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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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通过Tensor原型对齐,为智能工艺制造提供零射击故障诊断.

Bocheng Ren, Laurence T Yang, Xin Nie

    IEEE transactions on neural networks and learning systems
    |March 18, 2024
    PubMed
    概括

    本研究介绍了MetaEvolver,这是一个用于工艺制造过程中零射击故障诊断的新框架. 它增强了识别未见故障的能力,提高了智能制造环境中的准确性和通用性.

    科学领域:

    • 工艺制造 工艺制造 制造业
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 识别看不见的故障对于工艺制造中的数字化转型至关重要.
    • 由于有限的概括性,现有的方法与未见的缺陷作斗争.
    • 传统的零射击识别在动态制造场景中表现不佳.

    研究的目的:

    • 为工艺制造开发基于张量器的零射击故障诊断框架 (MetaEvolver).
    • 为了提高故障诊断的准确性,并将其推广到未见的故障领域.
    • 在动态和未见故障场景中解决当前方法的局限性.

    主要方法:

    • 提出了不确定元域的概念,并以类级属性为指导构建了样本原型.
    • 开发了一个基于张量器的框架 (MetaEvolver) 用于演化的双原型分布.
    • 采用了一种策略,从另一种模式注入原型分发信息,以增强对齐.
    • 利用设计的损失函数用于知识传输和未见域泛化.

    主要成果:

    • 在五个工艺制造数据集中,MetaEvolver在零射击故障诊断方面展示了卓越的性能.
    • 实现了对未见故障识别的准确性和通用性的显著改进.

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  • 在五个零射击基准上验证了框架的潜力.

    • 结论:

    • 在智能工艺制造中,MetaEvolver为零射击故障诊断提供了强大的解决方案.
    • 该框架有效地处理未见的故障,并增强域名通用性.
    • 这种基于张数的方法显示出极大的优越性和实际应用的潜力.