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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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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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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. 
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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基于MC-CNN的多速率质量变量的软传感器

Bing Song, Yichen Zhou, Hongbo Shi

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

    这项研究引入了一种新的深度学习模型,即多时通道卷积神经网络 (MC-CNN),以解决工业软传感器建模中的挑战,使用不同的数据采样速率. 尽管存在数据差距,但MC-CNN有效地预测了多个质量变量.

    科学领域:

    • 工业过程控制 工业过程控制
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 数据驱动软传感器建模在工业,化学和生物化学过程中至关重要.
    • 现有的多输入多输出 (MIMO) 传感器往往忽略了过程和质量变量之间的采样率的时间变化.
    • 在质量变量之间不一致的抽样率对准确的建模构成了重大挑战.

    研究的目的:

    • 提出一种新的深度学习 (DL) 模型,即多时通道卷积神经网络 (MC-CNN),以处理软传感器建模中的不同采样速率.
    • 开发一种有效地同时预测多个质量变量的方法,考虑到它们不同的时间分辨率.
    • 解决工业应用中现有的MIMO传感器的局限性.

    主要方法:

    • 设计了一个基于多时通道卷积神经网络 (MC-CNN) 的深度学习 (DL) 模型.
    • MC-CNN架构具有用于时间特征提取的共享网络和用于单个质量变量的并行预测网络.
    • 使用修改后向传播 (BP) 算法,将未采样的数据点 (空白值) 排除在训练过程中.

    主要成果:

    • 拟议的MC-CNN模型在两个工业案例研究中证明了在预测多个质量变量的有效性.
    • 该方法成功地管理和利用了不同采样频率的数据.

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  • 经过修改的BP算法可以防止模型训练期间缺少数据的干扰.
  • 结论:

    • 开发的MC-CNN为工业过程中的软传感器建模提供了有效的解决方案,具有异步采样率.
    • 这种方法在复杂的工业环境中提高了质量变量预测的准确性和可靠性.
    • 该研究验证了深度学习在处理过程监控中的时间数据复杂性的实用性.