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

作者： Collins, Fiona C. Noichl, Florian Slepicka, Martin Cones, Gerda Borrmann, André Chair of Computational Modeling and Simulation School of Engineering and Design Technical University of Munich Munich80333 Germany

Point clouds, image data, and corresponding processing algorithms are intensively investigated to create and enrich Building Information Models (BIM) with as-is information and maintain their value across the building lifecycle. Point clouds can be captured using LiDAR and enriched with color information from images. Complementary to such dual-sensor systems, thermography captures the infrared light spectrum, giving insight into the temperature distribution on an object’s surface and allowing a diagnosis of the as-is energetic health of buildings beyond what humans can see. Although the three sensor modes are commonly used in pair-wise combinations, only a few systems leveraging the power of tri-modal sensor fusion have been proposed. This paper introduces a sensor system comprising LiDAR, RGB, and a radiometric thermal infrared sensor that can capture a 360-degree range through bi-axial rotation. The resulting tri-modal data is fused to a thermo-color point cloud from which temperature values are derived for a standard indoor building setting. Qualitative data analysis shows the potential for unlocking further object semantics in a state-of-the-art Scan-to-BIM pipeline. Furthermore, an outlook is provided on the cross-modal usage of semantic segmentation for automatic, accurate temperature calculations. © 2024 Fiona C. Collins et al.

关键词： Semantics

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NUMERICAL FLOW VISUALIZATION: VISTA AND EXPEDITION

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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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An Alternative Approach to Automated Code Checking – Application of Graph Neural Networks Trained on Synthetic Data for an Accessibility Check Case Study 19th

An Alternative Approach to Automated Code Checking – Applic...

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19th International Conference on Computing in Civil and Building engineering, ICCCBE 2022

作者： Bloch, Tanya Borrmann, André Pauwels, Pieter Faculty of Civil and Environmental Engineering Technion Israel Institute of Technology Haifa Israel Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany Faculty of Built Environment Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783031353987

Automated Code Checking (ACC) can be defined as a classification task aiming to classify building objects as compliant or not compliant to a code provision at hand. While Machine Learning (ML) is a useful tool to perform such classification tasks, it presents several drawbacks and limitations. Buildings are complex compositions of instances that are related to each other by functional and topological relationships. This type of data can be easily supported by property graphs that provide a flexible representation of attributes for every instance as well as the relationships between the instances. This, together with the recent developments in the field of graph-based learning led the authors to explore a novel approach for ACC supported by Graph Neural Networks (GNN). This paper presents a new workflow that implements GNNs for ACC to leverage the advantages of ML but alleviate the limitations. We illustrate the suggested workflow by training a GNN model on a synthetic data set and using the trained classifier to check compliance of a real BIM model to accessibility requirements. The accuracy of the classifier on a test set is 86% and the accuracy of obtained results during the accessibility check is 82%. This suggests that GNNs are applicable to ACC and that classifiers trained on synthetic data can be used to classify building design provided by the industry. While the results are encouraging, they also point to the need for further research to establish the scope and boundary conditions of applying GNNs to ACC. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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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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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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Full-waveform inversion via the scaled boundary finite element method

arXiv

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arXiv 2025年

作者： Daneshyar, Alireza Kollmannsberger, Stefan Chair of Computational Modeling and Simulation Technical University of Munich Germany Data Science in Civil Engineering Bauhaus-Universität Weimar Germany

