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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

272
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...
272
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Uncertainty: Overview00:59

Uncertainty: Overview

1.5K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.5K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
10.1K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
7.2K
Quantitative Analysis01:12

Quantitative Analysis

1.2K
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
1.2K

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 Binding Affinities of Serum Proteins to Nanoparticles.

Journal of the American Chemical Society·2025
Same author

Consensus on functional assessment of chronic pain in primary care: a Delphi study.

Current medical research and opinion·2021
Same author

[Cost-effectiveness model of a fixed dose combination of solifenacin and tamsulosin for the treatment of LUTS associated with BPH with inadequate response to monotherapy.]

Archivos espanoles de urologia·2018
Same author

Effects of Red Blood Cell Transfusion on Long-Term Disability of Patients with Traumatic Brain Injury.

Neurocritical care·2015
Same author

Fibromyalgia and eating disorders in morbid obesity.

Endocrinologia y nutricion : organo de la Sociedad Espanola de Endocrinologia y Nutricion·2014

相关实验视频

Updated: Jan 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

在不确定性下过程的综合设计和调度:量子神经网络方法.

Lavinia M P Ghilardi1,2, Gabriel D Patrón1,2, Antonio Alcántara3

  • 1Department of Computing, Imperial College London, London SW7 2AZ, U.K.

Industrial & engineering chemistry research
|November 10, 2025
PubMed
概括

这项研究引入了量子神经网络,以优化生产厂的设计和调度,在不确定的电价下. 这种方法降低了计算成本,并允许做出风险意识的决策,提高了工厂的弹性.

更多相关视频

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis
14:11

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Published on: March 29, 2016

27.5K

相关实验视频

Last Updated: Jan 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis
14:11

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Published on: March 29, 2016

27.5K

科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 优化优化 优化优化
  • 机器学习 机器学习

背景情况:

  • 基于电解的生产厂的设计和调度面临着来自电价预测的不确定性.
  • 对于这个问题,传统的二阶段随机编程方法是计算密集的.

研究的目的:

  • 为生产中随机编程的第二阶段开发一个计算效率高的替代模型.
  • 为了实现风险意识优化,使用量子神经网络进行集成设计和调度.

主要方法:

  • 利用量子神经网络作为二阶段随机程序中的第二阶段值函数的替代模型.
  • 将神经网络代理嵌入到优化框架中,以避免基于采样的近似.
  • 融入的有条件风险价值 (CVaR),以及对联合优化的预期.

主要成果:

  • 与样本平均近似相比,以代孕为基础的方法显著减少了计算要求.
  • 整合风险措施 (CVaR) 的优化导致了对电解器和存储的投资增加.
  • 该方法为集成电厂的设计和调度提供了高质量的决策.

结论:

  • 量子神经网络代理提供了一个高效和有效的解决方案,以优化在价格不确定性下的生产.
  • 整合风险措施可以提高工厂的稳定性,以应对不稳定的电力成本.
  • 这种基于替代品的方法推进了可再生能源应用过程系统工程领域的发展.