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Recognition of temporary vertical objects in large point clouds of construction sites

Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2022年第4期174卷 134-149页

作者： Torres, Miguel A. Vega Braun, Alexander Noichl, Florian Borrmann, André Bauer, Heiko Wohlfeld, Denis Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany CIO TUM Group Lead Digital Twinning Chair of Computational Modeling and Simulation Technical University of Munich Munich Germany FARO Labs Faro Europe GmbH and Co. KG Korntal-Münchingen Germany Director of Research Networks FARO Labs Faro Europe GmbH and Co. KG Korntal-Münchingen Germany

Although adherence to the project schedule is the most critical performance metric among project owners, still 53% of typical construction projects exhibit schedule delays. While construction progress monitoring is key to allow effective project management, it is still a largely manual, error-prone and inefficient process. To contribute to more efficient construction progress monitoring, this research proposes a method to detect automatically the most common temporary object classes in large-scale laser scanner point clouds of construction sites. Finding the position of these objects in the point cloud can help determine the current state of construction progress and verify compliance with safety regulations. The proposed workflow includes a combination of several techniques: image processing over vertical projections of point clouds, finding patterns in three-dimensional (3D) detected contours and performing checks over vertical cross-sections with deep learning methods. After applying and testing the method on three real-world point clouds and testing with three object categories (cranes, scaffolds and formwork), the results reveal that the authors' technique achieves rates above 88% for precision and recall and outstanding computational performance. These metrics demonstrate the capability of the method to support automatic 3D object detection in point clouds of construction sites. © 2022 ICE Publishing: All rights reserved.

关键词： Deep learning

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A computational Pipeline for Patient-Specific Prediction of the Post-operative Mitral Valve Functional State 12th

A Computational Pipeline for Patient-Specific Prediction of...

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Functional Imaging and modeling of the Heart - 12th International Conference, FIMH 2023, Proceedings

作者： Liu, Hao Simonian, Natalie T. Pouch, Alison M. Gorman, III, Joseph H. Gorman, Robert C. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences and the Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Departments of Radiology and Bioengineering University of Pennsylvania PhiladelphiaPA19104 United States Gorman Cardiovascular Research Group Department of Surgery Smilow Center for Translational Research Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

ISBN: (纸本)9783031353017

Mitral valve (MV) repair is safer than replacement for mitral regurgitation (MR) treatment, but long-term outcomes remain suboptimal and poorly understood. Moreover, preoperative optimization is complicated due to the heterogeneity of MR presentations and potential repair configurations. We thus developed a patient-specific MV computational pipeline to quantitatively predict the post-repair MV functional state using standard-of-care preoperative imaging data alone. First, we built a finite-element model of the full patient-specific MV apparatus by quantifying the MV chordae tendinae (MVCT) distributions from 5 CT-imaged excised human hearts and incorporating this data with patient-specific MV leaflet geometries and and MVCT origin displacements from preoperative 3D echocardiography. We then calibrated the leaflet and MVCT pre-strains by simulating preoperative MV closure in order to tune the functionally equivalent, patient-specific mechanical behavior. With this fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by modifying the annular displacement to match the applied ring size. In all patient cases, the postoperative geometries were predicted to within 1 mm of the target, and the MV leaflet strain fields demonstrated very good global and local correspondence to results from a previous heavily validated pipeline. Additionally, our model predicted increased postoperative posterior leaflet tethering in a recurrent patient, which is the likely driver of long-term MV repair failure. This pipeline allows us to predict postoperative outcomes using strictly preoperative clinical data, which lays the foundation for quantitative surgical planning, personalized patient selection, and ultimately, more durable MV repairs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Echocardiography

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Knee Detection in Bayesian Multi-Objective Optimization Using Thompson Sampling

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IEEE Transactions on Evolutionary Computation 2024年

作者： Heidari, Arash Qing, Jixiang Gonzalez, Sebastian Rojas Branke, Juergen Dhaene, Tom Couckuyt, Ivo Computer Science at IDLab Ghent University Ghent Belgium Imperial College London United Kingdom Department of Computational Mathematics Data Science Institute Belgium Warwick Business School University of Warwick Coventry United Kingdom Computer Modeling and Simulation Research Group University of Antwerp Antwerp Belgium Cluster in IDLab Ghent University Belgium

Real-world problems often consist of multiple conflicting objectives to be optimized simultaneously, featuring a set of Pareto-optimal solutions. Estimating the entire Pareto front can be computationally expensive, and is not always necessary, as decision makers will likely be interested only in specific regions of the Pareto front. In the absence of knowledge about the decision maker preferences, the so-called knees in the Pareto front are considered to be particularly attractive. In this paper, we propose using Thompson Sampling in the Bayesian optimization framework to estimate the location of the knee regions in a data-efficient manner. Our experimental results show that the proposed methods accurately locate the knee regions after a very small number of evaluations, providing a computationally efficient approach to single-and multi-knee detection in multi-objective optimization. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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A computational Pipeline for Patient-Specific Prediction of the Postoperative Mitral Valve Functional State

