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相关概念视频

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Vector Components in the Cartesian Coordinate System01:29

Vector Components in the Cartesian Coordinate System

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Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
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Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
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变化自编码器理解结的拓学.

Anna Braghetto1,2, Sumanta Kundu3,4, Marco Baiesi1,2

  • 1University of Padova, Department of Physics and Astronomy, Via Marzolo 8, I-35131 Padova, Italy.

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概括

本研究介绍了一种混合机器学习模型 (VAEC),通过掌握复杂的拓概念,有效地分类聚合物结. 该模型可以识别结合力,并且在没有模拟的情况下生成现实的结结配置.

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科学领域:

  • 聚合物物理 聚合物物理
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 标准的数学方法难以识别复杂的聚合物结构中的节点.
  • 机器学习为聚合物拓分析提供了有希望的替代方案.

研究的目的:

  • 开发一种混合监督/无监督机器学习方法,用于聚合物结的分类.
  • 评估模型理解和利用拓概念的能力.

主要方法:

  • 引入一个变化自编码器,增强一个节点类型分类器 (VAEC).
  • 在标记的3D聚合物配置上训练VAEC.
  • 评估VAEC在节点分类和奇拉性检测方面的表现.

主要成果:

  • VAEC成功地将节点组织到其潜在的表示中,捕捉了像拉性和节点家族这样的拓特征.
  • 该模型准确地区分了以前未被检测到的结 (9_42和10_71) 的性.
  • VAEC的潜伏空间使得结结聚合物配置的忠实生成成为可能.

结论:

  • 混合机器学习模型可以有效地捕捉纠线程的复杂拓特征.
  • VAEC展示了高分子科学中先进结分析和生成建模的潜力.
  • 这种方法提供了一种无模拟的方法来重建和产生结结的聚合物配置.