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相关概念视频

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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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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Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Statically Indeterminate Problem Solving01:16

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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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...
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Surrogate Model Development for Digital Experiments in Welding
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基于复杂软件网络的软件技术债务预测.

Bo Jiang1, Jiaye Cen1, Erluan Zhu1

  • 1School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, China.

PloS one
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PubMed
概括
此摘要是机器生成的。

本研究引入了社交网络分析 (SNA) 度量来改进软件技术债务预测 (TDP) 模型. 结合SNA和传统指标显著提高了预测准确性,XGBoost显示出最佳性能.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 软件技术债务预测 (TDP) 模型经常产生不满意的表现.
  • 现有的模型主要依赖于与技术债务 (TD) 相关的指标,限制了它们的有效性.
  • 社交网络分析 (SNA) 的指标通过分析软件元素作为一个网络,提供了一个新的视角.

研究的目的:

  • 探索SNA指标在提高软件技术债务预测方面的有效性.
  • 通过将SNA指标与现有的TD相关指标相结合,提出一个改进的TDP方法.
  • 用组合度量套件来评估机器学习分类器的性能.

主要方法:

  • 软件被建模为一个类依赖网络来计算SNA指标.
  • 通过合并SNA和TD相关的指标,创建了一个Combined Metric Suite (CMS).
  • 使用7个常见的机器学习分类器来使用CMS构建TDP模型.

主要成果:

  • 综合度量套件 (CMS) 显著提高了现有的TDP模型的性能.
  • 在测试的分类器中,XGBoost表现出优异的性能,达到0.77的[公式:参见文本],MI比率约为0.10,回忆接近0.87.
  • 分析了不同度量组合的相对有效性.

结论:

  • 整合SNA指标为提高软件技术债务预测提供了一个有希望的途径.
  • 使用CMS和机器学习分类器的拟议方法为TDP提供了更有效的方法.
  • 进一步的研究可以探索不同的度量组合和分类器,以获得最佳的结果.