Search research articles

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

THe Biom: a platform for visualization and exploration of cancer transcriptomic biomarkers identified by robust feature selection.

Bioinformatics advances·2026

Same author

Guaranteed Visibility in Scatterplots with Tolerance.

IEEE transactions on visualization and computer graphics·2023

Same author

Toward Efficient Deep Learning for Graph Drawing (DL4GD).

IEEE transactions on visualization and computer graphics·2022

Same author

Visualizing Food-Drug Interactions in the Thériaque Database.

Studies in health technology and informatics·2021

Same author

GSAn: an alternative to enrichment analysis for annotating gene sets.

NAR genomics and bioinformatics·2021

Same author

A 750 K Photocharge Linear Full Well in a 3.2 μm HDR Pixel with Complementary Carrier Collection.

Sensors (Basel, Switzerland)·2018

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Jul 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

通过随机梯度下降的重叠去除与 (外) 形状意识.

Loann Giovannangeli, Frederic Lalanne, Romain Giot

IEEE transactions on visualization and computer graphics

|January 9, 2024

概括

本研究介绍了对2D数据可视化改进的重叠删除 (OR) 算法. 新方法有效地消除了形状重叠,同时保持了数据拓和可读性.

科学领域:

计算机科学计算机科学
数据可视化数据可视化
计算几何学的计算几何学

背景情况:

2D可视化中的数据点通常以形状表示,但不适当的尺寸/形状选择可能会导致重叠,模糊信息.
覆盖删除 (OR) 算法是后处理解决方案,以确保图形元素准确地表示底层数据.
保存原始数据布局的拓对于有效的可视化探索至关重要.

研究的目的:

在2D可视化中扩展FORBID算法进行重叠删除 (OR).
开发一种形状感知OR算法 (SORDID),能够处理多边形状.
为了实现无重叠的布局,平衡紧性和原始布局的保存.

主要方法:

用随机梯度下降来建模OR作为关节应力和缩放优化问题.
开发SORDID,这是FORBID算法的一个形状意识的适应,用于多边形数据点.
通过使用质量指标,将拟议的方法与最新的算法进行比较.

主要成果:

扩展的FORBID和SORDID算法有效地消除2D可视化中的重叠.
这些方法在数据紧性和保留原始布局结构之间取得了平衡.
评估表明与现有的最先进的OR算法相比,性能优越.

更多相关视频

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

相关实验视频

Last Updated: Jul 6, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

结论:

拟议的OR算法在可视化清晰度和数据探索方面提供了显著的改进.
SORDID提供了一个强大的解决方案,用于删除随意的多边形形状的重叠.
这些进步提高了复杂的2D数据可视化的可靠性和可解释性.