Material Reconstruction From Single Image Combining Neural Networks With Singular Value Decomposition,
2026
National Engineering Laboratory for Modeling and Emulation in E-Government, Harbin Engineering University, Harbin 150001, China
Material Reconstruction From Single Image Combining Neural Networks With Singular Value Decomposition, Zhiqiang Li, Xukun Shen, Yong Hu, Xueyang Zhou, Yifan Chen
Journal of System Simulation
Abstract: The tabulated BRDFs (bidirectional reflectance distribution function) can realistically reproduce the surface appearance of objects. However, due to their high-dimensional characteristics and the fact that a single planar image contains limited reflectance information and small differences, methods for estimating tabulated BRDFs typically require complex equipment or the capture of multiple images. To address this issue, a method is proposed for reconstructing material properties from a single image by combining neural networks with singular value decomposition. The singular value decomposition is introduced to compress the material into a lower-dimensional space. The task of solving the tabulated BRDFs is simplified to …
Full-Body Co-Speech Gesture Generation Based On Spatial-Temporal Enhanced Generation Model,
2026
Beijing Information Science & Technology University, Beijing 102206, China
Full-Body Co-Speech Gesture Generation Based On Spatial-Temporal Enhanced Generation Model, Shuozhe Zhang, Wenfeng Song, Xia Hou, Shuai Li
Journal of System Simulation
Abstract: Full-body co-speech gesture generation significantly enhances the interactivity of virtual digital humans, requiring generated gestures to not only align accurately with speech but also demonstrate realistic full-body dynamics. To address limitations of existing methods—Transformer-based approaches often overlook temporal features of action sequences, while diffusion model-based ones inadequately capture spatial correlations between body parts, a full-body action generation method integrating diffusion models, Mamba, and attention mechanisms is proposed. We introduce the spatial self-attention-temporal state space model (STMamba Layer) as the core of denoising network to extract
inter-part spatial features and intra-part temporal features, thus enhancing action quality and diversity. …
Vrbt: Vr Badminton Training With Multitask Injury Alerts Based On Lightweight 3d Skeletal Reconstruction,
2026
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Vrbt: Vr Badminton Training With Multitask Injury Alerts Based On Lightweight 3d Skeletal Reconstruction, Yuning Zhu, Meng Yang, Tianyue Chen, Weiliang Meng
Journal of System Simulation
Abstract: To overcome the limitations of traditional badminton training, a VR training method that integrates multiple models for collaborative simulation is proposed. A "perception-decision- interaction" framework is developed within Unity, featuring diverse training modules powered by a physics engine for realistic trajectory simulation. The system employs a lightweight MHFormer for 3D pose estimation and a novel multi-task model (enhanced injury prediction system, EIPS) that combines random forest and XGBoost to jointly assess injury risk. This approach offers a solution for balancing real-time performance with accuracy in skeleton reconstruction and enables personalized training through dynamic risk assessment.
Military Metaverse: Conceptual Connotation, Construction And Application Framework, Key Issues,
2026
Army Arms University of PLA, Beijing 100072, China; PLA 32302 Troops
Military Metaverse: Conceptual Connotation, Construction And Application Framework, Key Issues, Dayong Liu, Zhiming Dong, Jiancheng Gao
Journal of System Simulation
Abstract: Based on the analysis of the concept of the metaverse, the military metaverse concept model is established and compared with virtual-real fusion systems such as the digital twin battlefield, analyzing its core characteristics and construction significance. To accelerate the construction of the military metaverse, an overall logical architecture for the construction and application of the military metaverse is designed, the concept of military metaverse primitives is proposed, and the technical architecture is designed. The main application directions of the military metaverse are analyzed, and the construction stage division and overall thinking are provided. The key issues in construction and …
Virtual-Real Fusion Simulation Technology And Application Research For Industrial Control Systems Cybersecurity Of Process Manufacturing,
2026
College of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China; Key Laboratory of Oil and Gas Production Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
Virtual-Real Fusion Simulation Technology And Application Research For Industrial Control Systems Cybersecurity Of Process Manufacturing, Xinwei Wang, Jinjiang Wang, Zheng Wang, Laibin Zhang
Journal of System Simulation
Abstract: Aiming at the problem that the industrial control system in the process manufacturing industry lacks an effective attack and defense drill platform when facing network attacks, it is difficult to truly simulate the attack situation, verify the protective measures, and accurately evaluate the impact of attacks on the physical system, an industrial control cybersecurity simulation technology based on virtual-real fusion is proposed to build an efficient attack and defense drill range. The industrial control cybersecurity simulation architecture based on virtual-real fusion is designed, and the consistency analysis of virtual-real fusion data is carried out. At the same time, …
