Engineering Commons^™

Fine-Grained Traffic Flow Inference Model Based On Dynamic Back Projection Network, Ming Xu, Guangyao Qi, Geqi Qi

Journal of System Simulation

Abstract: To solve the problem of large errors in the inference results of existing fine-grained urban flow inference models in complex traffic areas, a fine-grained traffic flow inference model based on dynamic back-projection network is proposed. The multi-dimensional interaction between the input coarse-grained traffic flow and external factors is calculated, and the interaction results are dynamically and adaptively fused with the coarse-grained traffic flow, so that the features can interact and adjust each other to assist model reasoning. Combining deep convolution and self-attention mechanism to learn local information and global information, and improve the understanding of input data by subsequent …

Research On Transformer Fault Diagnosis Method Based On Digital Twin, Lun Jiang, Dajiang Wang, Wenlei Sun, Shenghui Bao, Han Liu, Saike Chang

Journal of System Simulation

Abstract: Aiming at the inability of existing intelligent algorithms for transformer fault diagnosis to quickly and efficiently identify transformer faults, resulting in fault misdetection and untimely detection, this paper proposes a transformer fault diagnosis method using the improved sparrow optimization algorithm to optimize the two-layer fault diagnostic model of XGBoost combined with the digital twin technology. The method adopts advanced sensors to collect oil and gas data and temperature data of the transformer, uses 5G module to transmit the real-time data to the digital twin system. The system monitors the temperature data in real-time by setting the equipment alarm threshold; …

Spatio-Temporal Graph Neural Networks For Streamflow Prediction In The Upper Colorado Basin, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow prediction is vital for effective water resource management, enabling a better understanding of hydrological variability and its response to environmental factors. This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. Using 30 years of monthly streamflow data from 20 monitoring stations, the STGNN predicted streamflow over a 36-month horizon and was evaluated against traditional models, including random forest regression (RFR), LSTM, gated recurrent units (GRU), and seasonal auto-regressive integrated …

Investigating Spatial Mapping Of Lipid And Protein Alterations Within Cells And Tissues Using Raman Microspectroscopy, Elnaz Sheikh

LSU Doctoral Dissertations

The progression of various diseases is associated with alterations in the microenvironment of cells, consisting of a variety of metabolites like lipids and proteins. It is well known that lipids and proteins are potential biomarkers for indicating alterations within the cell microenvironment induced by disease, and various methods are frequently used for their examination. To understand the chemical composition and spatial distribution of constituents in tissues and cells, confocal Raman spectroscopy and microscopy, as non-destructive tools, have shown potential for monitoring abnormalities at the cellular level with high resolution and can facilitate detection through targeting multiple biomarkers. The application of …

03.17.2025 Ored Connect, Liz Williamson

ORED Newsletter

SPA director Andrea Rich

ARC Nomination Deadline Extended

Vibration Simulation And Multivariate Statistical Analysis Method Of Composite Structures, Bo Guo, Ming Tie, Wenhui Fan

Journal of System Simulation

Abstract: To investigate the natural frequency characteristics of composite laminates under parametric uncertainties and the different degree of influence of these parameters on the natural frequency under different boundary conditions and different vibration orders, a two-dimensional anisotropic medium-thick plate material model and a three-dimensional anisotropic cylindrical thin-shell material vibration model are established. Aiming at the uncertainty of structural parameters of these composite materials, the composite material vibration simulation and multivariate statistical analysis software are developed to simulate the structural vibration of composite materials. A multivariate statistical analysis method for natural frequency uncertainty of composite materials is presented. Through principal component …

An Intelligent Ambulance Regulation Model Based On Online Reinforcement Learning Algorithm, Lei Zhang, Xuechao Zhang, Chao Wang, Xianglei Bo

Journal of System Simulation

Abstract: In emergency scenarios where ambulances are used to evacuate casualties, it is necessary to fully coordinate the rescue capability of the ambulance with the real-time status of the casualties in the scenario to achieve the best rescue results. Such problems are generally non-deterministic polynomial problems, and the traditional deterministic scheduling algorithms are less effective. This paper aimed at the modeling research of the real-time regulation of ambulances in emergency scenarios, an online reinforcement learning DNQ algorithm frameworks based on the data enhancement method is proposed and applied to the solution of the ambulances control model. To solve the problems …

Research On Pedestrian Avoidance Strategy For Agv Based On Deep Reinforcement Learning, He Wang, Jianing Xu, Guangyu Yan

