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相关概念视频

Fast Fourier Transform01:10

Fast Fourier Transform

258
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
258
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

277
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
277
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

213
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
213
Discrete Fourier Transform01:15

Discrete Fourier Transform

209
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
209
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

256
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
256
Trigonometric Fourier series01:17

Trigonometric Fourier series

176
Fourier series is a foundational mathematical technique that decomposes periodic functions into an infinite series of sinusoidal harmonics. This method enables the representation of complex periodic signals as sums of simple sine and cosine functions, facilitating their analysis and interpretation in various fields, including signal processing, acoustics, and electrical engineering.
The trigonometric Fourier series specifically expresses a periodic function with a defined period T using sine...
176

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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基于适应变压器神经网络和基于FFT的特征提取的时间序列预测模型.

Kyrylo Yemets1, Ivan Izonin1,2, Ivanna Dronyuk3

  • 1Department of Artificial Intelligence, Lviv Polytechnic National University, 79905 Lviv, Ukraine.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了用于时间序列预测的增强型变压器模型,通过使用快速里埃变换 (FFT) 来增加频域特征来提高准确性. 这种新的方法显著提高了传感器数据的预测性能.

关键词:
一个年龄,一个年龄.大数据的大数据大数据这是一个深度AR DeepAR.这是LSTM的LSTM.注意力机制注意力机制深度学习是一种深度学习.快速的里埃转换是什么?功能提取 特性提取预测 预测 预测 预测绩效评价 绩效评价 绩效评价 绩效评价 绩效评价传感器 传感器 传感器时间序列时间序列变压器的变压器是一个变压器.

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科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 准确的时间序列预测在金融,气候学和工程学中至关重要.
  • 神经网络因体积,噪声和长期依赖而难以处理传感器数据.
  • 现有的模型面临着各种传感器数据特征的挑战.

研究的目的:

  • 为了提高传感器收集数据的时间序列预测准确度.
  • 解决当前神经网络模型在处理复杂时间序列方面的局限性.
  • 通过结合频域信息来提高预测性能.

主要方法:

  • 为时间序列预测提出了一个适应的变压器架构.
  • 引入了一种使用快速富里埃转换 (FFT) 来将时间域转换为频率域的数据预处理方法.
  • 用复杂值的频域特征丰富数据,以增强信息内容.

主要成果:

  • 拟议的模型在三个不同的传感器数据集中展示了卓越的性能.
  • 与LSTM,DeepAR和Transformer等最先进的模型相比,实现了更高的准确性.
  • 在五个不同的绩效指标上始终优于现有方法.

结论:

  • 适应的变压器模型与FFT预处理显著提高时间序列预测的准确性.
  • 该方法有效地应对大量,噪音和对传感器数据的长期依赖所带来的挑战.
  • 这种方法为数据驱动应用程序的准确预测提供了强大的解决方案.