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Related Concept Videos

Transfer Function to State Space01:23

Transfer Function to State Space

392
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...
392
State Space to Transfer Function01:21

State Space to Transfer Function

297
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:
297
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

810
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.
To derive the transfer function, consider a general nth-order linear time-invariant...
810
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

147
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.
In the absence...
147
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
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...
140
Transformers in Distribution System01:27

Transformers in Distribution System

153
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
153

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Related Experiment Video

Updated: Sep 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

629

Total Style Transfer with a Single Feed-Forward Network.

Minseong Kim1, Hyun-Chul Choi2

  • 1Alchera Inc., 225-15 Pangyoyeok-Ro, Bundang-gu Seongnam, Seongnam-si 13494, Korea.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces total style transfer, a novel method that effectively transfers scale-across style patterns from a style image to a content image. It achieves this using multi-scaled feature maps, improving upon existing techniques.

Keywords:
computer visiondeep learningimage style transferinter-scale transformerintra-scale transformermulti-scaled style transfer

Related Experiment Videos

Last Updated: Sep 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

629

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Image Processing

Background:

  • Recent image style transfer methods can quickly alter an image's style.
  • Existing methods struggle to transfer style patterns across different scales effectively.

Purpose of the Study:

  • To propose a novel style transfer method, "total style transfer," addressing the scale-across pattern limitation.
  • To enhance the fidelity of style transfer by incorporating multi-scale statistics.

Main Methods:

  • Utilizing intra/inter-scale statistics of multi-scaled feature maps for style transformation.
  • Employing a general feature transform layer to capture multi-scaled style.
  • Generating stylized images with a single decoder network incorporating skip-connections.

Main Results:

  • Successfully transferred scale-across style patterns from style to content images.
  • Achieved lower memory consumption and faster feed-forwarding speed compared to cascade schemes.
  • Obtained the lowest style loss among recent style transfer methods.

Conclusions:

  • The proposed total style transfer method effectively overcomes the scale-across pattern limitation.
  • The method offers improved efficiency and performance in image style transfer.
  • This approach enables high-fidelity style transfer with reduced computational resources.