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相关概念视频

State Space Representation01:27

State Space Representation

534
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
534
Discrete Fourier Transform01:15

Discrete Fourier Transform

856
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...
856
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

663
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]...
663
Linear time-invariant Systems01:23

Linear time-invariant Systems

874
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
874
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

395
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 of...
395
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

18.8K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
18.8K

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相关实验视频

Updated: Jan 18, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

动态自回归张量因子化用于空间时空系统的模式发现.

Xinyu Chen, Dingyi Zhuang, HanQin Cai

    IEEE transactions on pattern analysis and machine intelligence
    |June 4, 2025
    PubMed
    概括

    本研究介绍了动态自回归张量分解,这是一个无监督的机器学习框架,用于发现复杂的时空系统中的模式. 该方法有效地从各种现实世界的数据中揭示了时间变化的空间和时间洞察力.

    科学领域:

    • 数据科学数据科学数据科学
    • 机器学习 机器学习
    • 复杂系统分析 复杂系统分析

    背景情况:

    • 时空系统在科学学科中普遍存在,具有关键的数据模式.
    • 使用数据驱动的机器学习来描述这些系统是一个基本的挑战.
    • 现有的方法可能无法完全捕捉时空数据的动态和多维性质.

    研究的目的:

    • 为时空系统引入一个无监督模式发现框架.
    • 为了能够在多变量和多维数据中描述时间变化的自回归.
    • 从复杂的数据集中发现可解释的空间和时间模式/模式.

    主要方法:

    • 开发了动态自回归张量分解,一个无监督的机器学习框架.
    • 集成张量分解与时间变化的自回归用于模式发现.
    • 假设一个直角的空间因子矩阵,以实现高效的建模.

    主要成果:

    • 将框架应用于流体动力学,国际贸易和城市流动性数据集.
    • 在国际贸易数据中确定可解释的进出口模式.
    • 在2019年至2022年期间使用共享乘车数据检测到城市人类流动模式的变化.

    更多相关视频

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    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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    相关实验视频

    Last Updated: Jan 18, 2026

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

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    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

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    结论:

    • 动态自回归张量分解框架有效地发现了时空系统中的有意义的模式.
    • 发现的模式既具有时间变化和多维性,提供了重要的见解.
    • 该框架在各种科学领域和数据类型中展示了多功能性.