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相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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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
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
530
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

620
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
620
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

642
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...
642
Work and Energy for Variable Forces01:10

Work and Energy for Variable Forces

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When an object is acted upon by a variable force, the amount of work done and the change in energy of the object can be more complex to calculate compared to when a constant force is applied. Work is the product of force and displacement, while energy is the capacity of a system to do work. When a constant force is applied to an object, the work done can be calculated as the product of the force and the distance moved in the direction of the force. However, when a variable force is applied, the...
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Quantifying Work02:30

Quantifying Work

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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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一个多重控制的托福利驱动的自适应量子神经网络模型,用于云环境中的动态工作负载预测.

Ishu Gupta, Deepika Saxena, Ashutosh Kumar Singh

    IEEE transactions on pattern analysis and machine intelligence
    |May 16, 2024
    PubMed
    概括

    本研究介绍了一种新的多重控制托福驱动的自适应量子神经网络 (MCT-AQNN),用于云计算工作负载预测. MCT-AQNN模型显著提高了预测动态和波动性云工作负载的准确性.

    科学领域:

    • 云计算 云计算 云计算 云计算
    • 人工智能的人工智能
    • 量子计算是一种量子计算.

    背景情况:

    • 云计算在动态资源扩展,负载平衡和功耗方面面临着挑战.
    • 准确的工作负载预测对于解决这些云计算挑战至关重要.
    • 现有的工作负载预测方法与动态云工作负载的高变异性作斗争.

    研究的目的:

    • 为准确的云工作负载预测引入一种新型模型.
    • 为了解决当前处理挥发性云工作负载的方法的局限性.
    • 通过量子学习优化探索,适应和利用能力.

    主要方法:

    • 介绍了一种新的多重控制托福利驱动的自适应量子神经网络 (MCT-AQNN) 模型.
    • 量子计算的适应性与机器学习算法相结合.
    • 在量子神经网络 (QNN) 的隐藏和输出层中使用多重控制托福利 (MCT) 门.
    • 为训练QNN而开发了一种统一自适应量子机器学习 (UAQL) 算法.

    主要成果:

    • MCT-AQNN模型在工作负载预测方面表现出卓越的性能.
    • 在四个现实世界的基准数据集上进行的实验显示了显著的准确性改进.

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  • 与最先进的方法相比,拟议的模型达到高达32%-96%的准确性.
  • 结论:

    • MCT-AQNN模型为复杂和弹性的云工作负载预测提供了有效的解决方案.
    • 量子增强的机器学习从动态工作负载提供了更精确的相关性.
    • 这种新的方法提高了云环境中的学习能力和预测准确性.