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arXiv 2022年

作者： Hug, Lisa Dahan, G. Kollmannsberger, S. Rank, E. Yosibash, Z. Chair for Computational Modeling and Simulation Technical University of Munich Arcisstr. 21 Munich80333 Germany School of Mechanical Engineering The Iby and Aladar Fleischman Faculty of Engineering Tel-Aviv University Ramat-Aviv69978 Israel

Proximal humerus impacted fractures are of clinical concern in the elderly population. Prediction of such fractures by CT-based finite element methods encounters several major obstacles such as heterogeneous mechanical properties and fracture due to compressive strains. We herein propose to investigate a variation of the phase field method (PFM) embedded into the finite cell method (FCM) to simulate impacted humeral fractures in fresh frozen human humeri. The force-strain response, failure loads and the fracture path are compared to experimental observations for validation purposes. The PFM (by means of the regularization parameter `0) is first calibrated by one experiment and thereafter used for the prediction of the mechanical response of two other human fresh frozen humeri. All humeri are fractured at the surgical neck and strains are monitored by Digital Image Correlation (DIC). Experimental strains in the elastic regime are reproduced with good agreement (R2 = 0.726), similarly to the validated finite element method [9]. The failure pattern and fracture evolution at the surgical neck predicted by the PFM mimic extremely well the experimental observations for all three humeri. The maximum relative error in the computed failure loads is 3.8%. To the best of our knowledge this is the first method that can predict well the experimental compressive failure pattern as well as the force-strain relationship in proximal humerus fractures. © 2022, CC BY-NC-ND.

关键词： Finite element method

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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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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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Generating brick-like melt pools for Laser-Based Powder Bed Fusion using flexible beam shaping: a proof of concept on bead-on-plate single tracks

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Procedia CIRP 2024年 124卷 746-750页

作者： Jonas Grünewald Robin Prudlik Vijaya Holla Philipp Kopp Richard Off Thomas Stoll Stefan Kollmannsberger Katrin Wudy Professorship of Laser-based Additive Manufacturing TUM School of Engineering and Design Technical University of Munich Freisinger Landstraße 52 85748 Garching Germany Oerlikon AM Europe GmbH Freisinger Landstraße 52 85748 Garching Germany Chair of Computational Modeling and Simulation TUM School of Engineering and Design Technical University of Munich Arcisstraße 21 80333 Munich Germany Chair of Data Science in Civil Engineering Bauhaus University Weimar Coudraystraße 13A 99423 Weimar Germany

Despite the highest industrial maturity among additive manufacturing processes, laser-based powder bed fusion of metals (PBF-LB/M) lacks the productivity to further establish as an industrial manufacturing process. One approach for adjusting the melt pool shape to increase productivity in PBF-LB/M is spatial beam shaping. The freedom of beam shaping is almost unlimited, owing to modern optical concepts. The key is a beam shape optimized for a specific target. In this publication, the target is a brick-like melt pool cross-section. The publication demonstrates how the corresponding laser beam shape is designed using numerical optimization. The computed beam shape is reproduced using reflective spatial light phase modulators. The calculation is validated based on single-track experiments. Applying the optimized beam shape results in a melt pool shape similar to the optimization target. The width and depth of the melt track produced deviate by a maximum of 7 % from the specified target values.

关键词： Additive Manufacturing PBF-LB/M LCOS-SLM IN718 Beam Shape Optimization

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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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Distributed Strategy for Power Re-Allocation in High Performance Applications

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Journal of Computer and Communications 2020年第12期8卷 142-158页

作者： Vaibhav Sundriyal Masha Sosonkina Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk Virginia 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 distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in a performance degradation of as low as 1% under the proposed strategy compared with the execution in the unlimited power case.

关键词： Multinode Power Allocation RAPL UFS DVFS Maximizing Performance Component Power

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Attack Graph Embedded Machine Learning Platform For Cyber Situational Awareness

Attack Graph Embedded Machine Learning Platform For Cyber Si...

