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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.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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使用转移学习解决光学矩阵乘法器建模中的数据稀缺问题.

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    此摘要是机器生成的。

    转移学习在训练光学矩阵乘法器的神经网络模型时显著减少错误,即使实验数据有限. 这种方法使用合成数据进行预训练,提高光子芯片应用的准确性.

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    科学领域:

    • 光子学是指光子学的使用方法.
    • 光学计算是指光学计算的应用.
    • 机器学习 机器学习

    背景情况:

    • 为光学矩阵乘法器训练神经网络 (NN) 模型通常需要大量的实验数据.
    • 数据稀缺性在开发精确的光子设备方面构成了重大挑战.

    研究的目的:

    • 评估转移学习用于训练NN模型的基于马赫-泽恩德干扰仪网状光学矩阵乘法器的转移学习.
    • 为了解决NN模型培训中的实验数据稀缺性,用于光子应用.

    主要方法:

    • 从分析模型中使用合成数据进行NN模型预训练.
    • 在有限的实验数据下微调预训练模型.
    • 使用规范化技术和整体平均化.

    主要成果:

    • 与独立的分析模型或NN模型相比,转移学习显著减少了建模错误.
    • 在3x3矩阵权重上实现了<1dB的平方中根误差.
    • 成功训练模型,只使用25%的可用实验数据.

    结论:

    • 转移学习是克服数据稀缺的有效策略,用于训练光学矩阵乘法器的NN模型.
    • 拟议的方法可以在减少实验力度的情况下准确地建模光子装置.
    • 这种方法提高了开发复杂光子集成电路的实用性.