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相关概念视频

Open and closed-loop control systems01:17

Open and closed-loop control systems

737
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
737
PD Controller: Design01:26

PD Controller: Design

224
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
224
PI Controller: Design01:24

PI Controller: Design

263
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
263

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相关实验视频

Updated: Jun 29, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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通过算法集成电路共同设计,通过精确和低功耗的闭环神经调节.

Jie Yang1, Shiqi Zhao1, Junzhe Wang1

  • 1CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China.

Frontiers in neuroscience
|March 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于闭环神经调节的AI集成电路,显著减少了预测设备中的错误报警和功耗.

关键词:
在ASIC的基础上,它是ASIC.人工智能的人工智能是人工智能.封闭循环的封闭循环.由事件驱动的事件驱动.植入式设备可以植入设备.低功率的低功率电机通过神经调节进行神经调节.

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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相关实验视频

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科学领域:

  • 神经调节是一种神经调节.
  • 人工智能的人工智能
  • 集成电路 集成电路

背景情况:

  • 植入式神经调节器件可以改善神经疾病的治疗,但在开放循环系统中面临过度刺激等局限性.
  • 闭环系统提供适应性刺激,但存在高误报警率和能源效率低下的问题.
  • 目前的技术阻碍了对大脑疾病的响应性神经调节的全部潜力.

研究的目的:

  • 开发一种人工智能集成电路联合设计,以解决闭环神经调节中的错误报警和能源消耗.
  • 为了证明这个系统的有效性,使用闭环预测在线演示.
  • 提高用于神经治疗的可植入装置的精度和效率.

主要方法:

  • 利用神经网络的搜索和量子化来开发两个模型:对灵敏度的二进制神经网络和对错报警拒绝的卷积神经网络.
  • 在55nm技术中制造了一个专门的低功耗处理器,以实现神经网络模型.
  • 设计了具有可重新配置和事件驱动处理功能的应用特定集成电路 (ASIC).

主要成果:

  • 使用优化的神经网络模型实现了0.1/h的低误报警率来预测发作.
  • 制造出来的ASIC占据了一个小的面积 (5mm2) 并显示出低的平均功耗 (142μW).
  • 与现有最先进的系统相比,提出的解决方案显著降低了虚假报警率和功耗.

结论:

  • 与人工智能集成电路的共同设计有效地减少了闭环神经调节系统中的错误报警和功耗.
  • 这一进步有望为治疗神经系统疾病的更精确,更高效,更安全的可植入装置提供希望.
  • 开发的系统比目前的响应性大脑刺激技术有了显著的改进.