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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Light Acquisition02:16

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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相关实验视频

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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将基于模型的深度学习适应多个获取条件:Ada-MoDL

Aniket Pramanik1, Sampada Bhave2, Saurav Sajib2

  • 1Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.

Magnetic resonance in medicine
|June 19, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度学习框架,Ada-MoDL,使用单一模型在各种环境中进行高质量的并行MRI重建. 这种方法需要更少的培训数据,并消除了对多个专业网络的需求.

关键词:
收购设置 收购设置适应性框架 适应性框架平行MRIMRI并行MRI并行MRI没有注册的深度学习.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 低采样并行MRI数据采集通常需要不同的设置单独的模型.
  • 现有的方法需要训练和存储多个网络,增加复杂性和数据要求.

研究的目的:

  • 引入基于单一模型的深度网络,以从低样本并行MRI数据中获得多个序列和设置的高质量重建.
  • 开发一个适应性框架,可以在各种获取参数上概括,包括现场强度和对比度.

主要方法:

  • 建议采用单一的未滚动深度网络架构 (Ada-MoDL),通过功能和规范化参数扩展来适应不同的采集设置.
  • 一个多层感知子模型从代表特定获取设置的条件向量中得出缩放权重.
  • 网络参数和卷积神经网络 (CNN) 权重通过使用多种多设定数据进行联合训练.

主要成果:

  • 与对所有数据进行单一模型训练相比,适应性框架在所有测试的获取条件中始终表现出更好的性能.
  • 与独立训练的网络相比,拟议的方案要求每设置的训练数据要少得多,以实现良好的性能.

结论:

  • 根据Ada-MoDL框架,可以为多个MRI采集设置提供基于单个模型的无网络.
  • 这种方法减少了训练和存储多个网络的需求,并减少了每个设置所需的训练数据.