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相关概念视频

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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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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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 of...
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相关实验视频

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Decoding Natural Behavior from Neuroethological Embedding
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软传感器用于非均采样非线性动态过程使用不规则的时间间隔潜伏的概率学可预测性嵌入监督深度网络.

Zhengxuan Zhang1, Xu Yang2, Yuri A W Shardt3

  • 1Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, 100083, Beijing, China.

ISA transactions
|November 14, 2025
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概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型,即嵌入监督深度网络 (ILPPSDN) 的不规则时间间隔潜伏概率可预测性,用于工业软传感. ILPPSDN有效地处理与非均采样数据的非线性动态过程,显著提高预测准确性.

关键词:
动态潜变量的动态潜变量不规则的时间间隔.潜在的概率学可预测性不统一的采样方式软感应感应是一种柔软的感应.监督深度网络监督深度网络

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 动态隐性变量 (DLV) 模型对于工业软传感至关重要,但与非线性动态和非均数据作斗争.
  • 传统的DLV模型仅限于线性特征提取,并且在采样数据不规则的情况下表现不佳.

研究的目的:

  • 提出一种先进的软传感器,即嵌入监督深度网络 (ILPPSDN) 的不规则时间间隔潜伏概率可预测性,用于采样不均的非线性动态过程.
  • 在软传感应用中增强特征可预测性和模型潜伏时间依赖性.

主要方法:

  • 开发了一个ILPPSDN,将预测规范化术语纳入自编码器的解码损失.
  • 利用一个普通微分方程网络来参数化在一个变量循环神经网络内的内部状态导数.
  • 为所有网络组件实施统一培训,并为软传感器开发提供雇员预培训和监督微调.

主要成果:

  • 通过ILPPSDN,在初始化剂和硫回收装置的各种不均的采样比率中,实现了根平均平方误差 (RMSE) 的显著降低.
  • 在工业案例研究中,RMSE的减少至少为21.1%至26.1%.
  • 废弃性研究证实了拟议方法的有效性,在各自的工业案例中至少减少了5%和6%的RMSE.

结论:

  • 拟议的ILPPSDN为非线性动态过程中的软传感提供了强大的解决方案,采用非均采样数据.
  • 这种深度学习方法增强了特征可预测性和时间依赖性建模,比传统方法带来了更高的性能.
  • 该ILPPSDN证明了实用性和在工业过程监测和控制方面的重大改进.