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NUMERICAL FLOW VISUALIZATION: VISTA AND EXPEDITION

Journal of Flow Visualization and Image Processing 2022年第3期29卷 1-27页

作者： Liu, Zhanping Department of Computational Modeling and Simulation Engineering Old Dominion University ODU-CMSE 1300 ECSB 4700 Elkhorn Avenue NorfolkVA23529 United States

Numerical flow visualization resorts to data processing, feature extraction, geometric construc-tion, image synthesis, graphical display, and scene manipulation to enable intuitive insightful interactive analysis of tangential directions of movement embedded in a velocity vector field. This paper presents some advances in numerical flow visualization over the past three decades, with emphasis on geometry-based and texture-based methods. Also introduced in this context is our own research for representing, depicting, and exploring flow data. Not intended for an exhaustive survey of the literature, our discussions revolve around streamline integration, interactive streamline placement, automatic streamline placement (including evenly spaced layout of streamlines), streamline clustering, time-dependent flow lines (e.g., pathlines), flow texture synthesis (e.g., line integral convolution), feature mining (e.g., flow topology), and parallel vi-sualization. These topics address flows ranging from planar to surface and volumetric domains, from steady to unsteady cases, and from Cartesian to curvilinear and unstructured grids. This scope allows us to examine a variety of approaches in the working mechanism, numerical accu-racy, representation effectiveness, visual quality, and computational performance so as to show the respective advantages and disadvantages. The findings may help visualization researchers pinpoint main long-lasting challenges, visualization developers identify practically applicable techniques, and domain scientists perceive important promising advances in numerical flow visualization. © 2022 by Begell House, Inc.

关键词： Textures

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Detection and Classification of PCB Defects using Deep Learning Methods 31

Detection and Classification of PCB Defects using Deep Learn...

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31st IEEE Microelectronics Design and Test Symposium, MDTS 2022

作者： Legon, Andria Deo, Makarand Albin, Sacharia Audette, Michel Old Dominion University Department of Computational Modeling and Simulation Engineering NorfolkVA United States Norfolk State University Department of Engineering NorfolkVA United States

ISBN: (纸本)9781665468169

Automatic inspection methods are needed to cost-effectively discover defects in fabricated printed circuit boards (PCBs). This paper adapts a deep convolutional neural network design, called RetinaNet, to simultaneously locate and classify defects in PCBs. To improve detection performance, several network designs with regularization and dropout hyperparameters were tested. Not all defects render a PCB nonfunctional. This paper proposes re-evaluating network performance downweighing cosmetic defects. Adding a 25% dropout rate to each layer of the ResNet backbone, achieved an impressive 95.03% accuracy and 97.09% F-score on PCB images from a publicly available dataset. The study demonstrates the effectiveness of designing a single deep learning network to reliably and accurately detect as well as classify defects in PCB images. Our design can be extended to identification of defects in microelectronic circuits and semiconductor circuits with suitable modifications. © 2022 IEEE.

关键词： Printed circuit boards

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Runtime Energy Savings Based on Machine Learning Models for Multicore Applications

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Journal of Computer and Communications 2022年第6期10卷 63-80页

作者： Vaibhav Sundriyal Masha Sosonkina Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk USA

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.

关键词： Machine Learning RAPL DVFS Uncore Frequency Scaling Energy Savings Performance modeling

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Definition and Detection of Hypervulnerabilities using a Framework for Assessing Port Resilience 24

Definition and Detection of Hypervulnerabilities using a Fra...

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24th International Conference on Harbor, Maritime and Multimodal Logistics Modelling and simulation, HMS 2022

作者： Smith, Katherine Diaz, Rafael Shen, Yuzhong Longo, Francesco Virginia Modeling Analysis & Simulation Center Old Dominion University 1030 University Blvd SuffolkVA23435 United States Department of Computational Modeling and Simulation Engineering Old Dominion University 1300 Engineering & Computational Sciences Building NorfolkVA23529 United States Mechanical Department University of Calabria Via Pietro Bucci Arcavacata CS Rende87036 Italy

ISBN: (纸本)9788885741751

Long term plans for maritime ports are identifying investments that will increase their capacity while decreasing their environmental footprint and operating costs. These changes are leading to increases in complexity at a time when leaner practices are driving investments to become more strategic. As such, this work proposes a generalized definition that allows a system or entity to be classified as exceedingly vulnerable by comparing it to other entities. This definition is developed from a set of definitions gathered from disparate fields. From this definition grounded in theory, the initial rules for complex system implementation are developed and demonstrated on both a small conceptual example and a port example. Finally, conclusions and directions for future work are provided. © 2022 The Authors.

关键词： Investments

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Structural constitutive models for soft biological tissues and biomaterials: the role of mechanical interactions

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Mechanics of Soft Materials 2025年第1期7卷 1-39页

作者： Motiwale, Shruti Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences Austin USA The Department of Mechanical Engineering The University of Texas at Austin Austin USA The Department of Biomedical Engineering The University of Texas at Austin Austin USA

In this review, we explore the development of the structural constitutive models for soft biological tissues and biomaterials, focusing on the role of mechanical interactions. Soft tissues, such as myocardium, heart valves, skin, and other fibrous biological structures, display complex, non-linear mechanical behaviors driven by their fibrous composition. While traditional constitutive models have been widely adopted to describe the anisotropic and hyperelastic nature of these tissues, they often fall short in capturing the detailed mechanical interactions between fibers, the ground matrix, and other tissue components under diverse loading conditions. To address these gaps, we focus on the development of meso-structural models that account for mechanism-driven fiber-matrix and fiber-fiber interactions. We delve into the kinematics of these interactions, highlighting the roles of fiber rotation, extension, and shear, and how these mechanisms differ across various tissues and biomaterials. Our review draws on experimental evidence from studies on exogenously crosslinked tissues, myocardium and electrospun polycarbonate urethane biomaterials, which reveal the presence of significant mechanical interactions that influence the overall stress response. In addition, we also discuss recent advancements in computational methods for implementing these models, particularly neural network-based approaches that mitigate the computational challenges typically associated with structural models while maintaining high predictive accuracy. Finally, we propose future directions for multiscale modeling to enhance our understanding of fiber network topologies and their mechanical roles in both native tissues and biomaterials.

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From Narratives to Conceptual Models via Natural Language Processing 22

From Narratives to Conceptual Models via Natural Language Pr...

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Proceedings of the Winter simulation Conference

作者： David Shuttleworth Jose Padilla Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk VA USA Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

This paper explores the use of natural language processing (NLP) towards the semi-automatic generation of conceptual models, and eventual simulation specifications, from descriptions of a phenomenon. Narratives describing the problem are transformed into a list of concepts and relationships and visualized using a network graph. The process relies on pattern-based grammatical rules and an NLP dependency parser identifying important concept types, namely actors, factors, and mechanisms. We use three conceptualizations, created by potential users, to understand how the NLP-generated model should and could be adjusted. The objective of the research is to develop potential standard approaches users can use to generate conceptual models; develop a conceptual modeling assistant that subject matter experts can use to make them participant in the simulation creation process; and to identify how narratives should be written so an NLP-based conceptual modeling assistant may provide a thorough description of a phenomenon.

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Real-Time Vehicle Detection And Urban Traffic Behavior Analysis Based On Uav Traffic Videos On Mobile Devices

arXiv

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arXiv 2024年

作者： Zhu, Yuan Wang, Yanqiang An, Yadong Yang, Hong Pan, Yiming Inner Mongolia Center for Transportation Research Inner Mongolia University Hohhot China Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk United States

