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相关概念视频

Prediction Intervals01:03

Prediction Intervals

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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. 
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Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

668
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

129
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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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...
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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基于张量优化,对零件的间歇性需求进行稳健的间隔预测.

Kairong Hong1, Yingying Ren1, Fengyuan Li1

  • 1China Railway Tunnel Group, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括

本研究引入了一种新的可靠间隔预测方法,用于间歇性备件需求. 张量优化方法有效地捕捉趋势并提高准确性,为售后市场服务提供可靠的预测.

关键词:
需求预测 需求预测间歇性的时间序列.间隔预测 间隔预测张量分解分解 张量分解时间序列预测时间序列预测

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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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科学领域:

  • 运营研究 运营研究
  • 数据科学数据科学数据科学
  • 制造业 工程 制造工程

背景情况:

  • 大型制造企业的售后服务依赖于准确的备件需求预测,用于库存和质量管理.
  • 间歇性的备件需求表现出随机波动和异常值,挑战了传统的时间序列预测方法.
  • 现有的方法很难捕捉进化模式,并为杂的间歇性数据提供可靠的预测.

研究的目的:

  • 为售后零件需求的间歇时间序列提出一个强大的间隔预测方法.
  • 为了应对需求数据中随机波动,异常值和间歇性特征的挑战.
  • 为了提高备件需求预测对售后市场服务的可靠性和准确性.

主要方法:

  • 一个序列平滑网络使用张量分解 (塔克分解) 和堆叠的自动编码器来识别需求数据.
  • 一种交替优化算法,可以从间歇序列中提取进化趋势并优化特征表示.
  • 一个具有动态更新的适应间隔预测算法,用于点和间隔预测.

主要成果:

  • 拟议的张量优化方法有效地捕捉了间歇序列的进化趋势,优于传统方法.
  • 使用现实世界的售后数据证明了预测准确度的提高,特别是对于小样本间歇序列,使用现实世界的售后数据.
  • 该方法提供可靠,弹性预测间隔,减轻因数据扭曲引起的问题.

结论:

  • 基于张量优化的稳健间隔预测方法为间歇性备件需求的准确和可靠预测提供了一个新的解决方案.
  • 这种方法提高了智能规划和决策在实际的维护和售后服务.
  • 该方法处理噪音和间歇性数据的能力在需求预测方面取得了重大进展.