Journal of System Simulation

Abstract: To ensure the safety and comfort of pedestrians during Automated Guided Vehicle (AGV) obstacle avoidance in smart factory environments, a deep reinforcement learning-based end-to-end obstacle avoidance method is proposed. The YOLOv8 module is introduced to extract pedestrian pose information, and a visual-based state space is designed. A reinforcement learning mechanism is formulated based on personal space theory, penalizing AGV behaviors such as entering pedestrian comfort space and collisions. A virtual simulation system is constructed, utilizing PPO algorithm along with LSTM network layer for obstacle avoidance strategy training and simulation experiments. Simulation results indicate that this obstacle avoidance strategy, under …

City Regional Traffic Flow Prediction Based On Spatiotemporal Multi-View Attention Residual Network, Jing Chen, Guowei Yang, Zhaochong Zhang, Wei Wang

Journal of System Simulation

Abstract: However, efficiently and comprehensively capturing the complex spatiotemporal correlations within urban traffic flow presents a key challenge. Existing research methods struggle to fully capture these spatiotemporal dependencies. To address these issues, we propose a novel end-to-end deep learning framework called the spatiotemporal multi-view attention residual network (ST-MVAR) for predicting traffic flow in urban areas. we integrate the proximity, periodicity, trend, and external factors of traffic flow as inputs to the network. This network employs skip connections to form a multi-layer nested residual network structure. Additionally, we design a Multi-View Extension module to capture spatial dependencies of traffic flow at …

Parallel Task Transmission And Processing Optimization Scheme For Uav-Assisted Internet Of Vehicles, Chao Yang, Ruiqun Zheng, Zhen Li, Hongwei Zhang, Yanqun Tang, Dongze Li

Journal of System Simulation

Abstract: To address the increasing of computation demands of internet of vehicles (IoV) users due to the sudden traffic congestion, unmanned aerial vehicles (UAVs) are introduced to the intelligent transportation systems (ITS) to construct an UAV-assisted IoV network. The UAV limited energy and computing resources lead to the current traditional UAV coverage strategy with one by one less efficiency. We propose a parallel task transmission and processing optimization strategy, considering the line-of-sight communication links and fast moving characteristics of UAV. After receiving the tasks from vehicles in the service point, UAV can fly to the next point and perform task …

Three-Way Decision Clustering Algorithm Fusion Of Mutant Fireflies Algorithm, Zhaobin Li, Jun Ye, Haoyan Zhou, Yixin Wang, Yuzhen Han

Journal of System Simulation

Abstract: To address problems such as the premature phenomenon in the three-way clustering algorithm caused by the random selection of initial cluster centers and the need for repeated experiments to determine the value of q in the q-nearest neighbor concept, a three-way clustering algorithm optimized by a variant of the firefly algorithm is proposed. The firefly algorithm is employed to solve the problem of sensitivity to initial cluster centers. The target function value is taken as the brightness intensity of firefly to search the clustering center point, and the optimal solution is taken as the clustering center of the algorithm …

Research On Improved A* Algorithm Path Planning Based On Global Key Point Extraction, Guijuan Lin, Zihan Li, Yu Wang

Journal of System Simulation

Abstract: To address the limitations of the traditional A* algorithm in large and complex scenes, including traversing a large number of nodes, long computation times, and susceptibility to U-shaped traps, this paper proposes an improved A* algorithm incorporating the jump point search (JPS) concept and image processing techniques to extract key points from the global map. The proposed method preprocesses the global map to identify corner points located one grid diagonally from obstacles, constructs a key point list, and replaces the nodes traditionally traversed by the A* algorithm with these global key points, significantly reducing computational overhead. The neighbor nodes …

Real-Time Nonlinear Economic Model Predictive Control Of Wind Energy Conversion System, Wenwen Wang, Xiangjie Liu, Xiaobing Kong

Journal of System Simulation

Abstract: To address the new challenges of economic control and real-time requirements in wind energy conversion systems (WECS), this study proposes a nonlinear economic model predictive control (NEMPC) strategy. This strategy aims to maximize power generation and while reducing fatigue loads on critical structures, such as towers and gearboxes. Additionally, a moving horizon estimator (MHE) has been designed to provide an effective initialization for optimization. By exploiting the similarity of nonlinear programs between adjacent sampling moments, the algorithm achieves real-time iterative (RTI) solutions. Using a 5 MW wind turbine as the research object, the proposed strategy is implemented in the …

