Artificial Intelligence and Robotics Commons^™

An Intelligent Tracking Control Method For Unmanned Vehicles With Time-Varying Disturbances, Jie Huang, Jie Huang

Journal of System Simulation

Abstract: An intelligent policy iteration tracking control method is proposed for the tracking control problem with bounded time-varying disturbances. An adaptive disturbance compensator is designed to counteract the bounded disturbance and guarantee the validity of the Hamilton-Jacobi-Bellman (HJB) equation. An identifier network is proposed to estimate the unknown vehicle dynamics, and a new HJB equation is derived using the reconstructed identifier tracking error. An online optimal tracking control strategy for unmanned vehicles is obtained in the state of identifier estimation with the assistance of actor-critic network. Based on Lyapunov theory, it is demonstrated that the identifier tracking error, identifier approximation …

Design Of Distributed Multi-Functional Integrated Signal-Level Confrontation Simulation System In Local Area, Weiqian Li, Tianyu Yang, Zongyang Li, Jianjun Chen

Journal of System Simulation

Abstract: In order to study the resources management and self-organized collaborative application method of multiple multi-functional integrated electronic equipment in the region, we build a signal-level digital simulation system that supports multiple distributed multi-functional integrated electronic equipment within a region to carry out cooperation or confrontation. A joint time advancing mechanism named "variable-step time advancing method based on frame scheduling" and "independent event driven time advancing method " is proposed. It can not only ensure the integrity of each frame of radar simulation data for each equipment, but also enable multiple equipment in the simulation system to advance simultaneously and …

Uav Path Planning Based On Improved Deep Deterministic Policy Gradients, Sen Zhang, Qiangqiang Dai

Journal of System Simulation

Abstract: Aiming at the problems of poor convergence and invalid exploration when UAVs perform path planning in complex environments, an improved deep deterministic policy gradient(DDPG) algorithm is proposed. Using a dual experience pooling mechanism to store success and failure experiences separately, the algorithm is able to use the success experience to strengthen the strategy optimization and learn from the failure experience to avoid the wrong path; an APF method is introduced to add a bootstrap term to the planning, which is combined with the exploration of noisy actions in a randomized sampling process to dynamically integrate the selected actions; multi-objective …

Remote Sensing Small Object Detection Based On Cross-Stage Two-Branch Feature Aggregation, Jie Li, Yang Liu, Liang Li, Bengan Su, Jialong Wei, Guangda Zhou, Yanmin Shi, Zhen Zhao

Journal of System Simulation

Abstract: Aiming at YOLOv8's leakage and false detection problems caused by target scale difference and complex background in remote sensing small target detection, this paper proposes a remote sensing image small target detection method based on cross-stage two-branch feature aggregation. The global shared weights in the convolution operator and the context-aware weights of specific tokens in the attention are fused to obtain high-frequency local information and low-frequency global information; the global remote dependencies are captured using a lightweight MLP, and the parallel cross-stage learnable vision center mechanism is designed to capture the information of the local corner regions of the …

A Radar Countermeasure Modeling Method Incorporating Cognitive Bias, Rui Wang, Xiangyang Li, Dong Wang, Hongguang Ma, Zhili Zhang

Journal of System Simulation

Abstract: Cognitive bias, stemming from electronic measurement error and variability in human perception, exists in cognitive electronic warfare and affects the outcomes of conflicts. In this paper, the dynamic game approach is employed to develop a model for cognitive bias induced by incomplete information and measurement errors in cognitive radar countermeasures. The payoffs for both parties are calculated using the radar's anti-jamming strategy matrix A and the jammer's jamming strategy matrix B. With perfect Bayesian equilibrium, a dynamic radar countermeasure model is established, and the impact of cognitive bias is analyzed. Drawing inspiration from the cognitive bias analysis method used …

Digital Twin Framework For The Generation And Optimization Of Security Policies For Tsn Industrial Control Systems, Huimai Zhang, Xiaoya Hu, Chunjie Zhou

