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Particle swarm optimization with local search for height-map surface reconstruction from point clouds in reverse engineering

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Neural Computing and Applications 2024年 1-20页

作者： Gálvez, Akemi Fister, Iztok Deb, Suash Fister, Iztok Iglesias, Andrés Dept. of Applied Mathematics and Computational Sciences E.T.S.I. Caminos Canales y Puertos University of Cantabria Avda. de los Castros s/n Santander39005 Spain Faculty of Pharmaceutical Sciences Toho University 2-2-1 Miyama Funabashi274-8510 Japan Faculty of Electrical Engineering and Computer Science University of Maribor Koroska cesta 34 Maribor2000 Slovenia IT & Educational Consultant Ranchi India Distinguished Professorial Associate Decision Sciences and Modeling Program Victoria University Melbourne Australia

Surface reconstruction is a classical process in industrial engineering and manufacturing, particularly in reverse engineering, where the goal is to obtain a digital model from a physical object. For that purpose, the real object is typically scanned or digitized and the resulting point cloud is then fitted to mathematical surfaces through numerical optimization. The choice of the approximating functions is crucial for the accuracy of the process. Real-world objects such as manufactured workpieces often require complex nonlinear approximating functions, which are not well suited for standard numerical methods. In a previous paper presented at the ISMSI 2023 conference, we addressed this issue by using manually selected approximation functions via optimization through the cuckoo search algorithm with Lévy flights. Building upon that work, this paper presents an enhanced and extended method for surface reconstruction by using height-map surfaces obtained through a combination of exponential, polynomial and logarithmic functions. A feasible approach for this goal is to consider continuous bivariate distribution functions, which ensures consistent models along with good mathematical properties for the output shapes, such as smoothness and integrability. However, this approach leads to a difficult multivariate, constrained, multimodal continuous nonlinear optimization problem. To tackle this issue, we apply particle swarm optimization, a popular swarm intelligence technique for continuous optimization. The method is hybridized with a local search procedure for further improvement of the solutions and applied to a benchmark of 15 illustrative examples of point clouds fitted to different surface models. The performance of the method is analyzed through several computational experiments. The numerical and graphical results show that the method is able to recover the shape of the point clouds accurately and automatically. Furthermore, our approach outperforms other alternati

关键词： Particle swarm optimization (PSO)

Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level Optimization Objective

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Improved Accuracy of Cardiac Tissue-Level Simulations by Con...

Annual International Conference of the Ieee engineering in Medicine and Biology Society (eMBC)

作者： e. Pouranbarani L. A. Berg R. S. Oliveira R. W. dos Santos A. Nygren Department of Electrical and Computer Engineering University of Calgary Alberta Canada Department of Computer Science and the Graduate Program of Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil Department of Computer Science University of São João del-Rei São João del-Rei Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Cardiac cellular models are utilized as the building blocks for tissue simulation. One of the imprecisions of conventional cellular modeling, especially when the models are used in tissue-level modeling, stems from the mere consideration of cellular properties (e.g., action potential shape) in parameter tuning of the model. In our previous work, we put forward an accurate framework in which membrane resistance (R m ) reflecting inter-cellular characteristics, i.e., electrotonic effects, was considered alongside cellular features in cellular model fitting. This paper, for the first time, examines the hypothesis that considering R m as an additional optimization objective improves the accuracy of tissue-level modeling. To study this hypothesis, after cellular-level optimization of a well-known model, source-sink mismatch configurations in a 2-dimensional model are investigated. The results demonstrate that including R m in the optimization protocol yields a substantial improvement in the relative error of the critical transition border which is defined as the minimum window size between source and sink that wave propagates. Model developers can utilize the proposed concept during parameter tuning to increase the accuracy of models.

关键词： Mathematical model Optimization computational modeling Resistance Tuning Fitting Protocols

Modelling the Human Immune System Response to the ChAdOx1 nCoV-19 Vaccine

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Modelling the Human Immune System Response to the ChAdOx1 nC...

2021 Ieee International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xavier, Maicom Peters Pompei, Lara T. De O. Vieira, Ana Carolina G. De Paula, Matheus A. M. Bonin, Carla R. B. Reis, Ruy F. Pigozzo, Alexandre B. Quintela, Barbara De M. Dos Santos, Rodrigo W. Lobosco, Marcelo Federal University of Juiz de Fora Graduate Program on Computational Modeling Juiz de Fora Brazil Federal University of Juiz de Fora FISIOCOMP Juiz de Fora Brazil Federal University of Juiz de Fora GET-EngComp Juiz de Fora Brazil IF Sudeste MG Cataguases Advanced Campus Cataguases Brazil Federal University of Juiz de Fora Computer Science Department Juiz de Fora Brazil Federal University of São João Del-Rei Computer Science Department São João Del-Rei Brazil

ISBN: (纸本)9781665401265

About seven months after the start of the COVID-19 pandemic, the first vaccine against the disease was approved for emergency use. Since then, twenty-three more vaccines have been approved, while more than three hundred are in development. Despite being one of the fastest vaccines ever created, several questions about it remain open. computational models can be useful to answer some of these questions. This paper aims to evaluate whether a computer model previously used to reproduce the effects of the yellow fever vaccine in the body is also capable of reproducing the effects of a distinct vaccine: ChAdOx1 nCoV-19. Preliminary results show that the model is a promising tool to achieve this goal since it was able to reproduce the antibody curves observed in individuals vaccinated with ChAdOx1 nCoV-19. © 2021 Ieee.

