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相关概念视频

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

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A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Galvanometer01:25

Galvanometer

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Common devices, including car instrument panels, battery chargers, and inexpensive electrical instruments, measure potential difference (voltage), current, or resistance using a d'Arsonval galvanometer. This electromechanical instrument is also known as a moving coil galvanometer.
The galvanometer consists of  two concave-shaped permanent magnets, providing a uniform radial magnetic field in the annular region. In the center, a pivoted coil of fine copper wire is placed in the uniform...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Velocity and Position by Graphical Method

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Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
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相关实验视频

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MatGD: 材料图形数字化器

Jaewoong Lee1, Wonseok Lee1, Jihan Kim1

  • 1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.

ACS applied materials & interfaces
|December 26, 2023
PubMed
概括
此摘要是机器生成的。

我们创建了Material Graph Digitizer (MatGD) 来从科学图表中提取数据. 这个工具准确地将数据线数字化,帮助材料科学研究和发现.

关键词:
电池 电池 电池 电池 电池 电池催化剂是一种催化剂.数据挖掘是数据挖掘的一个方法.图形采矿是采矿的重要组成部分.机器学习是机器学习.金属有机框架 (MOFs) 是一种金属有机框架.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 数据科学数据科学数据科学

背景情况:

  • 科学出版物包含大量的数字形式的有价值的数据.
  • 手动提取这些数据耗时且容易出现错误.
  • 需要自动化工具来有效地从已发表的科学图表中提取数据.

研究的目的:

  • 开发一个自动化工具,材料图表数字化器 (MatGD),用于数字化科学图表中的数据.
  • 提高从研究出版物中提取数据的准确性和效率.
  • 为了促进数据的收集,用于机器学习模型培训材料科学.

主要方法:

  • MatGD算法包括四个步骤:图形识别,轴/数据分离,数据线辨别 (包括传说匹配) 和数据提取.
  • 该工具应用于62,534篇关于电池,催化和金属有机框架 (MOF) 的论文中的501,045个数字.
  • 基于传奇检测和数据线分离的准确性来评估性能.

主要成果:

  • 在传说标记和文本检测方面,MatGD实现了超过99%的准确性.
  • 该工具在数据线分离方面显示出66%的准确性,超过了现有的工具.
  • 成功处理了来自关键材料科学领域的大量数据数据集.

结论:

  • MatGD是一种高精度和高效的工具,用于从科学图表中数字化数据.
  • 这种工具可以显著加速从文献中收集实验数据.
  • 提取的数据可以为增强材料预测和发现的机器学习模型提供动力.