This paper focuses on a real-time vehicle detection and urban traffic behavior analysis system based on Unmanned Aerial Vehicle (UAV) traffic video. By using UAV to collect traffic data and combining the YOLOv8 model and SORT tracking algorithm, the object detection and tracking functions are implemented on the iOS mobile platform. For the problem of traffic data acquisition and analysis, the dynamic computing method is used to process the performance in real time and calculate the micro and macro traffic parameters of the vehicles, and real-time traffic behavior analysis is conducted and visualized. The experiment results reveals that the vehicle object detection can reach 98.27% precision rate and 87.93% recall rate, and the real-time processing capacity is stable at 30 frames per seconds. This work integrates drone technology, iOS development, and deep learning techniques to integrate traffic video acquisition, object detection, object tracking, and traffic behavior analysis functions on mobile devices. It provides new possibilities for lightweight traffic information collection and data analysis, and offers innovative solutions to improve the efficiency of analyzing road traffic conditions and addressing transportation issues for transportation authorities. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Development of a framework to support informed shipbuilding based on supply chain disruptions

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Procedia Computer Science 2022年 200卷 1093-1102页

作者： Katherine Smith Rafael Diaz Yuzhong Shen Virginia Modeling Analysis Simulation Center Old Dominion University 1030 University Blvd Suffolk VA 23435 United States of America Department of Computational Modeling and Simulation Engineering Old Dominion University 1300 Engineering Computational Sciences Building Norfolk VA 23529 United States of America

In addition to stresses induced by the Covid-19 pandemic, supply chains worldwide have been growing more complex while facing a continuous onslaught of disruptions. This paper presents an analysis and extension of a graph based model for modeling and simulating the effects of such disruptions. The graph based model combines a Bayesian network approach for simulating risks with a network dependency analysis approach for simulating the propagation of disruptions through the network over time. The initial analysis provides evidence supporting extension to for using a multi-layered approach allowing for the inclusion of cyclic features in supply chain models. Initial results for individual layers and the aggregate model are presented and discussed. The paper is concluded with a discussion and recommended directions for future work.

关键词： Supply chain Risk disruption analysis modeling simulation Functional dependency analysis Graphical models

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Assessing and Improving Automated Viewpoint Planning for Static Laser Scanning Using Optimization Methods

Assessing and Improving Automated Viewpoint Planning for Sta...

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2024 ISPRS Technical Commission I Mid-term Symposium on Intelligent Sensing and Remote Sensing Application

作者： Noichl, Florian Stuecke, Maximilian Thielen, Clemens Borrmann, André Chair of Computational Modeling and Simulation School of Engineering and Design Technical University of Munich Munich80333 Germany Professorship of Complex Networks Tum Campus Straubing for Biotechnology and Sustainability Weihenstephan-Triesdorf University of Applied Sciences Straubing94315 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Garching bei München85748 Germany

The preparation of laser scanning missions is important for efficiency and data quality. Furthermore, it is a prerequisite for automated data acquisition, which has numerous applications in the built environment, including autonomous inspections and monitoring of construction progress and quality criteria. The scene and potential scanning locations can be discretized to facilitate the analysis of visibility and quality aspects. The remaining mathematical problem to generate an economic scan strategy is the Viewpoint Planning Problem (VPP), which asks for a minimum number of scanning locations within the given scene to cover the scene under pre-defined requirements. Solutions for this problem are most commonly found using heuristics. While these efficient methods scale well, they cannot generally return globally optimal solutions. This paper investigates the VPP based on a problem description that considers quality-constrained visibility in 3D scenes and suitable overlaps between individual viewpoints for targetless registration of acquired point clouds. The methodology includes the introduction of a preprocessing method designed to simplify the input data without losing information about the problem. The paper details various solution methods for the VPP, encompassing conventional heuristics and a mixed-integer linear programming formulation, which is solved using Benders decomposition. Experiments are carried out on two case study datasets, varying in specifications and sizes, to evaluate these methods. The results show the actual quality of the obtained solutions and their deviation from optimality (in terms of the estimated optimality gap) for instances where exact solutions can not be achieved. © 2024 Florian Noichl et al.

关键词： Visibility

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From Narratives to Conceptual Models via Natural Language Processing

From Narratives to Conceptual Models via Natural Language Pr...

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simulation Winter Conference

ISBN: (纸本)9781665476621

关键词： Visualization Natural language processing Object recognition Standards

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