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関連する概念動画

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...
982
Speed of Sound in Solids and Liquids00:51

Speed of Sound in Solids and Liquids

4.0K
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

8.7K
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)...
3.8K
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...
587

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4次元海洋音速フィールド予測のためのディープラーニングフレームワーク.

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
PubMed
まとめ

私たちは新しいディープラーニングモデルであるSwin Transformer-UNet (ST-UNet) を開発し,正確な海洋音速フィールド (SSF) 予測を実現しました. このモデルは4次元の時空情報を捉え,水中アプリケーションの予測精度を大幅に改善します.

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科学分野:

  • 海洋学 海洋学とは
  • 地質物理学 地質物理学とは地質物理学です.
  • 人工知能 (AI) とは,人工知能 (AI) のことです.

背景:

  • 海洋音速フィールド (SSF) の正確な予測は,水中通信,海洋探査,環境モニタリングに不可欠です.
  • ディープラーニングモデルはSSFの予測には有望だが,高次元データには苦戦し,3Dの特徴抽出と不完全な時空情報捕捉に限定される.

研究 の 目的:

  • 四次元 (4D) SSF予測のための新しいディープラーニングモデルを開発し,完全な時空情報を取得します.
  • 既存のSSF予測方法の精度と能力を高めること.

主な方法:

  • U-NetとSwin Transformerのネットワークを統合したSwin Transformer-UNet (ST-UNet) モデルを提案した.
  • マルチヘッドの自己注意を介して空間時間的な特徴の抽出のための利用されたスウィン・トランスフォーマー.
  • U-Netを使用し,コンボリューション的機能回復を通じて空間的詳細を精錬しました.

主要な成果:

  • ST-UNetモデルは,南シナ海から7日間の歴史的データを用いて24時間のSSF予測で0.783m/sの平方平均誤差を達成しました.
  • ベースラインアーキテクチャと比較して優れたパフォーマンスを実証し,33%から72%の改善が示されました.

結論:

  • ST-UNetモデルは,4D SSFを効果的に予測し,既存の方法よりも性能が優れています.
  • この進歩は,水中通信,海洋資源探査,環境モニタリングの改善に重要な可能性を秘めています.