformation of space computing power from conceptual exploration to engineering verification, becoming a key direction for building an integrated space-air-ground information infrastructure. This study delves into its strategic value, global landscape, industrial chain bottlenecks, and advancement paths. The research reveals that the core value of space computing power does not lie in replacing ground data centers, but rather in focusing on network coverage blind spots, data transmission limitations, and high-timeliness scenarios, providing a new supply model of “in-orbit computing + space-ground collaboration”. Currently, the world has entered a …

Exploring Ai-Driven Scaffolding For Critical Questioning In Argument Evaluation, Ebenezer A. Belete

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fast-paced changes caused by generative AI (GenAI) innovations call for exploring the potential benefits of GenAI in empowering 21st-century pedagogical strategies. Previous studies in the field of argumentation have shown how students can benefit from using critical questions. However, scaffolding argument evaluation through custom GenAI using critical questions has not been systematically investigated. This study involved two components: (1) designing and determining the usability of a GPT-powered conversational assistant (CQMAA Conversational Assistant) and (2) testing its impact on participants' efficacy for argument evaluation and their acceptance of GenAI as a learning tool through a pretest–posttest experiment. A convergent mixed-methods …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Toward Vehicle-Agnostic Driving Signatures For Cognitive Impairment Prediction From Naturalistic Driving Data, Aadarsha Gopala Reddy

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis studies whether naturalistic driving data can help predict binary Clinical Dementia Rating (CDR) status while accounting for differences across vehicles. The final analytic dataset comprised 26,968 participant-weeks from 304 participants. Weekly driving features were derived from real-world telematics data and combined with four demographic covariates. Primary model comparisons used leave-one-participant-out (LOGO) cross-validation, with one individual held out at a time and pooled participant-level metrics used as the main reporting surface.

The main comparison includes six model families evaluated on the same dataset under a shared LOGO framework. Performance remained modest overall. GRU-DANN had the highest participant-level ROC AUC …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …

​Autonomous Agentic Orchestration For Physics-Aware Scientific Discovery: An Integrative Multimodal Framework For 2d Material Characterization​, Sankalp Pandey

Electrical Engineering and Computer Science Undergraduate Honors Theses

The advancement of next-generation semiconductor and quantum technologies relies on the scalability of the fabrication of two-dimensional (2D) van der Waals heterostructures. However, this process is severely bottlenecked by characterization workflows. Optical microscopy provides high-throughput imaging of 2D material flakes, but lacks the explicit physical priors required for the discernment of sub-nanometer thickness variations, such as distinguishing monolayers from bilayers. The use of computer vision models to automate the localization and characterization process of the flakes was proposed. As a part of this effort, we develop QuantumFlake, an open-source framework to streamline the integration and deployment of computer vision models …

Robustness Of Vision Language Models For Pedestrian Detection Tasks, Ostonya K. Thomas

All Theses

Autonomous vehicle (AV) systems typically employ modular systems in which discrete components handle separate tasks such as perception, computation, and path planning. While flexible, this approach allows errors to propagate and compound across the pipeline, and many AI systems offer little transparency into their internal decision-making. Such limitations are particularly concerning in safety-critical domains where failures can carry lethal consequences. Vision Language Models (VLMs) have emerged as a promising alternative because they support end-to-end implementations that bypass compounding error risks and provide natural language explanations of their outputs. Despite these advantages, prior research has demonstrated that both computer vision systems …

Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf

Electrical & Computer Engineering Projects for D. Eng. Degree

As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …

Autonomous Deficiency Detection And Vision-Language Summarization For Underground Infrastructure On Embedded Edge Systems, Johny Lopez

LSU New Orleans Theses and Dissertations

Aging underground infrastructure poses significant risks to public health and environmental safety, yet structural condition assessment remains bottlenecked by labor-intensive manual CCTV inspections. This thesis proposes a comprehensive algorithmic framework enabling fully autonomous, real-time deficiency detection, geometric assessment, and natural language reporting on resource- constrained edge computing platforms. Three core components address this challenge. First, RAPID-SCAN, a novel semantic segmentation architecture utilizing a Dynamic Feature Pyramid Network and Channel-Spatial Attention, achieves real-time, pixel-precise defect localization with dramatically reduced parameters. Second, an Edge-Optimized Vision-Language Model pipeline employing LoRA and 4-bit QLoRA quantization compresses Phi-3.5 for local deployment, en- abling autonomous technical …

From Data Digitization To Personalized Care: Deep Learning And Decision Analytics In Healthcare Systems, Ahla Ko

Graduate Theses and Dissertations

This dissertation traces a progression from foundational data infrastructure to individualized clinical decision-making, developing and evaluating three computational frameworks that advance healthcare efficiency and personalization through deep learning and decision analytics. The first study presents an end-to-end pipeline for automated recognition of handwritten medical forms, addressing persistent challenges in health data digitization. Integrating a YOLO-based field detection model, a Convolutional Recurrent Neural Network (CRNN) for text transcription, and a confidence-based human-in-the-loop quality assurance framework, the system achieved 99.75\% Exact Match Accuracy and a 0.13\% Character Error Rate on medical forms collected from a tuberculosis research project in Moldova. A GPU-accelerated …

Analyzing The Writing Style Of Generative Ai When Prompted With Writing Samples, Samuel Mcdowell

Senior Honors Theses

Authorship attribution is an important topic in today’s world of Large Language Models (LLMs). It is the technology that helps to verify the author of a written work. This study explores whether LLMs can successfully mimic an individual’s writing style if they are given a text sample. A dataset of human-written texts was collected and used to prompt several LLMs to generate new texts that attempt to replicate the original author’s stylistic characteristics. The generated texts were then tested with modern authorship attribution models to determine whether they would be identified as being written by the original author. The results …

Learning Global Context For Sparse Activity Recognition In Lengthy Recordings With Limited Dataset Size, Zeyu Tang

All Dissertations

This dissertation describes methods to analyze lengthy recordings of data in order to detect sparsely occurring activities. The narrative below describes the progression of research that led to the development of these methods and their generalization into a unified framework. My research started with designing models for dietary monitoring, including detecting meals from day-long recordings and detecting intake gestures from meal-length recordings. Both tasks share some common characteristics: (a) the target event takes only a small portion of data recordings, and (b) there is global context within full-length data recordings that can help a model make better decisions. After finishing …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Aircraft Fault Detection Via Weight And Bias Analysis Of A Custom First Neural Network Layer, George Harrison Chen

Theses and Dissertations

Fault detection in aircraft is traditionally handled through redundant hardware and comparison algorithms to detect failures. Alternatives like model-based residual generation and data-driven approaches such as supervised fault classification and unsupervised anomaly detection have been explored, but they suffer from practical limitations; model-based methods require accurate system models, and data-driven methods have large constraints on the data limiting scalability and adaptability. This work presents a purely data-driven neural network architecture featuring a custom first layer designed for real-time fault detection where the weights and biases of this layer are used to detect faults. The network requires zero supervision and complements …

Adaptive Artificial Potential Field Guidance And Control For Autonomous Docking With Uncooperative And Unknown Spacecraft, Steven Holmberg

Theses and Dissertations

The increasing demand for on-orbit servicing (OOS), active debris removal (ADR), and space domain awareness (SDA) missions has increased the need for autonomous spacecraft rendezvous and proximity operations (RPO) with uncooperative and unknown targets. Traditional guidance and control methods are typically designed for cooperative systems with known geometry and state information. This work builds on previous research to develop and evaluate an artificial potential field (APF)-based control framework capable of autonomous operation with minimal prior target knowledge and applicability to both relatively static and tumbling spacecraft.

The proposed APF formulation incorporates established safety constructs from cooperative docking systems, including an …

Modelling Method Of Unmanned Vehicle Dynamics Based On Neural Network, Jun Wang, Min Liu, Xiaochuan Zhang, Yishan Ding, Juhui Feng, Ye Zhuang

Journal of System Simulation

Abstract: To address the challenges of high data acquisition costs of test data on dynamic characteristics between tires and soft terrain and low speed of numerical calculation for unmanned vehicles in complex terrestrial environments, a modeling method of unmanned vehicle dynamics based on a neural network was proposed. Tire-terrain contact dynamics models were built by using discrete element method (DEM) simulations for tire-terrain contact and experimental data, thereby creating a dataset of tire contact forces for various tire materials in terrestrial environments. The neural network was applied to regressively learn the dataset, and a nonlinear neural network tire model was …

Method For Testing And Evaluating Intelligence Level Of Virtual Forces Based On Operational Experiments, Dayong Liu, Zhiming Dong, Weidong Zhang, Wenjun Zhang, Jiancheng Gao

Journal of System Simulation

Abstract: The intelligence level of virtual forces is a key factor affecting the credibility and effectiveness of tactical confrontation simulations. To address the current lack of a testing and evaluation system, a method for testing and evaluating the intelligence level of virtual forces based on operational experiments is proposed. Guided by operational experiment theory, the method stimulates the intelligent behavior of virtual forces by constructing dynamic confrontation environments, and collects, calculates, analyzes, and evaluates their intelligence performance data according to a systematic process. The overall architecture, logical functional modules, and basic evaluation process of the method are designed. A "4M" …

