Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Manipulation and Analysis01:21

Manipulation and Analysis

23
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
23
Reducing Line Loss01:18

Reducing Line Loss

150
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
150
Levels of Use of a GIS01:29

Levels of Use of a GIS

46
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
46
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

41
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
41
Residual Plots01:07

Residual Plots

4.6K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
4.6K
Survival Tree01:19

Survival Tree

73
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
73

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

Clinical large language model centered on electronic medical records.

NPJ digital medicine·2026
Same author

Accurately fitting biophysical neuron models to experimental voltage data enabled by meta-learning.

Research square·2026
Same author

[Spatiotemporal Patterns of County-level Carbon Emissions in Jiangsu Province Based on Nighttime Light Imagery].

Huan jing ke xue= Huanjing kexue·2026
Same author

Sustained activation of M1 microglia by oligomerized alpha-synuclein released from dopaminergic neuronal damage through CD11b/Src/Erk/NOX2 axis after Paraquat exposure.

Toxicology·2026
Same author

Trends in Meningococcal B Vaccine Uptake Among 16- and 17-Year-Old Adolescents in the United States: An Analysis of the National Immunization Survey-Teen, 2018-2023.

American journal of public health·2026
Same author

Medical Knowledge-Driven Contrastive Learning for Similar Patient Retrieval.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Pattern Matching based reassembly - 3DGPM.

IEEE computer graphics and applications·2026
Same journal

Making Learning Visible: Turning Public Engagement into Evidence for Academic Learning.

IEEE computer graphics and applications·2026
Same journal

LlymX: Multimodal LLM-Augmented XR for Context-Aware Information Access.

IEEE computer graphics and applications·2026
Same journal

Dynamic Gaussian-Based Digital Twin Reconstruction of Articulated Multi-Joint Objects.

IEEE computer graphics and applications·2026
Same journal

Steiner and Poisson Traversal Initializations: Initial Curve Optimization for Geometric Flow-based Surface Filling.

IEEE computer graphics and applications·2026
Same journal

Insight Into the Insight Toolkit.

IEEE computer graphics and applications·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

LossLens:通过损失景观视觉分析对机器学习的诊断.

Tiankai Xie, Jiaqing Chen, Yaoqing Yang

    IEEE computer graphics and applications
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    LossLens可视化了复杂的神经网络损失景观,为模型架构和训练提供了新的见解. 该框架有助于理解本地和全球解决方案空间,以改进机器学习诊断.

    更多相关视频

    Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
    09:44

    Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

    Published on: October 16, 2018

    10.2K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K

    相关实验视频

    Last Updated: Jun 14, 2025

    An R-Based Landscape Validation of a Competing Risk Model
    05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

    2.0K
    Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
    09:44

    Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

    Published on: October 16, 2018

    10.2K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K

    科学领域:

    • 机器学习 机器学习
    • 数据可视化 数据可视化
    • 人工智能的人工智能

    背景情况:

    • 神经网络训练通过损失函数优化参数.
    • 分析损失景观为网络架构和学习提供了洞察力.
    • 全球损失景观的可视化具有挑战性.

    研究的目的:

    • 介绍LossLens,这是一个用于探索神经网络损失景观的视觉分析框架.
    • 允许对损失景观进行多层面分析.
    • 通过集成的全球和本地指标来增强模型诊断.

    主要方法:

    • 开发了一个视觉分析框架LossLens.
    • 综合全球和地方规模的指标.
    • 应用框架用于涉及ResNet-20和物理信息神经网络的案例研究.

    主要成果:

    • LossLens提供了损失景观的全面视觉表示.
    • 框架有助于理解建筑影响 (例如,残余连接).
    • 在可视化物理信息的神经网络参数效应方面证明了实用性.

    结论:

    • LossLens有效地解决了概念化和可视化全球损失景观的挑战.
    • 该框架通过整合多个规模的损失景观信息来增强模型诊断.
    • 对于分析各种神经网络架构和应用程序,LossLens显示出有前途的潜力.