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相关概念视频

Network Function of a Circuit01:25

Network Function of a Circuit

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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
291
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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Buffers: Buffer Capacity01:09

Buffers: Buffer Capacity

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Buffer capacity is the quantitative measure of a buffer to resist the change in pH. As shown in the following equation, the buffer capacity, denoted by 'beta', is expressed as the number of moles of acid or base needed to change the pH of a one-liter buffer solution by 1 unit. Here, Ca and Cb indicate the number of moles of acid and base, respectively. Note that dpH represents the change in pH.
In the graph, pH is plotted as a function of the number of moles of base (Cb) added to a weak...
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Block Diagram Reduction01:22

Block Diagram Reduction

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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
215
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

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In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.
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相关实验视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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二元图形卷积网络与容量探索

Junfu Wang, Yuanfang Guo, Liang Yang

    IEEE transactions on pattern analysis and machine intelligence
    |December 13, 2023
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    概括
    此摘要是机器生成的。

    本研究介绍了二进制图形卷积网络 (Bi-GCNs),通过二元化参数和属性来压缩图形神经网络 (GNNs). 这大大减少了内存,并加快了对大型图形数据的推断速度.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 图形神经网络的神经网络

    背景情况:

    • 图形神经网络 (GNN) 需要大量的内存来处理大型的属性图形.
    • 由于完全精确的处理,现有的GNN面临内存限制的局限性.

    研究的目的:

    • 开发一个内存高效和加速的GNN模型.
    • 用有限的资源来解决处理大型归因图的挑战.

    主要方法:

    • 拟议的二进制图卷积网络 (Bi-GCN) 二元化网络参数和节点属性.
    • 使用二进制运算,而不是浮点矩阵乘法.
    • 引入了一种新的渐变近似,用于Bi-GCNs的反向传播.
    • 开发了一个透覆盖假设,以解决二元化GNN的潜在容量问题.

    主要成果:

    • 对参数和数据的内存消耗平均减少了~31倍.
    • 在引用网络 (Cora,PubMed,CiteSeer) 上,推断速度平均加速了~51倍.
    • 将扩展二进制化扩展到其他GNN变体,实现类似的效率增长.
    • 在七个节点分类数据集上证实了与完全精确基线相对应的性能.
    • 验证了透覆盖假设用于压缩GNN中的容量管理.

    结论:

    • 双GCN为GNN提供了显著的压缩和加速.
    • 提出的方法在内存限制下有效处理大型图形数据.
    • 覆盖假设为二元化GNN的容量限制提供了一个可行的解决方案.