Numerical Analysis and Scientific Computing Commons^™

Research On Dual-Layer Path Planning Method For Lunar Rover Based On Slip Prediction, Zhang Xingyu, Baolei Wu, Jun Wang, Miaoying Hong, Jiahui Wang, Yongqiang Qi

Journal of System Simulation

Abstract: In response to the challenges faced by lunar rovers in the process of path planning, such as safe obstacle avoidance and target deviation caused by complex terrain, a dual-layer path planning based on slip prediction is proposed. In this approach, flat terrain is adaptively selected to reduce the wheel slip of the lunar rover. The overall complexity of the terrain is calculated using digital elevation information, and a Q-learning algorithm with a three-level reward mechanism is designed to navigate around highslip areas, achieving global path planning. A depth camera is used to perceive obstacles, a dynamic window method based …

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 …

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 …

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 …

Optimization Of Vehicle Routing For Cross-Infection Risk In The Epidemic, Xiaodong Shi, Yongcheng Guo, Mingqi Ma, Jiarui Pan

Journal of System Simulation

Abstract: In view of the safety risks associated with logistics distribution route optimization during public health emergencies, this paper investigates the vehicle routing problem by incorporating the risk of cross-infection, integrates the cross-infection risk caused by logistics activities in the epidemic area into the logistics distribution model, and establishes a logistics vehicle distribution model with the goal of cross-infection risk and cost. An improved genetic algorithm is designed for model optimization and solution. Based on the integration of chaos initialization population and adaptive crossover and mutation operations, a neighbor exclusion operator is further proposed to enhance the global search ability …

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 …

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 …

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 …

Signal Timing Optimization Via Reinforcement Learning With Traffic Flow Prediction, Ming Xu, Jinye Li, Dongyu Zuo, Jing Zhang

Journal of System Simulation

Abstract: In response to the existing reinforcement learning-based traffic signal control methods that do not consider the changing trends in traffic flow, leading to congestion and inability to adapt to complex and variable road conditions, we propose a traffic signal timing optimization reinforcement learning method based on flow prediction. A phase timing amplitude control model is introduced. This model analyzes the spatiotemporal characteristics of historical traffic data to predict the flow for the next time slot and calculates a reasonable range for phase timing based on the prediction results. The H-PPO algorithm is employed to control the signal phase while …

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 …

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 …

Disentanglement In Representation Learning: Interpretability In Dimension Reduction With Vae, Minh Hong Vu

LSU Doctoral Dissertations

This research explores both theoretical and practical aspects of disentangled representation learning by extending the VAE framework. We address the core challenge of extracting independent generative factors from observed data while preserving high reconstruction fidelity. To this end, we propose two novel VAE variants: (i) the $\lambda\beta$-VAE, which incorporates an additional $\ell^2$-norm reconstruction loss to improve accuracy, and (ii) the $\gamma\beta$-VAE, which introduces a mutual information regularization term to encourage independence across latent dimensions.

Our theoretical analysis is conducted in a linear Gaussian setting, where we derive optimal solutions for these VAE-based models. We further examine how varying levels of …

36 - Investigation Of The Digital Footprint Of Scientific Research In Social Media – Preliminary Findings, Lee Logan, Dominik Soos, Sean Baker, Jian Wu

Undergraduate Research Symposium

Title: Investigation of The Digital Footprint of Scientific Research in Social Media – Preliminary Findings

Authors: Lee Logan, Sean Baker, Dominik Soos, Jian Wu

The spread of scientific information and research beyond the confines of academic institutions plays a central role in how the public understands and trusts modern sciences. Social media has become an essential means of dissemination for scholarly news, papers, and other forms of engagement. This research aims to explore how scientific research is disseminated over social media to understand its role as a bridge between peer-reviewed research and the public's overall understanding. To support the research …

Towards Testing, Detecting, And Debloating Insecure Components In Android Applications, Zicheng Zhang

Dissertations and Theses Collection (Open Access)

The Android ecosystem’s openness and extensibility have fueled its dominance in the mobile market, but they also broaden the attack surface of applications by introducing insecure or redundant methods. Vulnerabilities arise from various sources, including insecure API usage, code cloning, and feature bloat, especially from unneeded components introduced during development. To address these challenges, this dissertation presents a systematic, three-phase pipeline that transitions seamlessly from vulnerability discovery to clone-based detection and, ultimately, to dynamic mitigation through runtime debloating. Each phase builds upon the insights and limitations of the previous, collectively forming a practical approach to improving Android app security.

In …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

Prioritizing Speech Test Cases, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, Bowen Xu, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

As Automated Speech Recognition (ASR) systems gain widespread acceptance, there is a pressing need to rigorously test and enhance their performance. Nonetheless, the process of collecting and executing speech test cases is typically both costly and time-consuming. This presents a compelling case for the strategic prioritization of speech test cases, which consist of a piece of audio and the corresponding reference text. The central question we address is: In what sequence should speech test cases be collected and executed to identify the maximum number of errors at the earliest stage? In this study, we introduce PRiOritizing sPeecH tEsT …

On The Probability Of Necessity And Sufficiency Of Explaining Graph Neural Networks: A Lower Bound Optimization Approach, Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao

Research Collection School Of Computing and Information Systems

The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two. Theoretically, the Probability of Necessity and Sufficiency (PNS) holds the potential to identify the most necessary and sufficient explanation since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limit its …

Learning-Guided Bi-Objective Evolutionary Optimization For Green Municipal Waste Collection Vehicle Routing, Shubing Liao, Yixin Xu, Yunyun Niu, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Waste management has emerged as a critical issue in modern society, where vehicles are scheduled to visit multiple locations for waste collection and transport. This study focuses on a key problem in waste management: route optimization of waste collection vehicles, and formulate it as a bi-objective vehicle routing problem with stochastic demand (VRPSD), aiming to minimizing both total costs and carbon emissions. Although previous studies have significantly advanced our understanding of solving similar problems, the lack of real-world data and limited problem-solving capabilities still restrict the practical applicability of existing methods. To bridge this research gap, this study designed a …

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …