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相关概念视频

Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Buoyancy01:12

Buoyancy

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When an object is placed in a fluid, it either floats or sinks. All objects in a fluid experience a buoyant force. For example, a metal ball sinks, while a rubber ball floats. Similarly, a submarine can sink and float by adjusting its buoyancy.  The concept of buoyancy raises several interesting questions. For instance, where does this buoyant force come from? How much buoyant force is required to make an object sink or float? Do objects that sink get any support at all from the...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Testing Water Quality01:14

Testing Water Quality

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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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相关实验视频

Updated: Jan 18, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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一个池溺水检测模型基于改进的YOLO.

Wenhui Zhang1, Lu Chen1, Jianchun Shi2

  • 1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.

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

一个新的YOLO11-LiB模型提高了在游泳池中溺水的检测. 这种高效的AI系统为实时安全监控提供了高准确度.

关键词:
在YOLO11上,你会发现YOLO11是什么意思.关注注意力注意力注意力注意力溺水探测器可以检测到溺水.功能融合功能融合功能轻量级的轻量级的轻量级的轻量级的

相关实验视频

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 公共卫生 公共卫生

背景情况:

  • 溺水是青少年死亡的主要原因.
  • 目前在池中使用的监控方法存在局限性.
  • 现有的人工智能模型在效率和复杂条件方面扎.

研究的目的:

  • 开发一个高效和强大的AI模型用于溺水检测.
  • 改进游泳池环境中的实时安全监控.
  • 解决边缘部署当前视觉模型的局限性.

主要方法:

  • 拟议的YOLO11-LiB模型基于YOLO11n.
  • 推出了轻量级特征提取模块 (LGCBlock),具有幽灵卷积和动态卷积.
  • 综合跨通道位置感知空间注意 (C2PSAiSCSA) 和双向特征融合网络 (BiFF-Net).

主要成果:

  • YOLO11-LiB实现了94.1%的溺水级平均精度 (DmAP50).
  • 该模型只有2.02万个参数,大小为4.25 MB.
  • 证明了准确性和效率之间的平衡.

结论:

  • YOLO11-LiB为实时溺水探测提供了高性能解决方案.
  • 该模型适合在游泳池的边缘部署.
  • 这项研究有助于通过先进的人工智能提高水生安全.