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相关概念视频

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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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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动态元图卷积循环网络用于异构的时空空间图的预测.

Xianwei Guo1, Zhiyong Yu1, Fangwan Huang1

  • 1College of Computer and Data Science, Fuzhou University, WuLong Jiang North Avenue, University Town, Fuzhou, 350108, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou University, WuLong Jiang North Avenue, University Town, Fuzhou, 350108, China.

Neural networks : the official journal of the International Neural Network Society
|October 25, 2024
PubMed
概括

本研究引入了一个新的动态元图卷积循环网络 (DMetaGCRN) 用于时空图 (STG) 预测. 该框架解决了城市计算中的动态空间依赖性和数据异质性,优于现有的方法.

关键词:
动态图表生成的动态图表生成.异质性 异质性 异质性一个元图形.时间空间图表预测

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科学领域:

  • 时间空间数据挖掘.
  • 城市计算城市计算
  • 图形神经网络是一个神经网络.

背景情况:

  • 时空图 (STG) 预测对于城市计算至关重要.
  • 现有的时空图形神经网络 (STGNNs) 在城市网络中与动态空间依赖和数据异质性作斗争.

研究的目的:

  • 提出一个新的框架,动态元图卷积循环网络 (DMetaGCRN),用于空间时间图的学习.
  • 解决当前STGNN在动态空间依赖性和城市数据异质性方面的局限性.

主要方法:

  • 开发了一个元图生成器,使用传感器信号,历史趋势,周期信息和元节点嵌入来动态创建图形结构.
  • 利用一个内存网络来引导元节点嵌入学习.
  • 设计了一个动态元图卷积循环单元 (DMetaGCRU),用于同时进行空间和时间依赖模型.
  • 使用编码器-解码器架构实现了DMetaGCRN.

主要成果:

  • 超图生成过程有效模拟动态空间依赖性,并捕捉数据异质性.
  • 与最先进的方法相比,DMetaGCRN在四个真实世界城市时空数据集上表现出更高的性能.

结论:

  • 拟议的DMetaGCRN框架在空间时间图表预测方面取得了重大进展.
  • DMetaGCRN有效地处理动态空间依赖性和数据异质性,改进城市计算应用.