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Journal of Biomechanical Engineering 2023年第11期145卷 111002-111002页

作者： Liu, Hao Simonian, Natalie T. Pouch, Alison M. Iaizzo, Paul A. Gorman, Joseph H. Gorman, Robert C. Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences Department of Biomedical Engineering The University of Texas at Austin AustinTX78712-1229 United States Departments of Radiology and Bioengineering University of Pennsylvania PhiladelphiaPA19104 United States Visible Heart Laboratories Department of Surgery University of Minnesota MinneapolisMN55455 United States Gorman Cardiovascular Research Group Department of Surgery Smilow Center for Translational Research Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States

While mitral valve (MV) repair remains the preferred clinical option for mitral regurgitation (MR) treatment, long-term outcomes remain suboptimal and difficult to predict. Furthermore, pre-operative optimization is complicated by the heterogeneity of MR presentations and the multiplicity of potential repair configurations. In the present work, we established a patient-specific MV computational pipeline based strictly on standard-of-care pre-operative imaging data to quantitatively predict the post-repair MV functional state. First, we established human mitral valve chordae tendinae (MVCT) geometric characteristics obtained from five CT-imaged excised human hearts. From these data, we developed a finite-element model of the full patient-specific MV apparatus that included MVCT papillary muscle origins obtained from both the in vitro study and the pre-operative three-dimensional echocardiography images. To functionally tune the patient-specific MV mechanical behavior, we simulated pre-operative MV closure and iteratively updated the leaflet and MVCT prestrains to minimize the mismatch between the simulated and target end-systolic geometries. Using the resultant fully calibrated MV model, we simulated undersized ring annuloplasty (URA) by defining the annular geometry directly from the ring geometry. In three human cases, the postoperative geometries were predicted to 1 mm of the target, and the MV leaflet strain fields demonstrated close agreement with noninvasive strain estimation technique targets. Interestingly, our model predicted increased posterior leaflet tethering after URA in two recurrent patients, which is the likely driver of long-term MV repair failure. In summary, the present pipeline was able to predict postoperative outcomes from pre-operative clinical data alone. This approach can thus lay the foundation for optimal tailored surgical planning for more durable repair, as well as development of mitral valve digital twins. © 2023 American Society of Mec

关键词： Geometry

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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks

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Physical Review Applied 2022年第6期17卷 064025-064025页

作者： Henri Tertilt Jesse Bakker Marlon Becker Bram de Wilde Indrek Klanberg Bernard J. Geurts Wilfred G. van der Wiel Andreas Heuer Peter A. Bobbert Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany NanoElectronics Group University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) University of Twente Enschede Netherlands Center for Computational Energy Research (CCER) Eindhoven University of Technology Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Department of Applied Physics Eindhoven University of Technology Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic in dopant network processing units. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics and artificially evolved basic Boolean logic gates in very good agreement with experiment. The simulations provide unique insights into the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries.

关键词： Doping effects Hopping transport Neuromorphic computing Devices for digital logic, storage & processing Disordered systems Doped semiconductors Genetic algorithm Monte Carlo methods

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Mechanism for reconfigurable logic in disordered dopant networks

Research Square

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research Square 2021年

作者： Tertilt, Henri Bakker, Jesse Becker, Marlon de Wilde, Bram Klanberg, Indrek Geurts, Bernard J. van der Wiel, Wilfred G. Heuer, Andreas Bobbert, Peter A. Institut für physikalische Chemie Westfälische Wilhelms-Universität Münster Münster Germany University of Twente Enschede Netherlands Multiscale Modeling and Simulation Group Faculty of Electrical Engineering Mathematics and Computer Science Center for Brain-Inspired Nano Systems University of Twente Enschede Netherlands Eindhoven University of Technology Eindhoven5600 MB Netherlands Fachbereich Physik Westfälische Wilhelms-Universität Münster Münster Germany Molecular Materials and Nanosystems Group Center for Computational Energy Research Eindhoven University of Technology Eindhoven Netherlands

We present an atomic-scale mechanism based on variable-range hopping of interacting charges enabling reconfigurable logic and nonlinear classification tasks in dopant network processing units in silicon. Kinetic Monte Carlo simulations of the hopping process show temperature-dependent current-voltage characteristics, artificial evolution of basic Boolean logic gates, and fitness-dependent gate abundances in striking agreement with experiment. The simulations provide unique insights in the local electrostatic potential and current flow in the dopant network, showing subtle changes induced by control voltages that set the conditions for the logic operation. These insights will be crucial in the systematic further development of this burgeoning technology for unconventional computing. The establishment of the principles underlying the logic functionality of these devices encourages the exploration and utilization of the same principles in other materials and device geometries. © 2021, CC BY.

