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arXiv 2024年

作者： Vega-Torres, Miguel Arturo Braun, Alexander Borrmann, André Chair of Computational Modeling and Simulation Technical University of Munich Arcisstraße 21 Bavaria Munich80333 Germany

This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework’s remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework’s significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://***/MigVega/SLAM2REF, Data: https://***/10.14459/2024mp1743877. © 2024, CC BY.

关键词： Decision making

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BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement

arXiv

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arXiv 2024年

作者： Vega-Torres, Migual A. Ribic, Anna Soto, Bojra García de Borrmann, Andre Chair of Computational Modeling Simulation Technical University of Munich Munich Germany Saadiyat Island United Arab Emirates

This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment technique to align real-world measurements with the model. Experiments using real-world open-access data show that BIMCaP achieves superior accuracy, reducing translational error by over 4 cm compared to current state-of-the-art methods. This advancement enhances the accuracy and cost-effectiveness of 3D mapping methodologies like SLAM. BIMCaP’s improvements benefit various fields, including construction site management and emergency response, by providing up-to-date, aligned digital maps for better decision-making and productivity. Link to the repository: https://***/MigVega/BIMCaP. © 2024, CC BY.

关键词： Optical radar

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Transfer Learning Enhanced Full Waveform Inversion

arXiv

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arXiv 2023年

作者： Kollmannsberger, Stefan Singh, Divya Herrmann, Leon The Technical University of Munich Chair of Computational Modeling and Simulation Bavaria Munich80333 Germany

We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI). The presented methodology discretizes the unknown material distribution in the domain with a neural network within an adjoint optimization. To further increase efficiency of the FWI, pretrained neural networks are used to provide a good starting point for the inversion. This reduces the number of iterations in the Full Waveform Inversion for specific, yet generalizable settings. © 2023, CC BY-NC-ND.

关键词： Waveform analysis

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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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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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Gan Enables Outlier Detection and Property Monitoring for Additive Manufacturing of Complex Structures

SSRN

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SSRN 2023年

作者： Henkes, Alexander Herrmann, Leon Wessels, Henning Kollmannsberger, Stefan Computational Mechanics Group ETH Zürich Switzerland Chair of Computational Modeling and Simulation TU Munich Germany Braunschweig Germany

Additive manufacturing technologies have seen significant economic growth over the past years. Although additive manufacturing processes have matured in many areas, difficulties with regard to printing accuracy persist. Possible defects are, e.g., the generation of unwanted internal pores or a lack of fusion between layers, to name only a few. In general, defects result in a deviation between as-planned and as-built geometries, all of which can be difficult to detect in an automatized fashion. Previous work has shown that image-based simulation can assist in quality monitoring of produced parts and may complement experimental testing. Yet, both experimental testing and simulation-based approaches are involved and not yet directly applicable to all manufactured parts in a series production. The paper at hand suggests a remedy to this problem by using a generative adversarial network (GAN).Generative adversarial networks have shown to be able to emulate as-built geometries of engineering relevance. Moreover, they can realistically reproduce the distributions of such deviations. To this end, we present how this feature can be harvested to employ generative adversarial networks for outlier detection. To this end, we use the discriminator of a GAN as a classifier on as-built parts to judge whether an as-built structure is acceptable or defective. The viability of the approach is demonstrated on basic artificial structures with spherical voids as well as additively manufactured lattice structures whose geometry is acquired after production via computed tomography (CT). The methodology is not only applicable for automated property monitoring but potentially also for reliability estimates of neural networkbased property predictors. © 2023, The Authors. All rights reserved.

关键词： 3D printing

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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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Calculation of Embodied GHG Emissions in Early Building Design Stages Using Bim and Nlp-Based Semantic Model Healing

SSRN

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SSRN 2022年

作者： Forth, Kasimir Abualdenien, Jimmy Borrmann, André Computational Modeling and Simulation Technical University of Munich Germany

To reach the goals of limiting global warming, the embodied \ac{ghg} emissions of new buildings need to be quantified and optimized in the very early design stages, during which design decisions significantly influence the success of projects in achieving their performance goals. Semantically rich building models (BIM) enable to perform an automated quantity take-off of the relevant components for calculating a whole building \ac{lca}. However, imprecise type and property information often found in today's BIM practice hinders a seamless processing for downstream applications. At the same time, the early design stages are characterized by high uncertainty due to the lack of information and knowledge, making a holistic and consistent \ac{lca} for supporting design decisions and optimizing performance challenging. In assessing this often vague information, it is essential to consider different levels of element and material information for matching \ac{bim} to \ac{lca} data. For example, the structural properties of concrete are not yet defined in early design stages and should instead be considered as a range of material options due to different compressive strength classes. This paper presents a methodology for automatically matching the coarse information available in BIM models of the early design stages to the respective entries in LCA databases, as a basis for calculating the embodied emissions. In more detail, the proposed method is based on \ac{nlp}, using different strategies to increase performance in matching materials from a \ac{bim} model to a knowledge database with environmental indicators of commonly used components. The knowledge database contains all missing information for \ac{lca}s and has different levels of information for a range of several potential design options of components, elements, and materials, including their dependencies. Accordingly, this paper investigates multiple \ac{nlp} techniques and evaluates the performance of state-of-the-a

关键词： Compressive strength

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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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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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