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MILCOM, Military Communications Conference

作者： Md Ariful Haque Sachin Shetty Charles A. Kamhoua Kimberly Gold Computational Modeling and Simulation Engineering Old Dominion University Norfolk VA USA The U.S. Army Research Laboratory Network Security Research Adelphi MD USA Crane Division Naval Surface Warfare Center Crane IN USA

An attack graph is a crucial tool for network security analysis. Traditionally, network administrators utilize attack graphs to generate possible attack paths and estimate the attack probability (risk) in a networked environment. However, the attack probability does not alone provide enough directions to take security measures. Furthermore, there is a lack of combining the attack probability with the attack node's topological influence factors, which also contributes to propagate cyberattacks. This work proposes a cyber risk assessment platform to enhance cyber situational awareness by addressing those gaps. We first present methods to compute cyber risk using the attack graph and then extract network topological influence factors (i.e., features) using graph centrality measures. Next, we apply unsupervised learning to the extracted features to find the network's highly exploitable attack points. Finally, we use graph embedding techniques to identify the objective similarity among the attack privilege nodes. We illustrate the applications of our machine learning-based cyber risk assessment platform using a SCADA (supervisory control and data acquisition) case study for the cyber-physical power systems. The simulation results demonstrate that the platform provides a better understanding of the cyber risk assessment and situational awareness by applying machine learning techniques on the attack graph.

关键词： Military communication simulation SCADA systems Machine learning Network security Feature extraction Power systems

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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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Estimation of Aortic Valve Interstitial Cell-Induced 3D Remodeling of Poly(Ethylene Glycol) Hydrogel Environments Using an Inverse Finite Element Approach

SSRN

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SSRN 2022年

作者： Khang, Alex Steinman, John Tuscher, Robin Feng, Xinzeng Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation Oden Institute for Computational Engineering and Sciences The Department of Biomedical Engineering The University of Texas at Austin 201 East 24th St Stop C0200 AustinTX78712-1229 United States

Aortic valve interstitial cells (AVICs) reside within the leaflet tissues of the aortic valve and function to replenish, restore, and remodel extracellular matrix components. AVIC contractility is brought about through the contractile properties of the underlying stress fibers and plays a crucial role in processes such as wound healing and mechanotransduction. Currently, it is technically challenging to directly investigate AVIC contractile behaviors within the dense leaflet tissues. As a result, optically clear poly (ethylene glycol) (PEG) hydrogel matrices have been used to study AVIC contractility through means of 3D traction force microscopy (3DTFM). However, the stiffness of the hydrogel material within the vicinity of the AVIC is difficult to measure directly and is further confounded by the remodeling activity of the AVIC. Ambiguity in the local hydrogel mechanical properties can lead to large errors in computed cellular tractions. Herein, we developed an inverse computational approach to estimate AVIC-induced remodeling of the hydrogel material. The capabilities of the model were validated with a ground truth data set generated via a test problem comprised of an experimentally measured AVIC geometry and a prescribed modulus field containing unmodified, stiffened, and degraded regions. The inverse model was able to estimate the ground truth data set with high accuracy. When applied to AVICs assessed via 3DTFM, the model estimated regions of significant stiffening and degradation local to the AVIC. We observed that stiffening was largely localized at AVIC protrusions and was likely a result of collagen deposition as confirmed by immunostaining for collagen type 1. Degradation was more spatially uniform and present in regions further away from the AVIC surface and likely a result of enzymatic activity. Our results indicate that AVICs substantially modify the local hydrogel mechanics, which were successfully quantified by our computational model. Looking forward

关键词： Tissue

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Parallel solution of saddle point systems with nested iterative solvers based on the Golub-Kahan Bidiagonalization

Parallel solution of saddle point systems with nested iterat...

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作者： Kruse, Carola Sosonkina, Masha Arioli, Mario Tardieu, Nicolas Rüde, Ulrich Cerfacs Toulouse France Department of Computational Modeling and Simulation Engineering Old Dominion University NorfolkVA United States Libera Universita Mediterranea Casamassima Italy IMSIA UMR 9219 EDF-CNRS-CEA-ENSTA Université Paris Saclay Paris France Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

The Golub-Kahan bidiagonalization is widely used in the singular value decomposition of rectangular matrices and has been generalized to an iterative solver for symmetric indefinite linear systems with a two-by-two block structure. In this work, we present a scalability study of this generalized solver as implemented in a recent release of the parallel numerical library PETSc (Portable, Extensible Toolkit for Scientific Computation). We present an improved solver performance for the two-dimensional (2D) Stokes equations as compared to previous work. Furthermore, we investigate the performance of different parallel inner solvers in the outer Golub-Kahan iteration for a three-dimensional Stokes problem. The study includes parallel sparse direct solvers and multigrid methods. When increasing the number of cores for a fixed total problem size, the solver exhibits good speedups of up to 50% at the 1024 core count. For the tests in which the total problem size grows while the workload in each core stays constant, the parallel performance of the solver scales almost linearly with the increase in the core counts. In particular, the computation time increases only by about 15% when the number of cores increases from 80 to 1024 for a 2D test case. © 2020 John Wiley & Sons, Ltd.

关键词： Singular value decomposition

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