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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

147
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...
147

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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基于中间板块模式视觉检查和BWO-DNN算法的板块平面视图模式的智能优化控制.

Zhong Zhao1, Chujie Liu1, Jiawei Wang1

  • 1State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China.

Materials (Basel, Switzerland)
|July 12, 2025
PubMed
概括

这项研究引入了一种新的BWO-DNN模型,用于预测板块作物模式,显著减少板块头部和尾部的不规则区域. 这种智能方法通过优化平面视图模式控制来提高板块产量.

关键词:
BWO-DNNN 在线观看这是一个PVPCPC.中间板块的中间板块.机器学习是机器学习.盘子 盘子 盘子 盘子

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 制造过程 制造过程 制造过程
  • 工业中的人工智能

背景情况:

  • 板生产产量受到作物砍伐和边缘损失的重大影响.
  • 准确预测和控制板平面视图模式对于最小化浪费至关重要.

研究的目的:

  • 开发一个智能模型来预测板块平面视图模式.
  • 为了优化板平面视图模式的控制参数.
  • 为了减少板材制造中的材料损失.

主要方法:

  • 使用图像处理捕获中间板块和成品板块的平面视图模式的检测方案.
  • 使用白优化-深度神经网络 (BWO-DNN) 算法的预测和控制模型的开发.
  • 使用BWO算法对深度神经网络 (DNN) 的超参数优化.

主要成果:

  • BWO-DNN模型显示出强大的预测和控制性能,通过合适度 (R2) 和平均绝对误差 (MAE) 进行验证.
  • 实现了对板式作物模式的智能预测和对平面视图模式控制的参数优化.
  • 现场验证显示,不规则的板头面积减少了17.2%,不规则的板尾面积减少了22.6%.

结论:

  • BWO-DNN模型为板材制造中的智能预测和控制提供了有效的解决方案.
  • 提出的方法大大减少了材料浪费,提高了整体生产效率.
  • 这种智能系统在工业板生产环境中具有实际应用性.