关键词： Coronavirus

Applying the MVC eA-IRPM to reporting-guidelines in medicine: A strategy that is a web system 4

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Applying the MVC EA-IRPM to reporting-guidelines in medicine...

4th Ieee eMBS International Conference on Biomedical and Health Informatics, BHI 2017

作者： Letouze, Patrick De Souza, J.I.M. Silva, Valeria Martins Climaco, Glaubos Chagas, F.B. Ishihara, Joao Y. Graduate Program of Computational Modeling of Systems Universidade Federal Do Tocantins Quadra 109 Norte Avenida NS-15 Palmas77001-090 Brazil Graduate Program of Systems' Engineering and Computation - PESC Universidade Federal Do Rio de Janeiro Rio de Janeiro Brazil Department of Informatics at the Universidade Federal Do Maranh/ Ao - UFMA Brazil Faculty of Computer Science Faculdade Pitagoras Imperatriz Imperatriz Brazil Faculty of Electrical Engineering Universidade de Brasília Brasília Brazil

ISBN: (纸本)9781509041794

Reporting-Guidelines in Medicine play an important role in promoting the quality of reports in health-related research. For instance, a poorly reported research may induce misinterpretation and inappropriate clinical settings. Basically, three major issues regarding Reporting-Guidelines in Medicine has to be addressed: (1) the existence of lots of reporting guidelines covering a broad spectrum of medical research increases the difficulties when reporting a medical research because it may not be clear which guidelines are applicable, or if all related guidelines are considered;(2) most guideline developers have not described how their guidance was developed, consequently tracking guidelines and features for evaluation, or even for correcting, may be not possible;(3) few guideline developers have evaluated the effectiveness of their guidance, then the real value that the guideline brings to reporting the research is not assured. Hence, we propose a solution to these three issues based on the Model-View-Controller evolutionary Acquisition Interdisciplinary Research Project Management (MVC eA-IRPM) that is simultaneously a strategy and a web system for the development and management of reporting guidelines. © 2017 Ieee.

关键词： Clinical research

executive Summary of Dagstuhl Seminar on Structured Decompositions and efficient Algorithms (08492)

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Executive Summary of Dagstuhl Seminar on Structured Decompos...

Structured Decompositions and efficient Algorithms 2008

作者： Dahlke, Stephan Daubechies, Ingrid elad, M. Kutyniok, Gitta Teschke, Gerd Philipps-Universität Marburg FB12 Mathematik und Informatik Hans-Meerwein Straße Lahnberge Marburg35032 Germany Princeton University Program in Applied and Computational Mathematics Fine Hall Washington Road PrincetonNJ08544-1000 United States Technion Computer Science Department Haifa Technion City32000 Israel Universität Osnabrück Institute of Mathematics Osnabrück49069 Germany Hochschule Neubrandenburg University of Applied Sciences Institute for Computational Mathematics in Science and Technology Brodaer Str. 2 Neubrandenburg17033 Germany

New emerging technologies such as high-precision sensors or new MRI machines drive us towards a challenging quest for new, more effective, and more daring mathematical models and algorithms. Therefore, in the last few years researchers have started to investigate different methods to efficiently represent or extract relevant information from complex, high dimensional and/or multimodal data. efficiently in this context means a representation that is linked to the features or characteristics of interest, thereby typically providing a sparse expansion of such. Besides the construction of new and advanced ansatz systems the central question is how to design algorithms that are able to treat complex and high dimensional data and that efficiently perform a suitable approximation of the signal. One of the main challenges is to design new sparse approximation algorithms that would ideally combine, with an adjustable tradeoff, two properties: a provably good 'quality' of the resulting decomposition under mild assumptions on the analyzed sparse signal, and numerically efficient design. The topic is driven by applications as well as by theoretical questions. Therefore, the aim of this seminar was to bring together a good mixture of scientists with different backgrounds in order to discuss recent progress as well as new challenging perspectives. In particular, it was intended to strengthen the interaction of mathematicians and computer scientists. © 2009 Dagstuhl Seminar Proceedings. All Rights Reserved.