We begin by addressing the time-domain full-waveform inversion using the adjoint method. Next, we derive the scaled boundary semi-weak form of the scalar wave equation in heterogeneous media through the Galerkin method. Unlike conventional scaled boundary finite element formulations, the resulting system incorporates variable density and two additional terms involving its spatial derivative. As a result, the coefficient matrices are no longer constant over the elements but instead depend on the so-called radial coordinate, rendering the common solution methods such as low-frequency expansion and continued fractions—both of which assume that these matrices can be defined solely by boundary values—inapplicable. In this context, we introduce a radial discretization scheme for solving the transient scalar wave equation with spatially varying density within the framework of the scaled boundary finite element method. This approach begins with temporal discretization in the time domain, followed by spatial discretization along the radial coordinate. We employ the Newmark method for the former and a finite differencing scheme for the latter. However, the choices underlying our ansatz are made for demonstration purposes and remain flexible. Following these discretizations, we introduce an algorithmic condensation procedure to compute the dynamic stiffness matrices of the elements on the fly. Therefore, we maneuver around the need to introduce auxiliary unknowns into the global algebraic system. As a result, the optimization problem is structured in a two-level hierarchy. The higher level computes the nodal wavefields on a relatively coarse mesh, while the lower level provides the gradient of the cost function for a finely distributed set of model parameters. We obtain the Fréchet kernel by computing the zero-lag cross-correlations of the forward and adjoint wavefields, and solve the minimization problems iteratively by moving downhill on the cost function hypersurface throug

关键词： Cost functions

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TwinGen: Advanced technologies to automatically generate digital twins for operation and maintenance of existing bridges 14th

TwinGen: Advanced technologies to automatically generate dig...

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14th European Conference on Product and Process Modelling, ECPPM 2022

作者： Vilgertshofer, S. Mafipour, M.S. Borrmann, A. Martens, J. Blut, T. Becker, R. Blankenbach, J. Göbels, A. Beetz, J. Celik, F. Faltin, B. König, M. Computational Modeling and Simulation Technical University of Munich Germany Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems RWTH Aachen University Germany Design Computation RWTH Aachen University Germany Computing in Engineering Ruhr University Bochum Germany

ISBN: (纸本)9781032406732

The high effort of operating and maintaining existing infrastructure facilities resulted in a large stock of structurally deficient bridges in most industrialized countries. Today, the condition assessment of bridges is conducted mostly manually. To relieve effort and costs, the digitization and automation of conventional manual, labour-intensive methods is necessary. We interpret the ambiguously used term "digital twin" (DT) in this study as a semantic-geometric model of an existing asset (here, a bridge) that contains all information required for assessing its current condition. This paper proposes an approach to automatically generate the DT of existing bridges from point cloud data (PCD) and images captured from the structure. The PCD of the bridge is semantically segmented by means of ML techniques, and a digital model is created using parametric modeling. Subsequently, detected damages and data from existing bridge maintenance systems are linked to the model to create a full DT. This paper reports the main results of the TwinGen research project: The digital twinning process of bridges can be automated to a large extent, in order to efficiently support the maintenance process of bridges. © 2023 the Author(s).

关键词： Bridges

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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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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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Deception for Characterizing Adversarial Strategies in Complex Networked Systems 19

Deception for Characterizing Adversarial Strategies in Compl...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Al Amin, Md Ali Reza Foytik, Peter Shetty, Sachin Dorismond, Jessica Gamarra, Marco Old Dominion University Computational Modeling and Simulation Engineering NorfolkVA United States Air Force Research Laboratory Information Directorate RomeNY United States

ISBN: (纸本)9781665431620

There is a need for systematic characterization of complex networked systems involving friendly forces and opponent forces to understand the adversary opportunities and capabilities to cause harm and develop counter-strategies that would minimize the adversarial impact. Specifically, we need the ability to quantify the incurred cost (cost of protecting friendly forces) and induced cost (opponent cost to cause damage to friendly systems). In this paper, we address the problem of characterizing adversarial strategies and develop a suite of metrics that quantify the opportunity and capability of the adversary. We also propose a deception strategy that would force the opponent to redirect away from mission-critical systems by optimally placing decoy nodes and thereby expending opponent resources with little gain. Our adversary strategy characterization involves an integrated graph theoretic and Bayesian network that tracks lateral propagation of adversaries in a complex networked sys-tem. Our deception strategy is devised by a Partially Observable Monte Carlo Planning (POMCP) Framework that provides a sequence of actions that force an attacker to be redirected to a decoy network with high probability. © 2022 IEEE.

关键词： Bayesian networks

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