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相关概念视频

Accelerating Fluids01:17

Accelerating Fluids

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When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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The Power Superposition Principle01:19

The Power Superposition Principle

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Consider a circuit with two sinusoidal voltage sources. Each one influences the circuit independently, and the superposition principle helps us understand the combined effect by adding up the responses from each source.
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Acceleration Vectors01:30

Acceleration Vectors

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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
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Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
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Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

385
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
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相关实验视频

Updated: Jul 4, 2025

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
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Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

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人工智能驱动的超现实主义:电影染的下一步?

Ramin Javan1, Navid Mostaghni1

  • 1From the Department of Radiology, George Washington University Hospital, 900 23rd St NW, Suite G2092, Washington, DC 20037 (R.J.); and California University of Science and Medicine School of Medicine, Colton, Calif (N.M.).

Radiology
|January 30, 2024
PubMed
概括

人工智能 (AI) 图像生成,特别是像Midjourney这样的扩散模型,可以增强3D医疗图像. 然而,解剖学准确性挑战需要人工智能开发人员和放射学家之间的合作.

科学领域:

  • 医学成像医学成像
  • 人工智能的人工智能是人工智能.
  • 扩散模型的扩散模型.

背景情况:

  • 由人工智能驱动的图像生成为增强3D医疗图像提供了潜力.
  • 扩散模型目前是人工智能图像增强的标准.

研究的目的:

  • 展示Midjourney v5.2用于生成医疗图像的能力.
  • 为在医疗环境中使用文本转图像人工智能提供实用指南.

主要方法:

  • 探索性研究中途的原则,参数和提示工程.
  • 调查的图像生成时间为2023年7月27日至8月3日.
  • 包括为创建真实的医疗图像的逐步说明.

主要成果:

  • 通过快速工程生成了30张图像 (眼睛,皮肤,血管动脉瘤),具有可定制的特征.
  • 由于训练数据的限制,解剖学准确性并未始终保持.
  • 在生成数字或文本等精确细节时发现的缺陷.

结论:

  • 人工智能图像生成可以改善特定应用的3D医疗图像.
  • 由于非医疗培训数据中的潜在不准确性,合作至关重要.

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