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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

388
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
388
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Classification of Signals01:30

Classification of Signals

549
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...
549
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

582
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
582
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

97
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
97
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

184
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
184

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

Data on air temperature, relative humidity, and dew point in three housing modes in a building in a hot and humid area of Douala, Cameroon.

Data in brief·2025
Same author

Improved exponential smoothing grey-holt models for electricity price forecasting using whale optimization.

MethodsX·2024
Same author

Assessing the severity of thermal discomfort in a building in the course of hot and humid climate.

F1000Research·2024
Same author

Comparison and classification of photovoltaic system architectures for limiting the impact of the partial shading phenomenon.

Heliyon·2024
Same author

A new theoretical approach to determine the air outlet temperature of an air-to-ground heat exchanger.

MethodsX·2024
Same author

Dataset for studying the average monthly change in ground temperature in an equatorial zone during the dry season.

Data in brief·2024

相关实验视频

Updated: Jul 24, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

使用波形变换和灰色多变量卷积模型的新型预测方法.

Flavian Emmanuel Sapnken1,2,3, Marius Tony Kibong1,2,3, Jean Gaston Tamba1,2,3

  • 1Laboratory of Technologies and Applied Science, IUT Douala, P.O. Box 8698, Douala, Cameroon.

MethodsX
|July 7, 2023
PubMed
概括

本研究引入了一种优化的灰色多变量卷积模型 (ODGMC(1,N)) 以实现更准确的电力需求预测. 改进后的模型提高了预测可靠性和稳定性,性能优于现有方法.

关键词:
卷积积分是什么意思 卷积积分是什么意思预测电力 预测电力灰色系统是一个灰色系统.最优的离散灰色多变量卷积模型.波段变换的波段变换是什么

更多相关视频

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

相关实验视频

Last Updated: Jul 24, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

科学领域:

  • 能源经济学 能源经济学
  • 预测科学 预测科学
  • 应用数学 应用数学 应用数学

背景情况:

  • 准确的电力消耗预测对于能源管理和规划至关重要.
  • 现有的灰色多变量卷积模型 (GMC(1,N)) 在准确性和稳定性方面存在局限性.

研究的目的:

  • 提出一个新的,优化的离散灰色多变量卷积模型 (ODGMC(1,N)).
  • 提高电力需求预测的准确性和稳定性.
  • 用喀麦隆的年度电力需求数据来验证该模型的性能.

主要方法:

  • 通过将线性校正项纳入GMC的结构,开发了ODGMC(1,N).
  • 采用代技术进行参数估计和累积预测函数计算.
  • 利用波波变换来降低噪声,并从输入数据中提取特征.

主要成果:

  • 该ODGMC(1,N) 模型显示出卓越的预测准确度,平均绝对百分比误差 (MAPE) 为1.74%和根平均平方误差 (RMSE) 为132.16.
  • 与竞争的预测技术相比,该模型显示出更好的可靠性和稳定性.
  • ODGMC ((1,N) 有效地纠正了对预测绩效的线性影响.

结论:

  • 拟议的ODGMC ((1,N) 模型为预测电力需求提供了更精确,更稳定的方法.
  • 该模型能够追踪年度电力需求,使其成为能源部门分析的宝贵工具.
  • 建议在能源消耗预测中进一步应用ODGMC ((1,N).