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

Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

101
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
101
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Correlation and Regression00:53

Correlation and Regression

1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
Survival Tree01:19

Survival Tree

66
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...
66
Classification of Signals01:30

Classification of Signals

418
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
418

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

Agentic and LLM-Based Multimodal Anomaly Detection: Architectures, Challenges, and Prospects.

Sensors (Basel, Switzerland)·2026
Same author

Macrophages in human atherosclerotic plaques in the era of single-cell and spatial transcriptomics.

ImmunoHorizons·2026
Same author

Editorial: Metabolism in the tumour microenvironment: implications for pathogenesis and therapeutics.

Frontiers in immunology·2026
Same author

The evolution of digital twins from reactive to agentic systems.

Nature computational science·2026
Same author

Postprandial Responses to Animal Products with Distinct Fatty Acid and Amino Acid Composition Are Diet-Dependent.

Nutrients·2025
Same author

Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control.

Scientific reports·2025
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: Jun 12, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

使用多变量数据进行实时预测状态监测.

Daniel Menges, Adil Rasheed, Harald Martens

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 26, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了使用热成像的实时状态监测和状态预测框架. 该方法提高了复杂系统中预测性维护和异常检测的准确性和效率.

    更多相关视频

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K

    相关实验视频

    Last Updated: Jun 12, 2025

    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    16.8K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    19.9K

    科学领域:

    • 工程 工程师 工程师 工程师
    • 数据科学数据科学数据科学
    • 机器学习 机器学习

    背景情况:

    • 有效的条件监测和状态预测对于运营效率和安全至关重要.
    • 高维数据在提取有意义的洞察力以进行预测性维护方面存在挑战.
    • 现有的方法往往缺乏复杂系统的实时适用性和稳定性.

    研究的目的:

    • 开发一个强大的算法框架,用于实时状态监测和状态预测,使用多变量数据.
    • 提高状态监测和状态预测的准确性,效率和稳定性.
    • 为了实现实时异常检测和风险评估.

    主要方法:

    • 使用正确直角分解 (POD) 的组合来进行特征提取和维度缩小.
    • 采用最佳采样位置 (OSL) 来识别最有信息的数据点.
    • 集成动态模式分解 (DMD) 用于状态预测和支持向量回归 (SVR) 用于数据归算.

    主要成果:

    • 在船舶发动机的热成像数据上表现出有效性.
    • 实现了显著的尺寸缩小,并确定了关键的系统动态.
    • 启用了实时适用性与较低的计算资源需求.
    • 成功实施无监督异常检测与状态预测相结合.

    结论:

    • 拟议的框架为预测性状况监测提供了一个强大的实时解决方案.
    • 整合POD,OSL和DMD增强了复杂的多变量数据的分析.
    • 该系统提供了实时风险评估和异常预测的能力.