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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
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There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte properties and...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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在室温半导体探测器中,在没有先验知识的情况下识别缺陷,使用物理启发的机器学习模型.

Srutarshi Banerjee1, Miesher Rodrigues2, Manuel Ballester1

  • 1Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA.

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概括

灵感来自物理学的机器学习模型现在可以识别室温半导体辐射探测器 (RTSD) 中未知的缺陷. 这些模型在体积上对缺陷进行了表征,改善了计算机断层扫描 (CT) 等应用的材料表征.

关键词:
运输费 运输费 运输费 运输费缺陷 缺陷 缺陷 缺陷 缺陷脱落的 脱落的 脱落机器学习是机器学习.材料表征材料的表征.启发物理的机器学习模型 (PI-ML)室温半导体检测器检测器半导体检测器捕捉陷 捕捉陷是一种捕捉.捕捉中心,捕捉中心.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 半导体物理 半导体物理
  • 机器学习 机器学习

背景情况:

  • 室温半导体辐射探测器 (RTSD),如CdZnTe,对于计算机断层扫描 (CT) 和其他成像应用至关重要.
  • 在RTSD中影响电子和孔传输的材料缺陷的特征至关重要,但劳动密集型,缺陷在设备之间差异很大.
  • 现有的缺陷在表征之前往往是未知的,这对准确的材料评估构成了挑战.

研究的目的:

  • 开发和演示一个灵感来自物理的机器学习 (PI-ML) 模型,能够识别RTSD中未知的材料缺陷.
  • 从体积上描述RTSD缺陷,捕捉因制造和材料特性而产生的空间异质性.
  • 评估PI-ML模型在整个检测器体积中确定特定缺陷的存在或不存在的能力.

主要方法:

  • 开发一个PI-ML模型,旨在考虑RTSDs的所有潜在的实物缺陷.
  • 对RTSD进行体积分离,以实现空间解决的缺陷分析.
  • 应用PI-ML模型来识别和定位缺陷,包括电子和孔的捕获,脱落和重组地点.

主要成果:

  • 该PI-ML模型成功地确定了在RTSD中存在或缺少特定的,以前未知的缺陷.
  • 在检测器上以空间分辨的体积方式实现了缺陷识别.
  • 该模型展示了捕捉RTSD材料固有的缺陷异质性的能力.

结论:

  • PI-ML模型提供了一种强大的,数据驱动的方法来描述RTSDs中的复杂缺陷.
  • 这种方法大大降低了与传统缺陷表征相关的劳动强度.
  • 识别未知和空间变化的缺陷的能力提高了RTSDs的质量控制和性能预测,用于CT成像等关键应用.