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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

115
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
115
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

107
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
107
Classification of Signals01:30

Classification of Signals

549
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...
549
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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相关实验视频

Updated: Jul 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用基于时间频率的特征和深度学习模型对削切削工具的剩余使用寿命预测.

Sameer Sayyad1, Satish Kumar1,2, Arunkumar Bongale1

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

预测削机的剩余使用寿命 (RUL) 对制造效率至关重要. 时间频域特征与像LSTM这样的深度学习模型和混合方法相结合,显著提高了RUL预测的准确性.

关键词:
功能提取 特性提取磨削工艺 磨削工艺 磨削工艺剩余的使用寿命.时间频域时间频域工具的磨损,工具的磨损.

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科学领域:

  • 制造业 工程 制造工程
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 机由于其多功能性,在制造业中至关重要.
  • 切割工具的精度和表面加工直接影响工业生产率.
  • 监控切削工具的寿命至关重要,以防止工具磨损导致的停机时间.

研究的目的:

  • 准确预测削机的剩余使用寿命 (RUL).
  • 通过防止意外停机,提高加工精度和表面加工.
  • 为了优化切削工具在削操作中的使用寿命.

主要方法:

  • 使用IEEE NUAA Ideahouse数据集进行RUL估计.
  • 使用的时间频域 (TFD) 特性提取技术,包括短时间里埃变换 (STFT) 和波形变换 (WT).
  • 应用深度学习 (DL) 模型,如长短期记忆 (LSTM) 变体,卷积神经网络 (CNN) 和混合CNN-LSTM模型.

主要成果:

  • 特性工程质量对于准确的RUL预测至关重要.
  • 与LSTM变种和混合型号相结合的TFD功能表现出强的性能.
  • 提出的方法实现了对削切削刀具RUL的预测准确度的提高.

结论:

  • 准确的RUL预测对于最大限度地提高切割工具寿命和工业生产率至关重要.
  • TFD特征提取与先进的DL模型相结合,为削工具RUL估计提供了一个有前途的方法.
  • 这项研究有助于减少加工停机时间,提高整体制造效率.