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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

126
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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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Neural Control of Respiration01:18

Neural Control of Respiration

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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
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Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Using Generative Art to Convey Past and Future Climate Transitions
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可解释的序列对序列GRU神经网络用于污染预测.

Sara Mirzavand Borujeni1, Leila Arras1,2, Vignesh Srinivasan1

  • 1Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany.

Scientific reports
|June 19, 2023
PubMed
概括
此摘要是机器生成的。

本研究使用可解释AI (XAI) 来理解深度学习空气污染预测. 层级相关传播 (LRP) 识别了关键的天气和时间特征,推动了污染物水平,有助于控制空气质量.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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科学领域:

  • 环境科学 环境科学
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 深度神经网络 (DNN) 在空气质量预测方面表现有前途,优于传统方法.
  • 从历史上看,DNN一直被视为"黑子",限制了对其预测的理解.
  • 可解释的人工智能 (XAI) 技术为DNN决策过程提供了洞察力.

研究的目的:

  • 适应和应用层级相关性传播 (LRP),XAI技术,到一个序列对序列的神经网络模型.
  • 确定影响主要空气污染物的积累的主要气象和时间特征 ([公式:见文本],[公式:见文本],[公式:见文本],[公式:见文本]).
  • 证明XAI在了解和潜在控制空气污染方面的实用性.

主要方法:

  • 采用了一种序列对序列神经网络架构,用于空气污染预测,该架构包含了Gated Recurrent Unit (GRU) 层.
  • 扩展了层级相关性传播 (LRP) 技术来分析开发的神经网络模型.
  • 生成解释热图以可视化污染物积累预测的特征重要性.

主要成果:

  • LRP成功地确定了重要的气象和时间输入特征,这些特征有助于预测四种主要的空气污染物.
  • 识别的特征与环境科学和污染研究的既定知识保持一致.
  • 由LRP提供的解释热图为模型的预测行为提供了可解释的见解.

结论:

  • XAI,特别是LRP,适用于理解复杂的空气污染预测模型.
  • 这些发现为有针对性的空气污染控制和减缓战略开辟了新的可能性.
  • 将XAI应用于深度学习模型可以提高对环境预测的透明度和信任.