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相关概念视频

Multimachine Stability01:25

Multimachine Stability

535
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:
535
Cerebrospinal Fluid01:21

Cerebrospinal Fluid

5.1K
Cerebrospinal fluid (CSF) is a colorless liquid that flows around the brain and the spinal cord, playing a vital role in the protection, support, and overall function of the central nervous system (CNS). CSF production, circulation, and absorption are tightly regulated processes essential for the brain and spinal cord to function properly.
CSF Production
CSF is produced mainly in the choroid plexus, a network of capillaries and ependymal cells located within the ventricular system of the brain....
5.1K
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Machines01:19

Machines

542
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
542
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

733
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
733
Clamper Circuit01:14

Clamper Circuit

948
A clamper circuit, also known as a DC restorer, represents a specialized variant of the rectifier circuit, notable for its method of taking the output across the diode rather than the capacitor. This configuration lends to several distinctive applications, particularly in handling square wave inputs.
Within this circuit, the diode's orientation prompts the capacitor to charge up to the level of the most negative peak of the input signal. Upon reaching this state, the diode ceases to...
948

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相关实验视频

Updated: Jan 10, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

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C2-LSM:一个基于Storm-NoC的神经形态处理器,用于使用立方体集群拓学的高精度液态机器.

Enyi Yao, Zhibin Luo, Zongfan Wu

    IEEE transactions on biomedical circuits and systems
    |November 24, 2025
    PubMed
    概括

    我们介绍了C2-LSM,这是一款新型的神经形态处理器,用于液态机器 (LSM) 的立方集群拓. 这种设计在时空任务上实现了高精度和高效率,优于现有的LSM处理器.

    科学领域:

    • 神经形态工程的神经形态工程
    • 尖端神经网络的神经网络.
    • 储水库计算 储水库计算

    背景情况:

    • 液态机器 (LSMs) 是尖端神经网络 (SNNs) 的一种变体,以其低训练复杂性而闻名.
    • 生物大脑展现出生物大脑的表现.
    • 小世界 - - 小世界.
    • 网络结构激发了高效的神经处理.

    研究的目的:

    • 提出C2-LSM,一种通过算法硬件共同设计开发的神经形态处理器.
    • 通过使用LSM来提高各种时空任务的准确性和效率.

    主要方法:

    • 算法级设计:引入了一种新的储存层,采用由生物神经网络启发的立方集群拓.
    • 硬件实现:在AMD Virtex UltraScale+ VCU129 FPGA上开发了一个定制的C2-LSM处理器,具有运行时配置.
    • 芯片上的网络 (NoC):集成了一个Storm路由算法来优化尖端事件传输.

    主要成果:

    • 实现了高分类准确度:98.02%在MNIST上,94.26%在N-MNIST上,93.00%在FSDD上.
    • 与最近进行基准测试的LSM神经形态处理器相比,表现出卓越的性能.
    • 在MNIST上实现了1155 FPS推断和1154 FPS学习速度,功率效率为103 GSOPS/W.

    更多相关视频

    Preparation of Neuronal Co-cultures with Single Cell Precision
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    Preparation of Neuronal Co-cultures with Single Cell Precision

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    A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform
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    相关实验视频

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    Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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    Preparation of Neuronal Co-cultures with Single Cell Precision
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    Preparation of Neuronal Co-cultures with Single Cell Precision

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    A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform
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    A Multi-compartment CNS Neuron-glia Co-culture Microfluidic Platform

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    结论:

    • C2-LSM处理器实现了对时空任务的最先进的准确性和效率.
    • 算法-硬件联合设计对于开发高性能神经形态系统是有效的.
    • 立方集群拓和优化的NoC有助于提高C2-LSM的性能.