关键词： Clustering algorithms

From FAIR to CURe: Guidelines for computational Models of Biological Systems

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew e. May, elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas e. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURe principles, emphasizing that models should be Credible, Understandable, Reproducible, and extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURe, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Homogenisation for the monodomain model in the presence of microscopic fibrotic structures

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arXiv 2020年

作者： Lawson, Brodie A.J. dos Santos, Rodrigo Weber Turner, Ian W. Bueno-Orovio, Alfonso Burrage, Pamela Burrage, Kevin Centre for Data Science Queensland University of Technology QLD Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers Queensland University of Technology QLD Australia Graduate Program on Computational Modeling Universidade de Juiz de Fora Minas Gerais Brazil Department of Computer Science University of Oxford Oxfordshire United Kingdom

MSC Codes 35B27computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes it particularly challenging to incorporate structural heterogeneities on small spatial scales, preventing a full understanding of the critical arrhythmogenic effects of conditions such as cardiac fibrosis. In this work, we explore the technique of homogenisation by volume averaging for the inclusion of non-conductive micro-structures into larger-scale cardiac meshes with minor computational overhead. Importantly, our approach is not restricted to periodic patterns, enabling homogenised models to represent, for example, the intricate patterns of collagen deposition present in different types of fibrosis. We first highlight the importance of appropriate boundary condition choice for the closure problems that define the parameters of homogenised models. Then, we demonstrate the technique’s ability to correctly upscale the effects of fibrotic patterns with a spatial resolution of 10 µm into much larger numerical mesh sizes of 100-250 µm. The homogenised models using these coarser meshes correctly predict critical pro-arrhythmic effects of fibrosis, including slowed conduction, source/sink mismatch, and stabilisation of re-entrant activation patterns. As such, this approach to homogenisation represents a significant step towards whole organ simulations that unravel the effects of microscopic cardiac tissue heterogeneities. © 2020, CC BY.

关键词： Heart

Analyzing dominance move (MIP-DoM) indicator for multi- And many-objective optimization

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arXiv 2020年

作者： do Val Lopes, Claudio Lucio Martins, Flávio Vinícius Cruzeiro Wanner, elizabeth Fialho Deb, Kalyanmoy Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

—Dominance move (DoM) is a binary quality indicator that can be used in multi-objective and many-objective optimization to compare two solution sets obtained from different algorithms. The DoM indicator can differentiate the sets for certain important features, such as convergence, spread, uniformity, and cardinality. DoM does not use any reference, and it has an intuitive and physical meaning, similar to the -indicator, and calculates the minimum total move of members of one set so that all elements in another set are to be dominated or identical to at least one member of the first set. Despite the aforementioned properties, DoM is hard to calculate, particularly in higher dimensions. There is an efficient and exact method to calculate it in a bi-objective case only. This work proposes a novel approach to calculate DoM using a mixed integer programming (MIP) approach, which can handle sets with three or more objectives and is shown to overcome the -indicator’s information loss. experiments, in the bi-objective space, are done to verify the model’s correctness. Furthermore, other experiments, using 3, 5, 10, 15, 20, 25 and 30-objective problems are performed to show how the model behaves in higher-dimensional cases. Algorithms, such as IBeA, MOeA/D, NSGA-III, NSGA-II, and SPeA2 are used to generate the solution sets (however any other algorithms can also be used with the proposed MIP-DoM indicator). Further extensions are discussed to handle certain idiosyncrasies with some solution sets and also to improve the quality indicator and its use for other situations. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

The Fisher Geometry and Geodesics of the Multivariate Normals, without Differential Geometry

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arXiv 2023年

作者： Lawson, Brodie A.J. Burrage, Kevin Mengersen, Kerrie Santos, Rodrigo Weber dos Centre for Data Science Queensland University of Technology Australia School of Mathematical Sciences Queensland University of Technology Australia ARC Centre of Excellence for Plant Success in Nature and Agriculture Australia Department of Computer Science University of Oxford United Kingdom Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil

Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling measure of statistical distance, and paths of shorter distance improve sampling techniques that leverage a sequence of distributions in their operation. Unfortunately, even for a distribution as generally tractable as the multivariate normal distribution, this information geometry proves unwieldy enough that closed-form solutions for shortest-distance paths or their lengths remain unavailable outside of limited special cases. In this review we present for general statisticians the most practical aspects of the Fisher geometry for this fundamental distribution family. Rather than a differential geometric treatment, we use an intuitive understanding of the covariance-induced curvature of this manifold to unify the special cases with known closed-form solution and review approximate solutions for the general case. We also use the multivariate normal information geometry to better understand the paths or distances commonly used in statistics (annealing, Wasserstein). Given the unavailability of a general solution, we also discuss the methods used for numerically obtaining geodesics in the space of multivariate normals, identifying remaining challenges and suggesting methodological *** Codes 62B11 (primary), 62-02, 62-08 (secondary) © 2023, CC BY.

关键词： Geometry