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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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Signal and System01:26

Signal and System

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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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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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SFG Algebra01:16

SFG Algebra

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In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
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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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Statgraphics01:10

Statgraphics

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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Updated: May 1, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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简介:SENGraph是一个自学进化和节点意识的图形网络,用于工业过程中的软传感.

Feng Yan, Cong Wang, Zichen Wang

    IEEE transactions on neural networks and learning systems
    |September 13, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了SENGraph,这是一个用于工业软传感的新型图形网络. 它有效地捕捉复杂的过程变量关系,并通过专注于重要数据节点来提高模型准确性.

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    科学领域:

    • 工业过程监控 工业过程监控
    • 机器学习用于化学工程的机器学习

    背景情况:

    • 传统的软传感模型往往忽略了工业过程数据中的复杂关系,从而限制了它们的有效性.
    • 现有的图形网络方法难以在动态的工业环境中同时建模变量间结构和变量内时间依赖.
    • 一个关键的挑战是从节点学习,这些节点对于精确的软传感具有不同的重要性.

    研究的目的:

    • 开发一个先进的图形网络模型,SENGraph,用于增强工业软传感.
    • 为了解决捕获复杂工业数据关系和节点重要性方面的局限性.
    • 在动态工业过程中提高软传感器的准确性和稳定性.

    主要方法:

    • 提出了一个自学图形生成 (SLG) 模块,用于创建组合粗粒和细粒图形.
    • 实现了一个自我进化的图模块 (EGM),使用遗传策略来实现多样化的节点特征学习.
    • 设计了一个节点意识模块 (NAM),以优先考虑信息节点,并减轻不那么重要的节点.

    主要成果:

    • 在工业数据中,SENGraph成功地捕获了全球趋势和本地动态.
    • 进化和节点意识模块增强了模型识别关键过程变量的能力.
    • 与最先进的方法相比,在四个现实世界的工业数据集上表现出卓越的性能.

    结论:

    • 通过有效地建模复杂的数据结构,SENGraph在工业软传感方面取得了重大进展.
    • 拟议的方法解决了基于图形的软传感方面的关键挑战,提高了歧视力.
    • 该模型的有效性通过对各种工业数据集进行广泛的实验来验证.