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相关概念视频

Absorption of Radiation01:05

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If a closed surface does not have any charge inside where an electric field line can terminate, then the electric field line entering the surface at one point must necessarily exit at some other point of the surface. Therefore, if a closed surface does not have any charges inside the enclosed volume, then the electric flux through the surface is zero. What happens to the electric flux if there are some charges inside the enclosed volume? Gauss's law gives a quantitative answer to this question.
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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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相关实验视频

Updated: Jul 9, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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对于排放性工程的一般深度学习框架.

Shilv Yu1, Peng Zhou2,3, Wang Xi1

  • 1School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.

Light, science & applications
|December 5, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一个新的AI框架,使用深度Q学习来设计波长选择性热发射器 (WS-TE). 这种方法自主选择材料并优化结构,用于热伪装和辐射冷却等应用.

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科学领域:

  • 超材料和纳米光子学
  • 材料科学中的人工智能 科学材料中的人工智能

背景情况:

  • 波长选择性热发射器 (WS-TE) 对于热伪装,辐射冷却和气体传感等应用至关重要.
  • 现有的设计方法缺乏一个一般的框架,需要特定应用的材料和结构,并且经常无法共同优化两者.
  • 以前的方法通常固定材料组成或结构参数,限制设计灵活性.

研究的目的:

  • 建立WS-TE的排放性工程的一般设计框架.
  • 开发一种人工智能驱动的方法,用于自主材料选择和结构优化.
  • 为了证明框架在各种应用中的多功能性.

主要方法:

  • 使用深度Q学习网络,强化学习算法,用于设计多层WS-TE.
  • 创建了一个自建的材料库,用于自主选择材料.
  • 优化结构参数以实现目标发射率光谱.

主要成果:

  • 成功设计和制造了三种不同的WS-TE用于热伪装,辐射冷却和气体传感.
  • 证明了深度Q学习算法的能力,提供一个超越1D多层结构的通用设计框架.
  • 验证了材料的自主选择和结构参数的优化,以达到目标光谱发射率.

结论:

  • 深度Q学习框架为设计各种应用中的WS-TE提供了一种可行和高效的方法.
  • 该框架在材料,结构和尺寸上的可扩展性为排放性工程提供了通用的解决方案.
  • 这项工作为在非线性优化问题中高效设计铺平了道路,超越了热元材料.