Tohf: A Feature Extractor For Resource-Constrained Indoor Vslam, Ruoqing Li, Yaochi Zhao, Zhuhua Hu, Wenlu Qi, Guangfeng Liu

Journal of System Simulation

Abstract: To address the issues of sensitivity to texture and lighting variations, excessive local dependence caused by feature point redundancy, and storage overhead under hardware resource constraints in existing VSLAM feature extractors in indoor environments, We propose the Texture- Oriented and Homogenized FAST Feature Extractor (TOHF), which integrates HVS (Human Visual System) for enhanced texture analysis. TOHF employs a two-stage thresholding strategy and dynamically adjusts feature point distribution, balancing computational efficiency and storage needs. We conducted experimental verification based on the ORB-SLAM3 framework on dataset from resource-limited device and the EuRoc dataset, focusing on matching rate, reprojection error, absolute trajectory …

Design And Verification Of Display And Control System Based On Mbse And Vaps For Civil Helicopter, Xi Cao, Bo Liu, Bingzhi Su, Tao Nie

Journal of System Simulation

Abstract: Aiming at the challenges of difficulties in tracing requirements, detecting interaction design defects, and achieving early system design verification, this paper proposes a design and verification for the display and control system (DCS) of civil helicopters based on model-based systems engineering (MBSE) and VAPS. The method begins with capturing stakeholder requirements to form system requirements, followed by the allocation of these requirements to system use cases. Black-box activity diagrams and sequence diagrams are constructed to conduct "requirement-function analysis" from the top down, describing the functional flow of the DCS. A running black-box statechart diagram is further established to verify …

Cae Simulation Optimization Method Based On Dynamic Coupling Model, Xue Chen, Jianwen Cao

Journal of System Simulation

Abstract: In order to solve the optimization problem of designing complex equipment under multi-factor coupling scene, a CAE simulation optimization method based on dynamic coupling model and multibranch parallel inference strategy is proposed. The dynamic hierarchical DEVS model is used to construct the automatic coupling model from pre-processing, numerical solution and post-processing phases of CAE software adaptively. Aiming at the key parameters of CAE model, multi-branch instance models with multi-factor constraints are constructed based on greedy algorithm. The multi-task parallel inference strategy is used to compute the multi-branch simulation results efficiently. The scheme optimization is realized based on the evaluation …

Visual Slam Algorithm Based On Feature Point Selection In Dynamic Scenes, Limei Jiang, Xinwei Chen

Journal of System Simulation

Abstract: To address low positioning accuracy and robustness in traditional visual SLAM algorithms under dynamic conditions, this paper proposes an improved dynamic SLAM algorithm based on feature point selection. Built upon the ORB-SLAM3 framework, it incorporates dynamic region partitioning and feature point filtering. The dynamic region partitioning module utilizes an enhanced RT-DETR object detection algorithm to detect dynamic objects in the images and divides the dynamic regions based on the detection boxes. The feature point selection module utilizes epipolar constraints and optical flow methods to filter out feature points on moving objects, retaining stationary dynamic objects and background points within …

Reinforcement Learning Modeling Of Missile Penetration Decision Based On Combat Simulation, Bin Zhang, Yonglin Lei, Qun Li, Yuan Gao, Yong Chen, Jiajun Zhu, Chenlong Bao

Journal of System Simulation

Abstract: Penetration capability is a primary measure of missile systems. In response to the shortcomings of traditional knowledge-based decision-making methods that are difficult to adaptively evolve, an intelligent penetration decision-making based on combat simulation and DRL is proposed. A missile intelligent decision-making training environment is constructed based on the WESS system. Taking missile maneuver penetration decision-making as an example, a maneuver penetration decisionmaking network model is designed and trained based on the SAC-discrete algorithm and the test of intelligence is conducted. Experimental results show that the intelligent decision model derived from machine learning has a better combat outcome than traditional …

Research On Air Target Threat Assessment Technology Based On Deep Learning, Dawei Jiang, Yangyang Dong, Lidong Zhang, Xiao Lu, Chunxi Dong

Journal of System Simulation

Abstract: In order to realize the effective assessment of air combat targets, a deep learning-based air target threat assessment method is proposed. According to threat characteristics of the air target, the threat attributes of air target faced by electronic countermeasure operation are analyzed from the two perspectives of platform layer and equipment layer, the air target threat assessment index system is constructed, and the air target threat assessment index data set is established. Based on convolutional neural network, a residual structure is introduced to optimize the network, a threat assessment model is established, and the threat ranking of air targets …

Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning, Xu Jin, Yuanbin Mo

Journal of System Simulation

Abstract: Aiming at the problems of local optimum and premature convergence in the design of optimization path of robot navigation system, a multi-strategy hybrid MGO(HMGO) improved algorithm based on the mountain gazelle optimizer(MGO) is proposed. The algorithm uses the quasi-reverse learning strategy to optimize the population initialization ensuring its diversity, introduces the dynamic adaptive density factor to adjust the parameters of the optimization mechanism, and integrates arithmetic optimization and sine-cosine strategies for random perturbations. Through ablation experiments, 13 benchmark test functions, and simulation experiments on the solution of two-dimensional and threedimensional space robot path planning problems, the results demonstrate that …

Gait Characteristics In People With Friedreich Ataxia: Daily Life Versus Clinic Measures, Hannah L. Casey, Vrutangkumar V. Shah, Daniel Muzyka, James Mcnames, Mahmoud El-Gohary, Kristen Sowalsky, Delaram Safarpour, Patricia Carlson-Kuhta, Christian Rummey, Fay B. Horak, Christopher M. Gomez

Electrical and Computer Engineering Faculty Publications and Presentations

Gait assessments in a clinical setting may not accurately reflect mobility in everyday life. To better understand gait during daily life, we compared measures that discriminated Friedreich ataxia (FRDA) from healthy control (HC) subjects in prescribed clinic tests and free, daily-life monitoring.MethodsWe recruited 9 people with FRDA (median age: 20, IQR [12, 48] years). A comparative healthy control (HC) subject cohort of 9 was sampled using propensity matching on age (median age: 18 [13, 22] years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory during a 2-min walk at a natural pace, followed by …

An Experimental And Numerical Investigation On The Flexural Behavior Of Two-Way Slabs Reinforced By Hybrid Reinforcement Schemes, Mahmoud A. Zidan, Mohamed H. Makhlouf, A. S. Debaiky, Gamal I. K.

Mansoura Engineering Journal

This study examines the Characteristics of flexural behavior in a two-way slab reinforced with a hybrid combination of Glass Fiber Reinforced Polymer. (GFRP) and steel reinforcement. It determines the effectiveness of hybrid reinforcement and fiber addition in enhancing the flexural resistance of two-way reinforced concrete slabs. Researchers tested twelve reinforced concrete slabs and divided them into four groups. There were two control specimens: one steel-reinforced and one glass fiber-reinforced polymer (GFRP)-reinforced, for comparison with the hybrid specimens. The remaining specimens were reinforced using a combination of steel bars and GFRP bars, The study focused on three-parameter categories: reinforcement type (steel …

Solving A Multi-Stage Mine Sequencing Model Encompassing Excavator Allocation Through Constraint Programming In A Bauxite Strip Mining Operation, Jorge Luiz Valença Mariz, Marcel Antonio Arcari Bassani, Rodrigo De Lemos Peroni, Octávio R. A. Guimarães, Flávio H. Tavares

Journal of Sustainable Mining

Sedimentary mineral deposits are generally tabular and sub-horizontal, composed of one or multiple well-defined seams, arranged over large extensions, and when close to the surface, they are mined by surface mining methods. These geometric characteristics make it suitable for the application of the strip mining method as an alternative to the traditional open-pit mining method, which allows the reduction of the truck haulage fleet and the costs associated with waste disposal. Consequently, it reduces the greenhouse gas emissions and carbon footprint associated with excavation and transportation processes. This article presents a two-stage mathematical model based on Integer Linear Programming to …

A Gaze-Driven Manufacturing Assembly Assistant System With Integrated Step Recognition, Repetition Analysis, And Real-Time Feedback, Haodong Chen, Niloofar Zendehdel, Ming C. Leu, Zhaozheng Yin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Modern manufacturing faces significant challenges, including efficiency bottlenecks and high error rates in manual assembly operations. To address these challenges, we implement artificial intelligence (AI) and propose a gaze-driven assembly assistant system that leverages artificial intelligence for human-centered smart manufacturing. Our system processes video inputs of assembly activities using a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for assembly step recognition, a Transformer network for repetitive action counting, and a gaze tracker for eye gaze estimation. The application of AI integrates the outputs of these tasks to deliver real-time visual assistance through a software interface that displays …

Neurosymbolic Knowledge-Grounded Planning And Reasoning In Ai Systems, Amit Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty

Faculty Publications

To build AI systems capable of decision-support assistance, such as AI-assisted healthcare, it is essential to develop user-centric decision-making processes that are robust, interpretable, and capable of effectively processing and acting on natural language interactions. Instruction-based prompting of large language models has demonstrated considerable success in supporting humans with information assistance tasks, including creative writing and content generation. However, recent studies reveal that language models exhibit limitations in performing complex reasoning and planning tasks, such as constructing compositional or hierarchical plans involving multiple reasoning steps. To address these challenges, we propose a neurosymbolic framework that integrates large language models with …

Multi-Model Integration For Dynamic Forecasting (Midf): A Framework For Wind Speed And Direction Prediction, Molaka Maruthi, Bubryur Kim, Sujeen Song, Jinwoo An, Zengshun Chen

Civil Engineering Faculty Publications

Accurate forecasting of wind speed and direction is critical for the efficient integration of wind power into energy systems, ensuring reliable renewable energy production and grid stability. Traditional methods often struggle with capturing nonlinear interdependencies, quantifying uncertainties, and providing reliable long-term predictions, particularly in complex atmospheric conditions. To address these challenges, this study introduces multi-model Integration for dynamic forecasting (MIDF), an ensemble machine learning framework that combines the strengths of DeepAR and temporal fusion transformer (TFT) models through a two-step meta-learning process. MIDF leverages DeepAR’s probabilistic forecasting capabilities and TFT’s attention mechanisms to enhance accuracy, robustness, and interpretability. Using a …

Exploring The Effect Of The Architecture Morphology On Urban Ventilation At Block Scale Using Cfd-Gis And Random Forest Combined Method, Bin Guo, Miaoyi Chen, Xiaowei Zhu, Zheng Wang, Lu Li, Lin Pei, Hailong Chen, Puhao Chen, Tengyue Guo

Mechanical and Materials Engineering Faculty Publications and Presentations

Urban ventilation plays a crucial role in dispersing air pollutants and mitigating the urban heat island effect. As a key factor, urban architectural morphology can significantly impact the wind field and ventilation efficiency. This study combines Computational Fluid Dynamics (CFD), Geographic Information System (GIS), and Random Forest (RF) methods to investigate the influence of architectural morphology on urban ventilation at the block scale. First, Remote Sensing (RS) and GIS were used to extract architectural morphology parameters. Second, CFD simulations, guided by in-situ observations, were conducted to model the wind field, with the Standard k-ɛ model validated as the optimal choice. …

The Spice (Sustainable, Physics-Inspired Culinary Education) Lab – A Digestible Few-Nexus Educational Platform, Carla Ramsdell

National Collaborative for Research on Food, Energy, and Water Education (NC-FEW)

The SPICE (Sustainable, Physics-Inspired Culinary Education) Lab is a unique educational platform that enables participants from a wide disciplinary background to engage and become literate in the unique connections between food, energy and water. This small but mighty space allows for outreach education in many formats, including formal college courses, university-centered hands-on activities and public outreach.

The work performed in this space fills a unique need in the FEW-Nexus education opportunities because it is adaptable to many learners and learning opportunities and its focus on kitchen science makes these concepts approachable which can later be expanded to understand other critical …

Physical And Biological Effects On Moths’ Navigation Performance, Yiftach Golov, Roi Gurka, Alexander Liberzon, Ally Harari

Physics and Engineering Science

In a chemosensing system, the local olfactory environment experienced by a foraging organism is defined as an odorscape. Using the nocturnal pink bollworm moth (Pectinophora gossypiella), we tested the combined effect of three biophysical aspects in its immediate odorscape to shed light on the coupling effects of biotic and abiotic factors on navigation performances of a nocturnal forager: i) the quality of the pheromone source, ii) the pheromone availability, and iii) the airflow characteristics. The navigation performance of the males was investigated using a wind tunnel assay equipped with 3D infrared high-speed cameras. The navigation performance of the males was …

Distributed Repeaters For Extending The Range Of Harmonic Rf Tags, Trevor Saunders

USF Tampa Graduate Theses and Dissertations

Passive harmonic Radio Frequency (RF) tags are highly advantageous compared to theiractive counterparts due to their ability to operate effectively in environments that are cluttered or where access to the tag for battery maintenance is challenging, dangerous and/or time consuming. They are also resistant to the local oscillator (LO) phase noise in radar applications where the doppler frequency from slow moving targets is buried beneath the noise. One of the major constraints of passive harmonic RF tags, however, lies in their operating range and the significant amount of power required to overcome the conversion loss and activate the nonlinear device …