Journal of System Simulation

Abstract: The characteristic of multi-service flow integration in TSN industrial control systems makes it very difficult to establish an accurate mathematical model. In order to ensure the coordination between the security policy and the real-time operation of the system, a four-layer double-closed-loop digital twin framework of "physical layer-data layer-twin layer-service layer" serving the generation and optimization of security policies is proposed. The optimal security policy generation is achieved through the internal closed loop composed of iterative optimization between the initial security policy generation at the service layer and the deployment verification at the twin layer. The deterministic communication process between …

An Event Ontology And Dataset Construction Method For Strategic Operations Analysis, Quanlin Chen, Jun Jia

Journal of System Simulation

Abstract: Aiming at the lack of professional datasets for information extraction technology research in the field of strategic operations research analysis, this paper proposes an event ontology and dataset construction method for strategic operations research analysis. The method proposes an event ontology model for strategic operations research analysis according to the needs of situation judgment in strategic operations research analysis, and uses the method of "a small amount of manual annotation + fine-tuned large language model annotation" to construct the event dataset EfSOA for strategic operations research analysis. The dataset construction method proposed in this paper and the constructed dataset …

Research On Economic Dispatching Strategy Of Chp Units Based On Srl, Xin Wang, Chenggang Cui, Xiangxiang Wang, Ping Zhu

Journal of System Simulation

Abstract: In addressing the challenge of the DRL algorithm in the optimization of combined heat and power (CHP) units, lacking safety and stability guarantees, a scheduling optimization method based on SRL is proposed. Utilizing Dymola platform, a district heating system model is constructed with the CHP unit as the heat source. A MDP model for the economic dispatching of CHP units is designed, incorporating control barrier functions (CBF) to guide safe exploration in DRL. Simulation results show that the CBF-DRL method, in complex and nonlinear district heating systems, not only accelerates the convergence of DRL algorithms but also efficiently utilizes …

Mobile Robot Path Planning Based On Search-Step Optimized A* Algorithm, Die Yu, Baizhong Bao, Yan Si, Jian Duan, Xiaobin Zhan, Tielin Shi

Journal of System Simulation

Abstract: A search-step optimized A* algorithm is proposed to address the issues with the traditional A* algorithm in robot path planning tasks, such as the high time consumption in large-scale high-resolution maps and the poor paths qualitys. Based on the cubic Hermite curve, a set of search steps (the path edges connecting the current node to its successors) is constructed, which can match the size of the robot and satisfy the dynamic constraints of the robot. More accurate cost functions are established based on the length and maximum absolute curvature value of the curve. Experimental results show that compared with …

Capability Dependency Analysis Based On Kill Chain And Fdna, Yushuai Wang, Guangya Si

Journal of System Simulation

Abstract: To better support the operation SoS analysis, deeply analyze the impact of dependency relationship during mission accomplishment, and accurately grasp the deep logic of SoS capability generation, the capability dependency analysis method based on the kill chain and function dependency network analysis(FDNA) is proposed. Combined with the analysis of the characteristics of the capability dependency relationship, the kill chain closure and the kill web formation process are abstracted from the perspective of operational interaction, a capability dependency network modeling method for the SoS is proposed, and a specific process covering the identification of capability dependency, calculation of operability, solving …

The Hidden Carbon Footprint Of Ai Models: Gpu-Aware Carbon Modeling, Youzhi Li

Undergraduate Research Symposium 2025

The rapid growth of AI technology has sparked transformative innovations but also increased carbon emissions. Recent research found that computer systems' carbon emissions are shifting from operational carbon to embodied carbon, but they did not fully capture the rapidly evolving AI landscape. Most recent research focused on operational carbon, neglecting the long-term environmental impact of embodied carbon. We found two gaps that persist in recent research. First, current carbon modeling focused on Central Processing Units (CPUs), neglecting the carbon modeling of Graphical Processing Units (GPUs). Second, it focused on primary components, neglecting significant contributions from peripheral components to the embodied …

Ai Assistance In Legal Analysis: An Empirical Study, Johnathan H. Choi, Daniel Schwarcz

Journal of Legal Education

No abstract provided.

Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh

Computer Science ETDs

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text …

Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu

Cybersecurity Undergraduate Research Showcase

Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …

Ai & Xr Explorations To Support Social Interactions: Speculative Design For Ubiquitous Workplace Space By And For Neurodivergent Employees, Dominique Michaud, Alejandro Reyes, Jonathan Proulx Guimond, Fafadzi Akpene Agbe, Valérie Payen, Diane Gabrielle Tremblay, Marie Claude Leblanc, Claude Vincent, Geoffreyjen Edwards, Caroline Brassard, Marie Helene Parizeau, Valéry Psyche, Martin Caouette, James Hutson, Piper Hutson, Julie Ruel, Julien Voisin, Jocelyne Kiss

Faculty Scholarship

This study explores the potential of interdisciplinary theories and advanced technologies, such as augmented realities and artificial intelligence, to address the socio-professional integration challenges faced by neurodivergent individuals, particularly those on the autism spectrum. It investigates the design of personalized, functional spaces that integrate interconnected living environments and intelligent systems tailored to support communication needs. Using speculative design methodology, the research adopts an experiential framework to examine alternative solutions, starting with a central hypothesis and testing it through debates with researchers, experts, neurodivergent individuals, and knowledge users. The premise is rooted in the recognition that neurodivergent individuals encounter significant barriers …

Integrating Ai-Driven Neurofeedback With Brain-Computer Interfaces: A Paradigm For Effortless Learning And Workforce Transformation, James Hutson

Faculty Scholarship

This editorial discusses the merging of AI-driven neurofeedback with brain-computer interfaces (BCIs) to create a new model for effortless, unconscious learning. By interpreting and reinforcing specific neural patterns, these technologies can enable users to acquire skills without traditional instruction, making them especially valuable in fast-evolving industries. They also offer powerful tools for individuals with physical impairments by enabling control through thought alone. However, the author emphasizes the importance of ethical oversight, particularly around cognitive autonomy, data privacy, and consent. As the field matures, ongoing research and regulation will be essential to ensure responsible development and widespread, beneficial use.

Perceptions Of Ai Skills In Resumes, Brandy Whitford, Patrick J. Cooper

Faculty and Staff Publications & Presentations

No abstract provided.

Data-Driven Strategy For Contact Angle Prediction In Underground Hydrogen Storage Using Machine Learning, Mehdi Nassabeh, Zhenjiang You, Alireza Keshavarz, Stefan Iglauer

Research outputs 2022 to 2026

In response to the surging global demand for clean energy solutions and sustainability, hydrogen is increasingly recognized as a key player in the transition towards a low-carbon future, necessitating efficient storage and transportation methods. The utilization of natural geological formations for underground storage solutions is gaining prominence, ensuring continuous energy supply and enhancing safety measures. However, this approach presents challenges in understanding gas-rock interactions. To bridge the gap, this study proposes a data-driven strategy for contact angle prediction using machine learning techniques. The research leverages a comprehensive dataset compiled from diverse literature sources, comprising 1045 rows and over 5200 data …

A Machine-Learning Tool-Supported Methodology For Nonprofit Donor Analysis, Corbin Weiss

Campus Research Month

We developed a machine-learning tool-supported methodology for modeling the nonprofit donor relationship. This approach was demonstrated in the case of a US-based nonprofit. Conclusions were drawn from this example and tool-support provided for use by other nonprofits.

The Effects Of Ai Tutors On Beginner Programmers*, Karan Swansi

Campus Research Month

Generative AI’s ability to solve coding problems has raised concerns about Computer Science (CS) education. However, recent research has shown promise in its ability to tutor students. Specifically, The literature does not tend to adequately preserve desirable difficulties, such as active recall or higher-order thinking. And when they do, they use self-reporting instead of experiments to measure the effectiveness of the AI tutor.

Our research will address this significant research gap by using a no-code AI tutor to preserve desirable difficulties, and a randomized control experiment followed by a post-test to obtain strong evidence that AI can be an effective …