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相关概念视频

Multimachine Stability01:25

Multimachine Stability

230
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
230
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

258
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,...
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What is a Mode?01:07

What is a Mode?

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The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

127
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...
127
Reinforcement Schedules01:24

Reinforcement Schedules

242
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
242
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
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...
150

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相关实验视频

Updated: Sep 13, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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一个新的动态调度模型用于多模式方法的应用.

Zineb Elqabli1, Oulaid Kamach2, Abdelhakim Khatab3

  • 1Innovative Technologies Laboratory, National School of Applied Sciences Tangier, Tangier, Morocco. Zineb.elqabli@etu.uae.ac.ma.

Scientific reports
|July 31, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了多模式系统的任务调度优化模型,提高了它们的弹性. 该模型通过动态调整工作时间表来最大限度地减少不稳定环境中的系统停机时间.

关键词:
动态调度时间表这使得西班牙.建模建模模型是什么多模式方法方法多模式方法.优化优化 优化优化

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
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相关实验视频

Last Updated: Sep 13, 2025

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科学领域:

  • 运营研究 运营研究
  • 工业工程 工业工程 工业工程
  • 系统弹性工程 系统弹性工程

背景情况:

  • 多模式系统是为了在动态环境中提供灵活性和可靠性而设计的,在正常和退化条件下运行.
  • 在这些系统中,由于波动性和意想不到的事件,工作安排面临挑战,影响系统的整体性能和稳定性.

研究的目的:

  • 为多模式系统提出一个新的工作动态调度优化模型.
  • 提高多模式系统的弹性和稳定性,以应对意想不到的中断.
  • 为了最大限度地减少多模式系统的任务调度中的制作时间.

主要方法:

  • 混合整数线性编程 (MILP) 优化问题的制定.
  • 包含限制,捕捉多模式系统的特定行为和特征.
  • 从现实场景中利用定量数据,包括处理时间,工作细节,操作,机器分配和剩余使用寿命 (RUL) 预测.

主要成果:

  • 通过各种实验证明拟议模型的有效性.
  • 验证模型在优化多模式系统工作时间表中的准确性.
  • 在模拟波动条件下增强系统弹性和稳健性的证据.

结论:

  • 拟议的MILP模型有效地优化了多模式系统中的工作安排.
  • 动态调度方法提高了系统应对意外事件的稳定性.
  • 该研究提供了一种经过验证的方法,用于在具有挑战性的环境中提高多模式系统的性能.