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State Space to Transfer Function01:21

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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.
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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.
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The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
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Updated: May 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Dual-domain style transfer network.

Changyang Hu1, Shibao Sun1, Pengcheng Zhao1

  • 1College of the software, Henan University of Science and Technology, Luoyang, China.

Science Progress
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Dual-Domain Style Transfer Network to reduce artifacts in arbitrary style transfer. The novel approach enhances style semantics and global textures for improved image quality.

Keywords:
Arbitrary style transferadaptive normalizationfrequency domainlong-range dependenciesself-attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Arbitrary style transfer is gaining traction for its diverse applications.
  • Current methods often result in artifacts and poor textures due to limited semantic understanding and long-range dependency capture.

Purpose of the Study:

  • To develop an advanced arbitrary style transfer method that minimizes artifacts and enhances texture quality.
  • To address the limitations of existing approaches by exploring style semantic distribution and long-range dependencies.

Main Methods:

  • Introduced a Dual-Domain Style Transfer Network.
  • Incorporated Adaptive Normalization with Style Semantics Awareness using self-attention.
  • Implemented Global Style Texture Enhancement in the frequency domain.

Main Results:

  • Achieved state-of-the-art performance on MSCOCO and Wikiart datasets.
  • Secured top scores in Learned Perceptual Image Patch Similarity (0.616), Structural Similarity Index (0.467), and content loss (2.31).
  • Obtained the second-best score in style loss (3.08).

Conclusions:

  • The proposed Dual-Domain Style Transfer Network effectively reduces artifacts and improves texture quality.
  • The integration of style semantics awareness and frequency domain enhancement offers a significant advancement in arbitrary style transfer.