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相关概念视频

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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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...
853
Distance Measurements by Taping01:18

Distance Measurements by Taping

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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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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...
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Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Instrument Calibration01:12

Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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基于机器学习和立体视觉的振动频率测量.

Jiantao Liu, Shenghui Liao, Beiji Zou

    Applied optics
    |September 22, 2025
    PubMed
    概括

    一个新的立体视觉系统与机器学习 (SVS/ML) 方法简化了振动频率测量. 这种方法可以实现高精度和稳定性,而不需要复杂的处理,因此非常适合工程应用.

    科学领域:

    • 工程 工程师 工程师 工程师
    • 计算机科学 计算机科学
    • 物理 物理学 物理

    背景情况:

    • 使用立体视觉系统 (SVS) 进行传统的振动频率测量是复杂的.
    • 现有的方法需要明确的时间历史提取,复杂的算法和光学线索,限制了适应性.

    研究的目的:

    • 引入一种简化和强大的振动频率测量方法.
    • 在振动分析中克服传统SVS技术的局限性.

    主要方法:

    • 该研究提出了一种新的立体视觉系统与机器学习 (SVS/ML) 方法.
    • 这种方法将立体视觉与机器学习相结合,消除了对显式时间历史提取的需求.

    主要成果:

    • 在SVS/ML方法直接生成像素级振动频率图与最小的错误.
    • 实验结果显示的准确性与工业级传感器相美.
    • 该方法在实验室和现场条件下表现出强大的稳定性,不需要额外的后处理.

    结论:

    • SVS/ML方法为振动频率测量提供了简单,准确和强大的解决方案.
    • 其易于实施和适应性使其适用于结构健康监测和机械诊断.

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