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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

62
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,...
62
Convergence of Fourier Series01:21

Convergence of Fourier Series

126
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
126
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

170
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
170
Downsampling01:20

Downsampling

127
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...
127
Upsampling01:22

Upsampling

201
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Updated: Jun 3, 2025

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渐进式有限误差 逐步线性近似与分辨率减小用于时间序列数据压缩.

Jeng-Wei Lin1, Shih-Wei Liao2, Yu-Hung Tsai1

  • 1Department of Information Management, Tunghai University, Taichung 407224, Taiwan.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了PBEPLA-RR,这是一种压缩来自AIoT设备的时间序列数据的新方法. 它能高效地产生多个近似值,精度不同,大大降低了存储需求和能源消耗.

关键词:
PBEPLA-RRR是什么意思摇摆-RRRR是什么意思有界误差 断片式 线性近似一个等级的残余编码.渐进式数据压缩.传感器数据 传感器数据时间序列时间序列

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科学领域:

  • 数据科学数据科学数据科学
  • 事物的人工智能 (AIoT)
  • 数据压缩数据压缩

背景情况:

  • 人工智能设备产生大量的时间序列数据,导致传输,存储和处理的高能源成本.
  • 现有的损耗压缩方法通过牺牲准确性提供更好的比率,但不同的应用需要不同的数据保真度.
  • 存储多个压缩版本用于不同的错误极限是低效的.

研究的目的:

  • 开发一种方法,从单个压缩表示中高效地生成多个时间序列近似,具有不同的误差极限.
  • 为了减少与存储和管理时间序列数据相关的整体数据大小和能源消耗.
  • 动态提供根据特定准确性要求定制的数据版本.

主要方法:

  • 时间序列逐渐分解成零碎的线性函数,从最大的误差边界开始.
  • 使用Swing-Recursive Residual (Swing-RR) 算法,在每个分解步骤中生成有限误差逐步线性近似 (BEPLA).
  • 聚合多个BEPLA以创建相继较小的误差界限的近似值.

主要成果:

  • 拟议的方法PBEPLA-RR在8个现实数据集上进行了评估,其误差极限为5%,1%和0.5%.
  • 多个BEPLA的组合数据大小与存储单个版本在最小误差边界的数据大小相当.
  • 这大大减少了存储需求,而不是为每个错误界限保持独立的压缩版本.

结论:

  • PBEPLA-RR有效地实现了时间序列数据的高压缩比.
  • 该方法提供了一种灵活的方式,可以从单个压缩模型中获得具有不同误差极限的多个近似值.
  • 这种方法为AIoT应用提供了大量的能源和存储节约.