Operations Research, Systems Engineering and Industrial Engineering Commons^™

Inverse Kinematics 3d Human Modeling Simulation Based On Multi-View Vision, Guoyu Fang, Yanze Li, Kai Chen, Xiaodong Zhao, Zizhuo Hu, Mingshi Yang, Wanqing Wu, Zichen Wang, Wenkai Guo

Journal of System Simulation

Abstract: In autonomous driving simulation and industrial virtual reality simulation, there is a high demand for accuracy and robustness in 3D human body modeling. However, current joint-based human modeling approaches suffer from issues such as continuous modeling jitter, local distortion, and poor adaptability to occlusion, which degrade model quality and limit the development of practical applications such as intelligent driving and digital factories. To address these challenges, this paper proposes a multi-view vision-based inverse kinematics 3D human modeling method using a vector quantized variational autoencoder(IK-VQ-VAE). By integrating joint training with an automatic variational gradient descent approach, the proposed method achieves …

A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?, Joshit Mohanty

Engineering Management & Systems Engineering Faculty Publications

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within …

Part Ii: Industrial Information Integration Review 2020-2025, Jinzhi Li

Information Technology & Decision Sciences Faculty Publications

Industrial Information Integration Engineering (IIIE) has become increasingly essential for improving operational efficiency and harmonizing heterogeneous industrial systems through advanced digital integration approaches. Fueled by rapid advancements in Industry 4.0 technologies—including digital twins, artificial intelligence, immersive interfaces, and IoT infrastructures—IIIE is substantially transforming traditional enterprise architecture and integration frameworks. This systematic review synthesizes recent developments and emerging trends, with particular attention to the accelerating adoption of digital twins and the deepening convergence between operational technologies (OT) and information technologies (IT) across multiple sectors. While notable progress has been made, significant challenges persist, especially in developing resilient integration architectures and fully …

A Macrocognitive Design Taxonomy For Simulation-Based Training Systems: Bridging Cognitive Theory And Human-Computer Interaction, Jessica M. Johnson

Virginia Digital Maritime Center (VDMC) Faculty Publications

Simulation-based training systems are increasingly deployed to prepare learners for complex, safety-critical, and dynamic work environments. While advances in computing have enabled immersive and data-rich simulations, many systems remain optimized for procedural accuracy and surface-level task performance rather than the macrocognitive processes that underpin adaptive expertise. Macrocognition encompasses higher-order cognitive processes that are essential for performance transfer beyond controlled training conditions. When these processes are insufficiently supported, training systems risk fostering brittle strategies and negative training effects. This paper introduces a macrocognitive design taxonomy for simulation-based training systems derived from a large-scale meta-analysis examining the transfer of macrocognitive skills from …

Uncertainty Quantification, Propagation & Conjunction Assessment In Orbital Mechanics Using Generalized Polynomial Chaos Expansion & 2-Dimensional Conjunction Plane Analysis Techniques, Monalisa Karim

Mechanical and Aerospace Engineering Theses

Uncertainties, that are inherent to dynamic models, can be associated with state initial conditions, force modelling errors, navigation and actuation errors. In system modelling stochastic differential equations are used to represent dynamic phenomena with uncertainties, for which the solutions are probability density functions of quantities of interest characterizing the realization of the stochastic processes. In Polynomial Chaos Expansion (PCE) propagation, these solutions are represented as weighted sums of multivariate spectral polynomials that are functions of the input random variables. Generalized polynomial chaos expansion (gPC) is an extension to the original homogenous PCE which projects the random solution onto a basis …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

Toward Secure And Practical Machine Learning-Based Access Control: A Framework With Real-World Constraints And Adversarial Analysis, Olusesi Balogun, Mohammad Ghasemigol, Zhipeng Cai, Daniel Takabi

School of Cybersecurity Faculty Publications

Attribute-Based Access Control (ABAC) frameworks coordinate access requests based on subject, object, and environment attributes, as well as policy rules, and are widely used in corporate security systems. Recently, machine learning has been applied to ABAC to address policy-generation imbalances, misassigned privileges, and attribute leakages. However, existing MLBAC techniques do not consider the structural constraints and attribute interdependencies present in traditional ABAC systems. Moreover, these frameworks have not been extensively evaluated under black-box attack scenarios. To address these gaps, we propose extensions to MLBAC that integrate structural constraints, attribute dynamism, and attribute weighting into the MLBAC objective function. Additionally, we …

Generative Ai And Llm Applications In Renewable Energy And Smart Grids: A Systematic Review For The Sustainable Energy Transition, Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper, Ibrahim Kucukdemiral, Huseyin Kusetogullari, Murat Kuzlu, Yashar Mousavi, Sonam Norbu, Taha Selim Ustun, David Flynn

Engineering Technology Faculty Publications

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting …

Adaptive Electric Vehicle Routing And Charging With Deep Reinforcement Learning, Mandana Farhang Ghahfarokhi, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

Engineering Management & Systems Engineering Faculty Publications

As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate …

Seeing The Invisible Load: Xr+ Multimodal Sensing For Cognitive Ergonomics In Industrial Training, Jessica M. Johnson, Andwele Grant

