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相关概念视频

Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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The Maximum Power Transfer Theorem01:20

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Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
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Sampling Theorem01:15

Sampling Theorem

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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MatSwarm:可信的群体传输学习驱动的材料计算,用于安全的大数据共享.

Ran Wang1,2,3, Cheng Xu4,5,6, Shuhao Zhang3

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China.

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|October 29, 2024
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概括
此摘要是机器生成的。

MatSwarm是一个新的框架,集成了联合学习和区块链,增强了协作材料研究. 它提高了模型准确性和数据安全性,克服了工业4.0材料开发中的挑战.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 工业4.0推动了对新材料的需求,需要机构间的合作.
  • 数据仓库和非识别的数据库. 在多机构设置中的数据阻碍了协作模型的准确性.
  • 保护敏感数据是协作材料研究的一个主要挑战.

研究的目的:

  • 引入MatSwarm框架,用于安全和准确的协作材料数据分析.
  • 为了应对数据异质性的挑战,非i.i.d. 数据,以及材料研究中的数据保密性.
  • 在材料科学的联合学习环境中增强模型的概括性和准确性.

主要方法:

  • 开发了MatSwarm框架,结合了群体学习,联合学习和区块链技术.
  • 实施了一种群体转移学习方法,用于参数对齐的规范化术语.
  • 使用Intel SGX的可信执行环境 (TEE) 来增强数据安全性和保密性.

主要成果:

  • 马特斯瓦姆成功地汇总了来自30多个机构的1400多万个物质数据条目.
  • 与独立训练的模型相比,该框架显示出更高的准确性和概括性.
  • 在整个模型培训和聚合过程中确保数据保密.

结论:

  • 在协作材料研究中,MatSwarm有效地克服了数据孤岛和安全挑战.
  • 该框架显著提高了材料数据分析的准确性和概括性.
  • MatSwarm促进了安全和高效的多机构合作,以加速材料发现.