Model-Based System Verification: Theoretical Framework, Key Technologies, And Future Prospects, Bo Sun, Yi Ren, Silin Wang, Qi Liu, Zhidong Li

Journal of System Simulation

Abstract: Traditional system verification methods face significant challenges in terms of efficiency, coverage, and traceability. To address these issues, this paper introduced model-based system verification (MBSV), which deeply integrated verification activities within the model-based systems engineering model system and evolution process. It presented the foundational logic of MBSV and proposed a multiview unified verification modeling strategy based on system modeling language (SysML), integrating requirements, structure, behavior, and constraints. The paper discussed the algorithms for selecting representative paths and reducing equivalent classes to enhance verification efficiency, the principles of test path search, as well as the intelligent path search mechanism based …

Large Language Model For X Language Simulation: Architecture, Key Technologies, And Typical Applications, Laichunyang Peng, Fei Ye, Xiaoming Guo, Jinglin Zhou

Journal of System Simulation

Abstract: General-purpose large language models lack training on X language-specific corpora, and traditional fine-tuning methods lack targeted adaptation to the interdisciplinary integration and multimodule coupling of X language, resulting in problems such as non-standard syntax and semantic deviation in generated code. To address these issues, this paper systematically proposed the definition and integrated architecture of a large language model for X language simulation. Modeling subclasses were defined according to the disciplines and classes of X language, and dedicated adapters were constructed for each subclass. By merging their weights during the inference phase, the incremental integration of multi-domain modeling skills was …

Space-Ground Integrated Collaborative Positioning Algorithm And Simulation For Trajectory Enhancement, Juhui Wei, Xinyong Zhang, Jiongqi Wang, Xuanying Zhou, Zhangming He

Journal of System Simulation

Abstract: To address the challenges of low credibility, weak consistency, and poor accuracy in the information of trajectory results from space-based and ground-based passive time difference positioning simulation systems, a space-ground integrated collaborative positioning method was proposed for trajectory enhancement. By analyzing the operating principle of the time difference positioning system, the influencing factors that measure positioning accuracy in different feature dimensions were obtained; spline smoothing was employed for data alignment between space-based and ground-based systems; a spline-constrained parametric trajectory model was proposed to further enhance the stability; an error-sensitive feature selection framework for improving simulation consistency was constructed to …

Intelligent Competition Platform And Mode Driven By Cloud-Native Simulation, Long Qin, Hesong Huang, Lujia Yin, Chuan Ai, Qi Zhang, Xinmeng Li

Journal of System Simulation

Abstract: To solve the problems faced by the adversarial competition mode of agents, including difficult development and deployment, low resource utilization, poor reusability, and difficulty in accessing reinforcement learning algorithms, a new agent simulation training platform was designed. The software components of the competition platform were decoupled based on cloud-native technology; a high-performance simulation engine for the competition environment was proposed; a new method of an embedded reinforcement learning model for an intelligent control terminal was designed, with multiple online and offline policy-based reinforcement learning algorithms set. The experiment demonstrates that the development and deployment of the system is efficient, …

Research On Control Of Coaxial Dual-Rotor Unmanned Aerial Vehicle Based On Improved Reaching Law, Xinhang Chen, Xiaodong Ling, Chengchang Lang, Shijun Zheng, Yiqi Tang

Journal of System Simulation

Abstract: To address the issues of significant chattering, slow convergence speed, and large overshoot in the control system of a coaxial dual-rotor unmanned aerial vehicle, a control method based on an improved double-power and hyperbolic function integral sliding mode reaching law was proposed. A novel reaching law was designed to achieve fast convergence when the system state is far from the sliding surface and smooth transition when approaching the sliding surface, thereby enhancing the overall convergence speed of the system and ensuring that the system reaches the sliding surface within a finite time. The saturation characteristic of the hyperbolic function …

Review On Optimization Of Simulation Modeling Strategies For Spacecraft Orbit Avoidance, Guozheng Li, Rui Wang, Shichao Fan, Xintong Cai, Xinyue Zhai

Journal of System Simulation

Abstract: The number of on-orbit spacecraft increases exponentially; the space environment becomes more complex, and the collision risk of on-orbit spacecraft increases significantly. On-orbit safety is thus severely threatened, posing higher requirements for orbit avoidance methods. The costs and risks of space activities are extremely high, making simulation an effective method to solve complex problems of orbit avoidance. The modeling, solution, and simulation methods for the two core issues of spacecraft orbit avoidance, "collision avoidance" and "pursuit-evasion games", were systematically reviewed, and the existing shortcomings were analyzed. The applications of technologies such as deep reinforcement learning in promoting orbit avoidance …