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State Space Representation01:27

State Space Representation

285
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
285
State Space to Transfer Function01:21

State Space to Transfer Function

302
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
302
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
124
Transfer Function to State Space01:23

Transfer Function to State Space

403
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
403
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

184
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
184
Normal and Tangetial Components: Problem Solving01:24

Normal and Tangetial Components: Problem Solving

223
Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
223

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Video Experimental Relacionado

Updated: Sep 10, 2025

Photorealistic Learned Landscapes for Augmented Reality
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CompletamientoMamba: domar el modelo espacial del estado para completar la nube de puntos

Zhiheng Fu, Jiehua Zhang, Longguang Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 25, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    CompletionMamba reconstruye efectivamente las formas 3D a partir de escaneos parciales mediante el uso de modelos de espacio de estado (SSM) para capturar dependencias de largo alcance. Este nuevo enfoque mejora la finalización de la nube de puntos mediante la integración de información de forma para una mayor precisión.

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    Área de la Ciencia:

    • Visión por computadora
    • Reconstrucción de la forma en 3D
    • Aprendizaje profundo

    Sus antecedentes:

    • La finalización de la nube de puntos es vital para reconstruir formas 3D a partir de datos incompletos.
    • Los transformadores sobresalen en las dependencias globales, pero luchan con los costos computacionales para secuencias largas.
    • Los modelos de espacio de estado (SSM) ofrecen eficiencia de memoria para secuencias largas, pero enfrentan desafíos con nubes de puntos no ordenadas debido a los requisitos de causalidad.

    Objetivo del estudio:

    • Desarrollar una nueva red de aprendizaje profundo para una finalización eficiente y precisa de la nube de puntos.
    • Abordar las limitaciones de los métodos existentes para capturar relaciones espaciales 3D complejas e información de forma.

    Principales métodos:

    • Se introdujo CompletionMamba, una red basada en el modelo espacial estatal (SSM) para la finalización de la nube de puntos.
    • Desarrolló un método para estructurar causalmente nubes de puntos reorganizando coordenadas y definiendo espacios de vecindad locales.
    • Códigos de forma integrados en el modelo Mamba para permitir la propagación de información de forma para el modelado integral.

    Principales resultados:

    • CompletionMamba captura efectivamente las dependencias globales y locales dentro de las nubes de puntos.
    • El Mamba consciente de la forma propuesto mejora significativamente el modelado de formas 3D completas.
    • Lograr un rendimiento de última generación en los conjuntos de datos MVP y PCN para las tareas de finalización de la nube de puntos.

    Conclusiones:

    • CompletionMamba ofrece una solución potente y eficiente para la finalización de la nube de puntos en 3D.
    • La integración de los SSM con los mecanismos conscientes de la forma representa un avance significativo en el campo.
    • Este método demuestra un rendimiento superior en la reconstrucción de formas 3D completas a partir de escaneos parciales.