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相关概念视频

Sampling Methods: Overview01:06

Sampling Methods: Overview

316
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...
316
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

222
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
222
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...
11.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
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...
106
Sampling Distribution01:12

Sampling Distribution

12.5K
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...
12.5K
Sampling Plans01:23

Sampling Plans

181
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
181

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RegGAN:一个虚拟样本生成网络,用于开发具有小数据的软传感器.

Yuhong Wang1, Pengfei Yan1

  • 1Department of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.

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概括
此摘要是机器生成的。

本研究介绍了一种新的生成对抗网络,用于创建虚拟样本,以提高化学生产中的软传感器性能. 当实际数据有限时,这种方法可以增强实时监控,将错误减少21%以上.

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 过程监控 过程监控
  • 人工智能的人工智能

背景情况:

  • 化学生产中的关键质量变量往往缺乏在线测量能力.
  • 软传感器对于实时监控至关重要,但需要广泛的标记数据进行开发.
  • 由于时间和成本的限制,获得足够的软传感器标记数据是具有挑战性的.

研究的目的:

  • 提出一种新的回归生成对抗网络 (GAN),用于生成虚拟样本.
  • 为了应对在开发高性能软传感器时标记数据不足的挑战.
  • 提高化学生产系统中软传感器的预测性能.

主要方法:

  • 开发了一个回归GAN,学习辅助和目标变量之间的数据分布和映射.
  • 包含一个权重权重的自动编码器,以提高生成模型训练的稳定性.
  • 利用相似度测量算法选择相关的虚拟样本,将其整合到训练集中.

主要成果:

  • 拟议的GAN有效地生成了与实际数据非常相似的虚拟样本.
  • 拟议方法生成的虚拟样本与其他方法相比,与真实样本的接近性更高.
  • 将虚拟样本集成到基于长期短期记忆 (LSTM) 的软传感器中,使得根平均平方误差减少了21.03%.

结论:

  • 提出的生成方法成功地创建了有价值的虚拟样本,以增强有限的现实世界数据.
  • 这些虚拟样本的整合大大提高了软传感器的预测准确度.
  • 这种方法为改善化学生产过程实时监控提供了可行的解决方案.