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相关概念视频

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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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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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...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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...
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Convolution Properties I01:20

Convolution Properties I

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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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对于紧网络表示的合张量分解.

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    本研究介绍了合过器分解,以减少卷积神经网络中的冗余性. 该方法通过在类似的过器中共享因素,降低参数和计算,有效地压缩模型.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 深度学习模型中的卷积层包含冗余过器,产生重叠的输出.
    • 这种冗余导致模型参数和计算复杂性的增加.

    研究的目的:

    • 引入一种新的过器分解方法,利用卷积过器中的冗余性.
    • 为了降低模型大小和计算成本,同时保持性能.

    主要方法:

    • 建议使用合的正规多分解 (CPD) 进行合过器分解.
    • 在分解前基于自定义指标实现过器聚类,以提高效率.
    • 在确定过器组内应用较少限制性的合约束.

    主要成果:

    • 结合过器分解方法显著降低了模型参数和计算复杂性.
    • 跨多种架构,数据集和任务的实验验证显示了与最先进的压缩技术相比具有竞争力的性能.
    • 该方法有效地解决了卷积神经网络中的过器冗余问题.

    结论:

    • 合过器分解是压缩深度学习模型的有效技术.
    • 该方法为高效的神经网络设计和部署提供了一个有希望的方向.
    • 提出的方法在模型压缩和性能之间实现了有利的权衡.