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SSRN 2024年

作者： Noichl, Florian Lichti, Derek D. Borrmann, André Computational Modeling and Simulation Technical University of Munich Arcisstr. 21 Munich80333 Germany Department of Geomatics Engineering University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada

Laser scanning is increasingly important for applications in the built environment and requires efficient planning for effective data collection. The conventional process is a time-consuming and repetitive manual task, presenting a strong case for automation. This paper introduces a novel method for scan planning in complex 3D environments, overcoming the limitations of existing approaches. It accepts any 3D model or point cloud as input by processing them as triangulated meshes. The method involves automated steps of mesh processing, viewpoint candidate generation, and evaluations of visibility and coverage. It facilitates optimized planning for static laser scanning missions by selecting appropriate viewpoints while considering targetless registration and sequence planning needs. Our method can handle uniform scene coverage requirements and environments with specific local requirements. It is rigorously tested through method comparisons and extensive parameter studies and applied to a multi-level indoor point cloud and a refinery’s 3D model, demonstrating its practical applicability. © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 315-329页

作者： Yifang Ma Zhenyu Wang Hong Yang Lin Yang Department of Information Technology and Decision Sciences Old Dominion UniversityNorfolkVA 23529 USA Department of Computational Modeling and Simulation Engineering Old Dominion UniversityNorfolkVA 23529 USA Department of Mechanical and Electrical Engineering Huazhong Agricultural UniversityWuhan 430070China

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation *** by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in *** achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV *** research have made great efforts in investigating different aspects of applying AI in AV ***,few studies have offered the research community a thorough examination of current practices in implementing AI in ***,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research ***,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision *** investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their *** on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected *** paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

关键词： Artificial intelligence(AI) autonomous vehicles(AVs) deep learning(DL) motion planning perception self-driving

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The Compliance Method for Tunnel and Delamination Cracks

SSRN

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SSRN 2024年

作者： Herrmann, Leon Mikkelsen, Lars P. Legarth, Brian N. Niordson, Christian F. Chair of Computational Modeling and Simulation Technical University of Munich MunichD-80333 Germany Department of Wind and Energy Systems Technical University of Denmark RoskildeDK-4000 Denmark Department of Civil and Mechanical Engineering Solid Mechanics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

We propose a method for quantifying the competition between tunnel and delamination cracking in laminates. Based on the compliance method for fracture, the competition between the two damage mechanisms is investigated based on compliance maps that may be pre-computed, thus providing an efficient tool for predicting damage development in laminates without ad hoc fracture simulations. Tunnel and delamination cracking in an off-axis layer, much thinner than the load-carrying layers of a laminate, are used as an example to illustrate the proposed methodology. However, the method may be generalized to any parameterizable cracking mechanism. The cracks are simulated in a specialized two-dimensional off-axis finite element framework. The method is validated against the energy balance method for tunneling cracks and the virtual crack closure technique for delamination cracks. The method has great potential in the context of non-destructive testing, where future damage states can be predicted from precomputed or experimentally determined compliance maps, thus reducing computational and experimental costs. © 2024, The Authors. All rights reserved.

关键词： Finite element method

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Towards a deep learning potential for anhydrous proton transport

Towards a deep learning potential for anhydrous proton trans...

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2020 AIChE Annual Meeting

作者： Achar, Siddarth Johnson, J. Karl Computational Modeling and Simulation Program University of Pittsburgh United States Department of Chemical Engineering University of Pittsburgh United States

ISBN: (纸本)9780816911141

Standard density functional theory (DFT) molecular dynamic (MD) simulations are prohibitively expensive in terms ofcompute time and memory requirements when the size of the physical system is larger than several hundred *** alternate approach is to develop a deep learning neural network potential that can accurately reproduce DFTenergies within an error of several 10s of meVs. Neural network potentials thus allow large-scale MD simulations formuch longer times than can be achieved with DFT-MD, but with comparable accuracy. We have used the recentlypublished DeePMD [1] formalism to develop potentials for systems relevant to modeling proton transport onfunctionalized graphane. We have used DFT-MD calculations for small structures of graphane and graphanol astraining data to build reliable machine-learned potentials. Apart from energies and forces, vibrational and thermalproperties such as phonon dispersion relations and thermal expansion coefficients from DFT-MD were used to validatethe accuracy of our trained DeePMD potentials. Our goal is to develop a machine learning potential that is able toaccurately predict proton conduction on graphanol in the complete absence of water. Previous DFT calculations haveshown that graphanol a promising candidate for anhydrous proton exchange membrane applications [2]. We seek toidentify large-scale and long-time proton hopping behavior for this system through the use of machine learningpotentials. © 2020 American Institute of Chemical Engineers. All rights reserved.

