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相关概念视频

Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Multi-input and Multi-variable systems01:22

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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...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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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...
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EasyDGL:编码,训练和解释用于连续时间动态图形学习.

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    此摘要是机器生成的。

    简单的DGL模型使用时间点过程 (TPP) 和注意力在连续时间中的动态图. 这种可解释的管道通过量化学习的频率内容,在时间条件下的预测任务中实现了卓越的性能.

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    科学领域:

    • 图形神经网络的神经网络
    • 动态系统建模 动态系统建模
    • 时间序列分析时间序列分析

    背景情况:

    • 动态图表在现实世界的场景中很普遍,需要灵活的连续时间建模.
    • 现有的方法往往缺乏可解释性或与合的时空动态斗争.

    研究的目的:

    • 介绍EasyDGL,一个易于使用的管道,用于连续时间建模动态图.
    • 为了提高模型适配能力和空间时间图形动态的可解释性.
    • 为量化学习频率内容对预测任务的影响提供一个框架.

    主要方法:

    • 一个时间点过程 (TPP) 调节了持续时间图形动态与边缘事件的注意力架构.
    • 一个有原则的损失函数,它结合了任务无意识的TPP后面最大化和任务意识的掩盖,用于动态图表预测.
    • 在图表中的可扩展的基于扰动的分析 里埃域 模型可解释性.

    主要成果:

    • 在公共基准中,EasyDGL在时间条件下的预测任务上表现优异.
    • 该管道有效量化了从不断变化的图形数据中学习的频率内容的预测能力.
    • 在动态图形建模中获得了强大的拟合能力和可解释性.

    结论:

    • EasyDGL为连续时间动态图形建模提供了灵活和可解释的解决方案.
    • 该框架促进了对模型如何在不断演变的图表中利用频率信息的理解.
    • 在动态链接预测和节点分类等任务上实现强大的性能.