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相关概念视频

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
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When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
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The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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MUSeg:用于复杂的地下矿场景的多模式语义细分数据集.

Shiyan Li1, Qingqun Kong1, Xuan Gao1

  • 1School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing, 100083, China.

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|July 8, 2025
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概括

这项研究介绍了MUSeg,这是一个新的多式联网数据集,用于智能采矿感知. 它解决了地下矿山视觉感知方面的挑战,使先进的多式联接融合语义细分成为可能.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 地质科学工程 工程地质科学工程

背景情况:

  • 由于恶劣的环境,地下采矿面临视觉感知方面的挑战,限制了智能自动化.
  • 在这些条件下,基于可见光的方法很难实现,因此需要采用多式联络方法.
  • 现有的多式联运数据集对于复杂的地下矿场景是不够的.

研究的目的:

  • 开发和引入多模式地下矿山语义细分 (MUSeg) 数据集.
  • 为地下矿山中多式融合语义细分提供一个基准数据集.
  • 促进采矿领域智能感知技术的研究和应用.

主要方法:

  • 从六个不同的中国矿山收集了3,171个对齐的RGB和深度图像对.
  • 手动注释了15个语义对象类别,与我的感知任务相关.
  • 与采矿专家验证了注释,并使用经典算法评估了数据集.

主要成果:

  • MUSeg数据集为地下矿山中的多式模式语义细分提供了一个全面的资源.
  • 它解决了在这个专业领域中缺少专用数据集的严重问题.
  • 最初的评估证明了数据集对基准测试算法的实用性.

结论:

  • MUSeg数据集对智能采矿做出了重大贡献,可以在感知方面取得进步.
  • 它为开发和应用强大的多式联络融合算法提供了基础.
  • 这一资源将加速向无人和智能采矿业务的过渡.