关键词： Density functional theory

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Numerical evaluation of high cycle fatigue life for additively manufactured stainless steel 316L lattice structures: Preliminary considerations

Material Design and Processing Communications

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Material Design and Processing Communications 2021年第4期3卷

作者： Alaimo, Gianluca Carraturo, Massimo Korshunova, Nina Kollmannsberger, Stefan Department of Civil Engineering and Architecture University of Pavia Pavia Italy Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany

Lattice components manufactured by selective laser melting processes are increasingly employed for producing high performing lightweight parts to be used in several industrial applications. However, the geometry at a submillimeter scale can exhibit not negligible differences with respect to the nominal design due to the high complexity of the manufacturing process. Accordingly, the mechanical behavior of lattice structures is strongly influenced by such process-induced geometrical defects. Therefore, to numerically predict the fatigue behavior of lattice components, the as-built geometry, as acquired, for instance, by means of micro-computed tomography, should be considered. In this work, we employ an immersed boundary method, namely, the finite cell method, to develop a numerical framework suitable to compute fatigue life directly on an as-built lattice geometry. © 2021 The Authors. Material Design & Processing Communications published by John Wiley & Sons Ltd.

关键词： Selective laser melting

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A High-Fidelity 3D Micromechanical Model of Ventricular Myocardium 11th

A High-Fidelity 3D Micromechanical Model of Ventricular Myoc...

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11th International Conference on Functional Imaging and modeling of the Heart, FIMH 2021

作者： Li, David S. Mendiola, Emilio A. Avazmohammadi, Reza Sachse, Frank B. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation Oden Institute for Computational Engineering and Sciences Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Computational Cardiovascular Bioengineering Lab Department of Biomedical Engineering Texas A&M University College StationTX United States Nora Eccles Harrison Cardiovascular Research and Training Institute Department of Biomedical Engineering The University of Utah Salt Lake CityUT United States

ISBN: (纸本)9783030787097

Pulmonary arterial hypertension (PAH) imposes a pressure overload on the right ventricle (RV), leading to myofiber hypertrophy and remodeling of the extracellular collagen fiber network. While the macroscopic behavior of healthy and post-PAH RV free wall (RVFW) tissue has been studied previously, the mechanical microenvironment that drives remodeling events in the myofibers and the extracellular matrix (ECM) remains largely unexplored. We hypothesize that multiscale computational modeling of the heart, linking cellular-scale events to tissue-scale behavior, can improve our understanding of cardiac remodeling and better identify therapeutic targets. We have developed a high-fidelity microanatomically realistic model of ventricular myocardium, combining confocal microscopy techniques, soft tissue mechanics, and finite element modeling. We match our microanatomical model to the tissue-scale mechanical response of previous studies on biaxial properties of RVFW and examine the local myofiber-ECM interactions to study fiber-specific mechanics at the scale of individual myofibers. Through this approach, we determine that the interactions occurring at the tissue scale can be accounted for by accurately representing the geometry of the myofiber-collagen arrangement at the micro scale. Ultimately, models such as these can be used to link cellular-level adaptations with organ-level adaptations to lead to the development of patient-specific treatments for PAH. © 2021, Springer Nature Switzerland AG.

关键词： Tissue

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First-principles determination of the phonon-point defect scattering and thermal transport due to fission products in ThO2

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Physical Review Materials 2024年第2期8卷 025401-025401页

作者： Linu Malakkal Ankita Katre Shuxiang Zhou Chao Jiang David H. Hurley Chris A. Marianetti Marat Khafizov Computational Mechanics and Materials Department Idaho National Laboratory Idaho Falls Idaho 83415 USA Department of Scientific Computing Modeling and Simulation SP Pune University Pune 411007 India Idaho National Laboratory Idaho Falls Idaho 83415 USA Department of Applied Physics and Applied Mathematics Columbia University 500 West 120th Street New York New York 10027 USA Department of Mechanical and Aerospace Engineering The Ohio State University 201 West 19th Avenue Columbus Ohio 43210 USA