关键词： Monte Carlo methods

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A dynamical model for the calcineurin-NFATc signaling pathway and muscle fiber shifting

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PAMM 2021年第1期20卷

作者： Yesid Villota-Narvaez Angélica Ramírez-Martínez Diego Garzón-Alvarado Oliver Röhrle Biomimetics Laboratory and Numerical Methods and Modeling Research Group (GNUM) Instituto de Biotecnología (IBUN) Universidad Nacional de Colombia Bogotá Colombia Stuttgart Centre for Simulation Sciences (SC SimTech) Pfaffenwaldring 5a 70569 Stuttgart Germany Computational modeling of natural systems Research Group (COMMONS) Mechanical Engineering Department Universidad Central Bogota Colombia Institute for Modeling and Simulation of Biomechanical Systems University of Stuttgart Pfaffenwaldring 5a 70569 Stuttgart Germany

Training exercise produces skeletal muscle adaptation: at the organ scale, as anatomical changes; and at the myofiber scale, as mitochondrial and protein type content. The protein content of a myofiber is controlled by the calcineurin-NFATc signaling pathway: exercise triggers the pathway, and its final product is the translocation of dephosphorylated NFATc to the nucleus. Once in the nucleus, NFATc controls the state of the gene program to encode the slow or the fast fiber type characteristics. In the long term, the adaptation of the fiber type characteristics produces a shift in muscle fiber type: an increase in the number of myofibers of the fast type (which means that myofibers of the slow type shift to fast type) is related to force production; and an increase in the number of myofibers of the slow type (myofibers of the fast type shift to slow type) is related to fatigue resistance. These macroscopic features, i.e. force production and fatigue resistance, are the main target of most training protocols; however, little attention is focused on the limitations imposed by the fiber distribution of muscles at the organ scale. Based on the calcineurin-NFATc signaling pathway, we represented an exercise stimulus by a cytosolic calcium signal, and simulated the dynamics of NFATc in the nucleus. In this contribution, we present a dynamical model for the calcineurin-NFATc signaling pathway to describe the time course of dephosphorilated NFATc in the nucleus. We used an experimental report of a continuous stimulation pattern for calibration, and an experimental report of a pulsed stimulation pattern for comparison; we obtained a good agreement between the simulations and the experimental measurements.

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A Novel computational Protocol to Support Transfemoral Prosthetic Alignment Procedure Using Machine Learning Techniques

SSRN

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SSRN 2022年

作者： Cárdenas, Andres M. Plata, Jesús A. Font-Llagunes, Josep M. Hernández, Alher M. Uribe, Juliana Bioinstrumentation and Clinical Engineering Research Group GIBIC Bioengineering Department Engineering Faculty Universidad de Antioquia UdeA Calle 70 No. 52-21 Medellín Colombia Research Group in Computational Modeling and Simulation GIMSC Engineering Faculty Universidad de San Buenaventura Carrera 56C No. 51-110 Medellín Colombia Biomechanical Engineering Lab Department of Mechanical Engineering Research Centre for Biomedical Engineering Universitat Politècnica de Catalunya Diagonal 647 Barcelona08028 Spain Institut de Recerca Sant Joan de Déu Santa Rosa 39‑57 Esplugues de Llobregat08950 Spain Grupo Rehabilitación en Salud Sede de Investigación Universitaria Universidad de Antioquia UdeA Calle 70 No. 52-21 Medellín Colombia Mahavir Kmina Artificial Limb Center Carrera 54 No. 79 AA Sur 40 Bodegas La Troja Local 116 La Estrella Colombia

Background: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal *** objective: To assist the prosthetist during the assessment of prosthetic alignment using a new computational protocol based on machine ***: Sixteen transfemoral amputees were recruited for training and validation of the alignment protocol. Four misalignments and one nominal alignment were performed. Eleven prosthetic limb ground reaction force parameters were recorded. A support vector machine with a Gaussian kernel radial basis function and a Bayesian regularization neural network were trained to predict the alignment condition, as well as the magnitude and angle of required to align the prosthesis correctly. The alignment protocol was validated by one junior and one senior prosthetist during the prosthetic alignment of two transfemoral ***: The support vector machine-based model detected the nominal alignment 92.6% of the time. The neural network recovered 94.11% of the angles needed to correct the prosthetic misalignment with a fitting error of 0.51°. During the validation of the alignment protocol, the computational models and the prosthetists agreed on the alignment assessment. The gait quality evaluated by the prosthetists reached a satisfaction level of 8/10 for the first amputee and 9.6/10 for the second ***: The new computational prosthetic alignment protocol is a tool that helps the prosthetist during the prosthetic alignment procedure thereby decreasing the likelihood of gait deviations and musculoskeletal diseases associated with misalignments and consequently improving the amputees-prosthesis adherence.

关键词： Alignment

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26th Annual computational Neuroscience Meeting (CNS*2017) of the Organization for computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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