Virginia Digital Maritime Center (VDMC) Faculty Publications

Extended reality (XR) technologies are increasingly positioned as disruptive Industry 5.0 tools for human-centric industrial training and intelligent human–system integration. Coupled with multimodal sensing (eye tracking, EEG, HRV, GSR, and other physiological signals), XR environments promise to make otherwise invisible cognitive demands observable, especially for novice trainees entering complex industrial settings. Yet the evidence base is fragmented: (1) there is no quantitative synthesis of the cognitive ergonomics benefits of XR plus sensing; (2) little is known about which XR–sensor configurations yield the strongest effects; (3) prior reviews rarely focus on industrial and manufacturing tasks; (4) multimodal signals are used predominantly …

Nanostructured Cathode Catalysts For Aem Electrolysis: From Catalyst Design To Degradation And Hydrogen Dynamics, Yamini Kumaran

Electronic Theses & Dissertations (2024 - present)

Anion exchange membrane water electrolysis (AEMWE) presents a promising pathway toward cost-effective and sustainable hydrogen production by integrating the chemical robustness of alkaline systems with the compact, zero-gap design of proton exchange membrane electrolyzers. However, the widespread implementation of AEMWE is limited by the availability of highly active and durable platinum-group-metal (PGM)-free catalysts and by an incomplete understanding of their degradation behavior under realistic operating conditions.

This dissertation focuses on the development, characterization, and mechanistic investigation of nanostructured MoNi₄–MoO₂-based electrodes for efficient and stable hydrogen generation under alkaline and membrane-integrated environments. MoNi₄–MoO₂ nanorods …

Optimizing Maintenance Routes For Highway Infrastructure Using Leader-Follower Autonomous Vehicles, Qing Tang, Chenxi Chen, Xianbiao Hu, Yuxin Ding, Tianjia Yang

Civil & Environmental Engineering Faculty Publications

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is …

Choice-Based Crowdshipping For Next-Day Delivery Services: A Dynamic Task Display Problem, Alp Arslan, Firat Kilci, Shih-Fen Cheng, Archan Misra

Research Collection School Of Computing and Information Systems

This paper studies integrating the crowd workforce into next-day home delivery services. In this setting, both crowd drivers and contract drivers collaborate in making deliveries. Crowd drivers have limited capacity and can choose not to deliver if the presented tasks do not align with their preferences. The central question addressed is: How can the platform minimize the total task fulfilment cost, which includes payouts to crowd drivers and additional payouts to contract drivers for delivering the unselected tasks by customizing task displays to crowd drivers? To tackle this problem, we formulate it as a finite-horizon Stochastic Decision Problem, capturing crowd …

Learning-Based Graph Shrinking For Quantum Optimization Of Constrained Combinatorial Problems, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Graph shrinking has recently emerged as a powerful preprocessing technique for hybrid classical–quantum optimization, enabling variable and constraint reduction before quantum solving. Conventional approaches rely on Semi-Definite Programming (SDP) relaxations to compute vertex correlations, but these methods suffer from high computational overhead, instance-specific tuning, and limited generalizability. In this work, we replace the handcrafted SDP correlation stage with a reinforcement learning (RL) based correlation estimator, trained to predict merge quality directly from graph structure. We reformulate the graph shrinking process as a Markov Decision Process (MDP), design a Graph Neural Network (GNN) policy to guide vertex merging, and integrate the …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

Hybrid Learning And Optimization Methods For Solving Capacitated Vehicle Routing Problem, Monit Sharma, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose a hybrid quantum–classical framework for the Capacitated Vehicle Routing Problem (CVRP) that integrates the Augmented Lagrangian Method (ALM) with deep reinforcement learning (RL). Directly solving CVRP via Variational Quantum Eigensolver (VQE) requires a slack-based QUBO formulation, where converting inequalities to equalities greatly increases the qubit count. To circumvent this, we employ an ALM-based reformulation that enforces constraints through Lagrange terms instead of slack variables, drastically reducing quantum resource demands. An RL agent, trained with Soft Actor–Critic, adaptively tunes the Lagrange penalties to improve convergence and feasibility. Experiments show that RL-Q-ALM outperforms static-penalty and plain VQE baselines in both …

(R2141) Analysis Of Map^I_1 , Ph^(Oa)_2 / Ph^I_1 , Ph^O_2 / 1 Retrial Inventory Queue With Two Way Communication, (S, S) Replenishment Policy, Feedback, Bernoulli Vacation And Impatient Customers, G. Ayyappan, V. Ganesan

Applications and Applied Mathematics: An International Journal (AAM)

This work discusses about the topic as the two-way communication retrial inventory queue model, the (s, S) replenishment policy, immediate feedback, Bernoulli vacations, and impatient customers. The assumption we make is that arrivals follow a Markovian arrival process, and the server provides phase type services. When the server is idle and there is a positive inventory, an arriving customer immediately receives service. If not, arriving customers goes to orbit with infinite capacity. Only in the account of positive inventory the server renders rapid feedback for incoming call arrivals, otherwise customer departs. Outgoing calls will only be made by the server …

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 …