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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

60
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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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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Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.4K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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M2 D-VAE:在缺失值干扰下,对未经监督的工业操作应用程序进行自我监督的概率的时空空间隐藏表示学习.

Qingyang Dai, Chunhui Zhao, Biao Huang

    IEEE transactions on neural networks and learning systems
    |October 23, 2024
    PubMed
    概括

    本研究引入了一种新的自我监督学习模型,从工业数据中提取缺失值的时空隐藏变量. 这种方法增强了工业操作应用,如数据归算和过程监控.

    科学领域:

    • 工业过程监控 工业过程监控
    • 数据科学数据科学数据科学
    • 机器学习 机器学习

    背景情况:

    • 工业过程数据经常含有缺失值,原因是传感器故障和传输错误.
    • 缺少的数据阻碍了分析时间空间相关性的数据驱动方法,影响下游的工业应用.
    • 现有的方法很难从不完整的顺序工业数据中有效地提取有意义的表示.

    研究的目的:

    • 提出一种自我监督的表示学习模型,用于从缺失值的工业数据中提取概率的时间空间潜变量 (LV).
    • 开发一个统一的框架,用于在各种工业运营应用中使用这些LV.
    • 证明模型在无监督的工业任务中的有效性,包括缺失值赋值和动态过程监控.

    主要方法:

    • 开发了一种新的深度动态概率潜变模型,马尔科夫动态变量自编码器 (MD-VAE),以捕捉时间空间依赖.
    • 引入了自我监督学习方法,掩盖了MD-VAE,以处理在LV提取过程中缺失的值干扰.
    • 纳入贝叶斯平滑用于隐藏的后部和可控制的约束,用于模型优化.

    主要成果:

    • 拟议的蒙面MD-VAE模型成功地从工业数据中提取缺失值的时间空间LV.
    • 提取的LVs被有效地用于下游工业任务的统一框架.

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  • 关于多相流程的案例研究表明,在缺失值赋值和动态过程监控方面表现优异.

    • 结论:

    • 蒙面的MD-VAE模型为缺乏数据的工业过程中的表示学习提供了强大的解决方案.
    • 统一的框架使得提取的LVs可以有效地用于关键的工业运营任务.
    • 这种方法显著提高了数据驱动方法在具有挑战性的工业环境中的可靠性和适用性.