Spatio-Temporal Swin Transformer-Based Flow-Solid Coupling Interaction Sequence Image Prediction Network,
2026
School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China
Spatio-Temporal Swin Transformer-Based Flow-Solid Coupling Interaction Sequence Image Prediction Network, Changjun Zou, Zhiyu Ge, Chenxi Zhong
Journal of System Simulation
Abstract: To address limitations in modeling long-term dependencies and multi-scale features in fluidstructure interaction scenarios, a spatiotemporal deep learning model (SwinLSTM) integrating ConvLSTM and Swin Transformer is proposed. The model employs a gated spatiotemporal attention mechanism that dynamically embeds Swin Transformer's window-based multi-head self-attention into ConvLSTM's output gate, enabling adaptive temporal-spatial feature coupling, and designs a multi-level ConvLSTM framework to hierarchically capture complex spatiotemporal correlations. Experiments on a self-built fluid-interaction dataset show that our method achieves the highest PSNR and leading SSIM scores, with superior performance in preserving vortex details and boundary consistency. This work provides an efficient solution …
Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion,
2026
North China University of Technology, Beijing 100044, China
Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion, He Zhu, Feng Zhou, Mengxiao Zhu, Ju Dai
Journal of System Simulation
Abstract: To enhance the semantic discrimination capability in point cloud semantic segmentation, a 3D point cloud semantic segmentation network named PL-Mamba is proposed, which is centered on the fusion of point cloud (P) and language (L) dual modalities. This method takes PointMamba as the backbone network, leveraging its excellent long-sequence modeling and global perception capabilities. It introduces a language prompt mechanism and uses a pretrained language model BERT to encode the context of category labels, obtaining semantically rich text features. The text information serves as a language guided token and is deeply integrated with point cloud features through cross modal …
Dehpr: A Diffusion-Based End-To-End Hand Pose Reconstruction Network,
2026
Modern Industry School of Virtual Reality (VR), Jiangxi University of Finance and Economics, Nanchang 330032, China; Jiangxi Tourism and Commerce Vocational College, Nanchang 330100, China
Dehpr: A Diffusion-Based End-To-End Hand Pose Reconstruction Network, Guoqiong Liao, Longjie Huang, Qingxin Li, Jiajun Zhang, Kefan Chen
Journal of System Simulation
Abstract: Traditional methods such as convolutional neural networks (CNNs) and Transformers suffer from strong dependence on large-scale annotated data and limited generalization capability when dealing with hand pose reconstruction in complex scenarios. To address these issues, a diffusion-based end-to-end hand pose reconstruction network (DEHPR) is proposed. This method employs a diffusion model to directly generate and refine 3D predictions, thereby reducing spatial uncertainties inherent in 2D-to-3D modeling paradigms. By incorporating an end-to-end framework that reprojects multiple 3D candidate predictions to select optimal joint positions, the approach ultimately produces accurate hand pose estimations. Comprehensive evaluations conducted on HO3D V2, DexYCB, …
Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement,
2026
School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102616, China; Beijing Key
Laboratory of Super Intelligent Technology for Urban Architecture, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement, De Zhang, Zishan Liang, Ningning Liu
Journal of System Simulation
Abstract: Crowd counting takes video surveillance data as input and can be applied to the construction of city digital twin platforms, virtual city modeling and smart city management, etc. However, when there are data domain differences between the application scenario and training scenario, counting performance often significantly decreases. A cross-domain crowd counting model based on frequency domain enhancement is proposed. To alleviate the distribution differences between domains, a frequency domain feature enhancement module and a domain invariant frequency domain adapter module are constructed: the former uses discrete cosine transform to extract key statistical features to enhance spatial representation ability, while …
Research On Real-Time Animatable Human Avatar Generation Via 3d Gaussian Splatting,
2026
School of Computing, Beihang University, Beijing 100191, China
Research On Real-Time Animatable Human Avatar Generation Via 3d Gaussian Splatting, Yuyou Zhong, Xukun Shen, Yong Hu
Journal of System Simulation
Abstract: Real-time animatable 3D human avatar generation technology hold significant application value in fields such as virtual reality and remote collaboration. To address the limitations of existing methods in detail modeling, real-time performance, and robustness under novel pose driving, an efficient human avatar generation and driving method based on 3D Gaussian splatting (3DGS) is proposed. This method integrates optimized parametric human reconstruction, tri-plane feature encoding, and dynamic offset prediction to achieve efficient modeling from monocular video input. By introducing a skeleton binding and visibility analysis strategy, while designing a multi-scale regularization loss to address the overfitting problem. Simulation experiments demonstrate …
Inverse Kinematics 3d Human Modeling Simulation Based On Multi-View Vision,
2026
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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 …
Optimal Placement Of Electric Vehicle Chargers: A Mixed-Integer Linear Programming Model,
2026
University of New Hampshire, Durham
Optimal Placement Of Electric Vehicle Chargers: A Mixed-Integer Linear Programming Model, Joubin Zahiri Khameneh, Emmanuel Fagbenle
Faculty Publications
Electric vehicle adoption is growing, but New Hampshire lags in public charging infrastructure, especially in rural areas. This gap increases range anxiety and economic inefficiencies. In this study, we developed a mixed-integer linear programming (MILP) model to optimally locate new electric vehicle chargers statewide, maximizing coverage and equity under budget constraints. The model includes geographic coverage requirements, population-weighted equity, capacity limits, and a $28 million budget. Moreover, the model recommends 855 Level 2 chargers and 149 Direct Current Fast Chargers (DCFCs) across 247 ZIP Codes, nearly doubling public charging capacity and achieving 98.8% coverage within defined service radii. The plan …
A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?,
2026
Old Dominion University
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 …
Work-In-Progress: Evaluating Feasibility Of Band Matrix Solvers For Scaling Up Extreme Learning Machine Method,
2026
Missouri University of Science and Technology
Work-In-Progress: Evaluating Feasibility Of Band Matrix Solvers For Scaling Up Extreme Learning Machine Method, Anton Akusok, Kaj Mikael Björk, Amaury Lendasse, Leonardo Espinosa Leal
Engineering Management and Systems Engineering Faculty Research & Creative Works
This work presents the results of the potential of band linear system solvers for improving the scalability of the Extreme Learning Machine (ELM) method at large model sizes. The model is tested on the standard MNIST dataset with a range of solvers provided by the SciPy Python library. The results are analyzed taking into consideration the overall performance and the performance impact of band solvers across different matrix bandwidths, as well as the performance versus runtime analysis. The findings show potential in applying the proposed method to very large ELM models with narrow band matrices.
Part Ii: Industrial Information Integration Review 2020-2025,
2026
Old Dominion University
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,
2026
Old Dominion University
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 …
Optimal Slotting In Hybrid Warehousing For Industry 4.0,
2026
Missouri University of Science and Technology
Optimal Slotting In Hybrid Warehousing For Industry 4.0, Teng Yang
Masters Theses
In the era of Industry 4.0, the warehouse management system (WMS) employed by many firms prescribes hybrid storage, i.e., products with high turnover, called fast movers, are kept in random storage for a short time duration before being shifted to a dedicated storage area, while products with low turnover, called slow movers, remain in random storage. From dedicated storage, the products are dispatched to the customer. The challenge for managers is selecting the slot in dedicated storage to assign to each product while demand data change because of fluctuating market conditions; this problem is referred to as slotting in the …
Developing Discharge Estimation Algorithm Using Low-Cost Velocity Sensor And Machine Learning,
2026
Missouri University of Science and Technology
Developing Discharge Estimation Algorithm Using Low-Cost Velocity Sensor And Machine Learning, Barkha Gautam
Masters Theses
Accurate river discharge estimation is essential for flood forecasting, water resources management, and hydraulic decision-making; however, continuous discharge records are unavailable at many river locations. Traditional stage-discharge rating curves are widely used but their reliability may decrease when channel conditions change or flow conditions vary rapidly. This study develops and evaluates Long Short-Term Memory (LSTM) models for discharge prediction using 15-minute time-series data from river monitoring stations in Missouri. Two model configurations, a baseline stage-only model and an enhanced stage-plus-velocity model, are developed and evaluated independently at two river sites to determine whether the inclusion of surface velocity improves discharge …
Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave,
2026
Missouri University of Science and Technology
Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave, Sourav Bolar, Steven Corns, Nayan Pundhir, Kumbla Chandrashekhara
Engineering Management and Systems Engineering Faculty Research & Creative Works
Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation …
Business Process Redesign For Reducing Undelivered Product Return Losses In E-Commerce – An Explainable Ai Approach,
2026
Indian Institute of Management Tiruchirappalli, India
Business Process Redesign For Reducing Undelivered Product Return Losses In E-Commerce – An Explainable Ai Approach, Venkataraghavan Krishnaswamy, Deepa R, Himanshu Sharma
Journal of International Technology and Information Management
Product returns in e-commerce affect the profitability of the e-tailer. We adopt a two-stage approach to reduce undelivered product returns in an e-commerce firm. First, we develop and compare machine learning techniques—logistic regression, decision trees, Naïve Bayes, random forest, adaptive boosting, gradient boosting, stochastic gradient boosting, and deep neural networks—on their ability to predict undelivered returns. Next, we use explainable methods, such as relative importance and Shapley values, to develop insights from the best-performing machine learning model. Finally, we use these insights and the predictive model to redesign the firm’s order fulfillment and return processes. A Post-implementation evaluation of the …
