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相关概念视频

Speed of Sound in Gases01:08

Speed of Sound in Gases

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The speed of sound in a gaseous medium depends on various factors. Since gases constitute molecules that are free to move, they are highly compressible. Hence, sound waves travel slowly through gases. Thermodynamics helps us understand the relationship between pressure, volume, and temperature of gases, thus, the speed of sound in an ideal gas can be determined using the laws of thermodynamics. At the same time, Newton's laws of motion and the continuity equation of fluid dynamics also come...
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Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

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As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
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Speed of Sound in Solids and Liquids00:51

Speed of Sound in Solids and Liquids

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Most solids and liquids are incompressible—their densities remain constant throughout. In the presence of an external force, the molecules tend to restore to their original positions, which is only possible because the constituents interact. The interactions help the constituents pass on information about external disturbances, like sound waves. Therefore, sound waves travel faster through these media. Compared to solids, the constituents in a liquid are less tightly bound. Thus, sound...
4.0K
Korotkoff Sounds01:12

Korotkoff Sounds

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Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...
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Heart Sounds01:15

Heart Sounds

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
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Soundness of Cement01:17

Soundness of Cement

587
The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
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这是一个深度学习框架,用于四维海洋声速场预测.

Yingjie Li1,2, Jixing Qin1,2, Shuanglin Wu1,2

  • 1College of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.

The Journal of the Acoustical Society of America
|February 12, 2026
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我们开发了一个新的深度学习模型,Swin Transformer-UNet (ST-UNet),用于准确的海洋声速场 (SSF) 预测. 该模型捕捉了四维的时空信息,显著提高了水下应用的预测准确性.

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科学领域:

  • 海洋学 海洋学 海洋学
  • 地质物理学 地质物理学
  • 人工智能的人工智能

背景情况:

  • 准确预测海洋声速场 (SSF) 对水下通信,海洋勘探和环境监测至关重要.
  • 深度学习模型对SSF预测有希望,但与高维数据作斗争,限制它们到3D特征提取和不完整的时空信息捕获.

研究的目的:

  • 开发一种新的深度学习模型,用于四维 (4D) SSF预测,捕获完整的时空信息.
  • 提高现有的SSF预测方法的准确性和能力.

主要方法:

  • 提出了Swin变压器-UNet (ST-UNet) 模型,集成U-Net和Swin变压器网络.
  • 使用Swin变压器通过多头自我注意来进行时空特征提取.
  • 使用U-Net通过卷积特征恢复来完善空间细节.

主要成果:

  • ST-UNet模型在使用南中国海7天历史数据的24小时SSF预测中实现了0.783m/s的根平均平方误差.
  • 与基线架构相比,表现出优越的性能,改进幅度从33%到72%不等.

结论:

  • ST-UNet模型有效地预测4DSSF,优于现有方法.
  • 这种进步具有改善水下通信,海洋资源勘探和环境监测的巨大潜力.