This work presents the first-principles calculations of the lattice thermal conductivity degradation due to point defects in thorium dioxide using an iterative solution of the Peierls-Boltzmann transport equation. We have used the nonperturbative Green's function methodology to compute the phonon-point defect scattering rates that consider the local distortion around the point defect, including the mass difference changes, interatomic force constants, and structural relaxation near the point defects. The point defects considered in this work include the vacancy of thorium (VTh) and oxygen (VO), substitutions of helium (HeTh), krypton (KrTh), zirconium (ZrTh), iodine (ITh), and xenon (XeTh) in the thorium site, and the three different configurations of the Schottky defects. The results of the phonon-defect scattering rate reveal that among all the considered intrinsic defects, the thorium vacancy and helium substitution in the thorium site scatter the phonon most due to the substantial changes in the force constant and structural distortions. The scattering of phonons due to the substitutional defects unveils that the zirconium atom scatters phonons the least, followed by xenon, iodine, krypton, and helium. This is contrary to the intuition that the scattering strength follows HeTh>KrTh>ZrTh>ITh>XeTh based on the mass difference. This striking difference in the zirconium phonon scattering is due to the local chemical environment changes. Zirconium is an electropositive element with valency similar to thorium and, therefore, can bond with the oxygen atoms, thus creating less force constant variance compared to iodine, an electronegative element, and the noble gases helium, xenon, and krypton. These results can serve as a benchmark for analytical models and help the engineering-scale modeling effort for nuclear design.

关键词： Carrier dynamics Energy materials Heat transfer Irradiation effects Lattice dynamics Nuclear power Phonons Thermal conductivity Thermal properties

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Selective Targeted Transfer Learning for Malware Classification

Selective Targeted Transfer Learning for Malware Classificat...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Priyanka Aggarwal Sayyed F. Ahamed Sachin Shetty Laura J. Freeman Computational Modeling and Simulation Engineering Old Dominion University Norfolk Virginia USA Virginia Tech Department of StatisticsVirginiaUSA

ISBN: (纸本)9781665416245

Data driven machine learning and deep neural models demand accessibility to high-quality, clean, and enormous datasets. Obtaining a labeled dataset is an expensive, challenging and time-consuming process, leading to the development of transfer learning (TL) methodology. TL leverages gained knowledge from a previously trained source model into specific yet similar task models with limited data domain coverage. For effective transfer, the training (source) and test (target) data may share similar or different marginal and conditional distributions. Negative transfer may occur, due to noise accumulated during learning iterations decreasing system performance metrics [1]. In this paper, we propose a selective targeted transfer learning (STTL) method to resolve the transferability between source and target with minimal data requirements to statically detect malicious Windows portable executable files. Kernel layers from deep models map the marginal distributions of the source and target domain into a common latent space while selective source feature and hard target samples selection draws the conditional distributions closer. Here we utilize benchmark Endgame Malware Benchmark for Research (EMBER) dataset which contains more than one million malware and benign samples, to validate our work. We automatically identify source, target pairs for three different malware families (banking trojan, adware, spyware) based on statistical measures and perform feature extraction/reduction (2381 features to 29, 28, 18 features) to formulate the new training dataset for effective targeted TL. For three different families (trojan, adware, spyware malwares) with varying dimensions, we prove that acquiring a small number of intentionally well-chosen samples from the target environment (2441, 2404, 3083 which is less than 14% of target sample count), we can drastically improve model accuracy to 90.8%, 94.4%, 93.5 % in comparison to the baseline transfer learning accuracy of 80.5%, 76.1 %

关键词： Training System performance Transfer learning Benchmark testing Feature extraction Data models Trojan horses

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Scalability of sensor simulation in ros-gazebo platform with and without using GPU

Scalability of sensor simulation in ros-gazebo platform with...

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2020 Spring simulation Multiconference, SpringSim 2020

作者： Saglam, Ahmet Papelis, Yiannis Department of Computational Modeling and Simulation Engineering Old Dominion University 5115 Hampton Blvd NorfolkVA United States Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Blvd SuffolkVA United States

ISBN: (纸本)9781713812883

Autonomous mobile robots depend heavily on sensors for interpreting the external environment and planning their movement. Given the complexity of even simple environments, the use of simulation is necessary to test sensory perception and movement strategies efficiently. Accurate and efficient sensor simulation is key to the successful development and fielding of autonomous mobile robots. In this paper, we investigate the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator. Specifically, the performance and scalability of the simulator are assessed with and without support from GPU in two virtual worlds, one highly-complex in terms of the number of collision targets and one not-complex. In both cases, we increase the number of sensors and evaluate system performance. The results show that the use of GPU helps improve performance, providing better speedups in the complex scene. However, the number of sensors that can be used is limited when using GPU. © 2020 Society for modeling & simulation International (SCS)

关键词： Multipurpose robots

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Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv

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arXiv 2022年

作者： Munir, Shirajum Kim, Ki Tae Adhikary, Apurba Saad, Walid Shetty, Sachin Park, Seong-Bae Hong, Choong Seon Virginia Modeling Analysis and Simulation Center Department of Computational Modeling and Simulation Engineering Old Dominion University SuffolkVA23435 United States Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Kyung Hee University Yongin-si17104 Korea Republic of Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space, and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation. © 2022, CC BY-NC-ND.

关键词： Internet of Everything

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