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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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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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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Streamlines, Streaklines, and Pathlines01:18

Streamlines, Streaklines, and Pathlines

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A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...
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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
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相关实验视频

Updated: Jun 5, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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PDG2Seq:周期动态图到序列模型,用于流量预测.

Jin Fan1, Wenchao Weng2, Qikai Chen3

  • 1Hangzhou Dianzi University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries, Hangzhou, China.

Neural networks : the official journal of the International Neural Network Society
|December 6, 2024
PubMed
概括

本研究引入了一种新的周期动态图到序列模型 (PDG2Seq),用于增强流量预测. PDG2Seq有效地捕捉动态,周期和未来的交通趋势,以实现更准确的智能交通管理.

关键词:
图表 卷积网络 卷积网络图形结构学习学习 图形结构学习周期性特征是周期性的特征.时空模型是一个时空模型.交通流量预测和预测

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相关实验视频

Last Updated: Jun 5, 2025

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科学领域:

  • 智能运输系统 智能运输系统
  • 数据科学是数据科学.
  • 机器学习是机器学习.

背景情况:

  • 当前的流量预测模型往往忽视了流量数据中的动态相关性和周期性特征.
  • 由于依赖于静态的历史数据,现有的方法难以准确地捕捉未来的交通趋势变化.

研究的目的:

  • 提出一个新的周期动态图到序列模型 (PDG2Seq),以提高流量预测的准确性.
  • 通过结合动态相关性和定期流量数据特征来解决当前模型的局限性.

主要方法:

  • 开发了一个周期动态图到序列模型 (PDG2Seq),包括一个周期特征选择模块 (PFSM) 和一个周期动态图卷积门循环单元 (PDCGRU).
  • PFSM提取周期性特征,而PDCGRU利用这些和动态流量特征生成周期动态图用于时空特征提取.
  • 解码阶段使用周期性特征来预测未来的交通趋势.

主要成果:

  • 在流量预测方面,PDG2Seq在最先进的基线上表现优越.
  • 该模型有效地从动态的实时交通数据中提取时空特征.
  • 在四个大型数据集上的实验验验证了模型的准确性和有效性.

结论:

  • 拟议的PDG2Seq模型通过整合周期性和动态特征,显著提高了流量预测的准确性.
  • 这种方法为智能交通管理系统提供了更强大的解决方案.
  • 该模型捕捉未来趋势的能力标志着该领域的重大进步.