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相关概念视频

Response Surface Methodology01:16

Response Surface Methodology

176
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Methods of Obtaining Topography01:25

Methods of Obtaining Topography

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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Surface Tension and Surface Energy01:16

Surface Tension and Surface Energy

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When a paint brush is immersed in water, the bristles wave freely inside the water. When it is taken out, the bristles stick together. The reason behind this effect is surface tension.
Consider a beaker filled with liquid. The bulk molecules in the liquid experience equal attractive forces on all sides with the surrounding molecules. However, the surface molecules experience a net attractive force downward due to the bulk molecules. The surface of the liquid behaves like a stretched membrane,...
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Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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Surface Tension of Fluid01:22

Surface Tension of Fluid

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Surface tension is a fundamental property of fluids, occurring at the boundary between a liquid and a gas or between two immiscible liquids. This phenomenon arises from the cohesive forces between molecules at the fluid's surface, creating an effect similar to a stretched elastic membrane. Inside each fluid, molecules are equally attracted in all directions by neighboring molecules, but surface molecules experience a net inward force, resulting in surface tension.
Surface tension varies...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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相关实验视频

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Deep Neural Networks for Image-Based Dietary Assessment
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泰勒-大猩猩部队优化了深度学习网络,用于表面粗度估计.

Syed Jahangir Badashah1, Shaik Shafiulla Basha2, Shaik Rafi Ahamed3

  • 1Professor ECE Department, Sreenidhi Institute of Science and Technology, Hyderabad, India.

Network (Bristol, England)
|August 22, 2023
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概括
此摘要是机器生成的。

一个新的基于优化器的泰勒-大猩猩部队深度神经模糊网络 (基于泰勒-GTO的DNFN) 估计了无接触的加工表面粗度. 这种方法提供了精确的表面粗度评估,克服了传统的接触式 stylus profilometry 的局限性.

关键词:
DNFNN DNFNN 在线阅读深度学习是一种深度学习.大猩猩部队的优化器.泰勒系列的泰勒系列估计表面粗度的估计.

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科学领域:

  • 制造业 工程 制造工程
  • 人工智能的人工智能
  • 材料科学 材料科学 材料科学

背景情况:

  • 表面粗度评估对于加工产品的质量至关重要.
  • 接触式笔造型测量是常见的,但会导致表面退化.
  • 需要一种非接触式,准确的方法来估计表面粗度.

研究的目的:

  • 提出一种新的,非接触表面粗度评估技术.
  • 开发一个深度神经模糊网络 (DNFN),通过混合算法优化粗度估计.
  • 评估拟议的基于泰勒-GTO的DNFN模型的性能.

主要方法:

  • 一种混合优化算法,Taylor-Gorilla Troops Optimizer (Taylor-GTO),是通过结合泰勒序列和大猩猩的部队优化器来开发的.
  • 一个深度神经模糊网络 (DNFN) 被训练使用泰勒-GTO算法进行表面粗度估计.
  • 该方法包括预处理,数据增强,特征提取,特征融合和粗度估计.

主要成果:

  • 提议的基于泰勒-GTO的DNFN模型在表面粗度估计中实现了高精度.
  • 该模型展示了最小的平均绝对误差 (0.403),平均平方误差 (0.416) 和根平均平方误差 (1.149).
  • 开发的技术有效地估计了表面粗度,而不会导致工件退化.

结论:

  • 基于Taylor-GTO的DNFN提供了一种可靠和准确的非接触方法来评估表面粗度.
  • 这种方法克服了传统的基于接触的方法的局限性,防止表面损伤.
  • 该研究强调了混合AI优化技术在精密制造质量控制中的潜力.