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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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Magnetic Vector Potential01:15

Magnetic Vector Potential

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In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
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Vector Transformation in Rotating Coordinate Systems01:16

Vector Transformation in Rotating Coordinate Systems

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Consider a vector rotating about an axis with an angular velocity, such that its tip sweeps a circular path.
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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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Position Vectors01:29

Position Vectors

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A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
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Acceleration Vectors01:30

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In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
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Video Experimental Relacionado

Updated: Feb 20, 2026

Direct Imaging of Laser-driven Ultrafast Molecular Rotation
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Direct Imaging of Laser-driven Ultrafast Molecular Rotation

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Generación de haces de vórtice vectoriales multimodo habilitada por una D²NN giratoria

Huijun Hu, Gongyuan Wang, Le Wang

    Optics express
    |February 18, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Una novedosa red neuronal profunda difractiva (R-D²NN) giratoria genera versátiles haces de vórtice vectoriales (VVBs) multimodo. Este dispositivo único y reconfigurable ofrece generación de VVB de alta fidelidad y sin entrenamiento para comunicaciones ópticas y detección.

    Palabras clave:
    haces de vórtice vectorialesredes neuronales profundas difractivasgeneración de hacescomunicaciones ópticasóptica adaptativamanipulación de hacesmodos de momento angular orbitalpolarización

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    Área de la Ciencia:

    • Óptica y Fotónica
    • Aplicaciones de Aprendizaje Automático
    • Generación y Manipulación de Haces

    Sus antecedentes:

    • Los haces de vórtice vectoriales (VVBs) son cruciales para aplicaciones ópticas avanzadas debido a sus propiedades únicas de polarización y momento angular orbital (OAM).
    • La generación de diversos VVBs a menudo requiere configuraciones complejas o reentrenamiento de dispositivos, lo que limita la flexibilidad y la eficiencia.

    Objetivo del estudio:

    • Introducir una novedosa arquitectura de red neuronal profunda difractiva giratoria (R-D²NN) para la generación dinámica de VVBs.
    • Demostrar una solución de un solo elemento y sin entrenamiento para producir VVBs multimodo con momento angular orbital (OAM) y polarización controlados.

    Principales métodos:

    • Una arquitectura R-D²NN reconfigurable que utiliza capas difractivas cuya rotación controla la salida del VVB.
    • Superposición coherente de campos ópticos polarizados ortogonalmente con distintos modos OAM.
    • Verificación mediante mediciones de parámetros de Stokes y simulaciones numéricas.

    Principales resultados:

    • Las simulaciones numéricas mostraron la generación de hasta 16 haces de vórtice vectoriales (VVBs) multimodo con una pureza de modo >99% utilizando cinco capas difractivas.
    • La realización experimental logró una pureza de modo promedio del 85% con un sistema de dos capas en un modulador de luz espacial (SLM).

    Conclusiones:

    • La R-D²NN ofrece un método de generación de VVB dinámico y de alta fidelidad.
    • Este enfoque de elemento único y sin entrenamiento es prometedor para comunicaciones ópticas, detección y otras aplicaciones fotónicas avanzadas.