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相关概念视频

Determining the Mechanical Strength of Ultra-Fine-Grained Metals05:04

Determining the Mechanical Strength of Ultra-Fine-Grained Metals

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The protocol presented here describes the high-pressure radial diamond-anvil-cell experiments and analyzing the related data, which are essential for obtaining the mechanical strength of the nanomaterials with a significant breakthrough to the traditional...
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Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction09:13

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This paper provides a detailed method to characterize the microstructure of ultra-fine grained and nanocrystalline materials using a scanning electron microscope equipped with a standard electron backscatter diffraction system. Metal alloys and minerals presenting refined microstructures are analyzed using this technique, showing the diversity of its possible...
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The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
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Fineness Modulus01:19

Fineness Modulus

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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
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Nanocrystalline Alloys and Nano-grain Size Stability06:52

Nanocrystalline Alloys and Nano-grain Size Stability

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Source: Sina Shahbazmohamadi and Peiman Shahbeigi-Roodposhti-Roodposhti, School of Engineering, University of Connecticut, Storrs, CT
Alloys with grain size less than 100 nm are known as nanocrystaline alloys. Due to their enhanced physical and mechanical properties, there is an ever-increasing demand to employ them in various industries such as semiconductor, biosensors and aerospace. 
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The current study describes a fine motor behavior test for examining motor deficits in rodent models, including the TgF344-AD rat, using machine...
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Updated: Jan 20, 2026

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深度细粒度聚类与模型重复使用

Jie Hong1, Xulun Ye1, Jieyu Zhao1

  • 1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.

Neural networks : the official journal of the International Neural Network Society
|January 18, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了针对细粒度任务的新型深度聚类框架,提高了高度相似样本的聚类一致性和稳定性. 该方法在图像数据集上实现了最先进的性能.

关键词:
深度学习是一种深度学习.有细粒度的聚类.模型重复使用

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相关实验视频

Last Updated: Jan 20, 2026

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 深度聚类方法优秀,但在涉及高度相似样本的细粒度任务中扎.
  • 传统的集群方法在区分微妙的语义差异方面面临挑战,导致集群界限不清楚.

研究的目的:

  • 为细粒度任务开发一种新的深度集群框架.
  • 学习特征表示,在嵌入空间中为类似数据点创建清晰的集群边界.

主要方法:

  • 提出一种用于细粒度聚类的新型重复使用框架.
  • 采用低级优化,在增强数据视图中实现集群一致性.
  • 使用重复使用的模型引导的散散化,以确保对类内变异和类间相似性的稳定性.

主要成果:

  • 拟议的框架增强了集群的一致性和稳定性.
  • 在三个细粒度图像数据集上实现最先进的集群性能.
  • 从理论上证明了在分散条件下,对样本增强矩阵的低等级的实现.

结论:

  • 这种新的框架提供了一个强大的细粒度无监督集群替代方案.
  • 与现有的细粒度聚类方法相比,显示出更高的性能.
  • 有效地处理微妙的语义差异,并改善嵌入空间的决策界限.