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Reducing Line Loss

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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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Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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Lossy Lines and Overvoltages01:22

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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
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Related Experiment Video

Updated: Sep 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

501

Lossless steganographic network via model arithmetic operations.

Yao Fan1, Fuqiang Di1, Mingqing Zhang1

  • 1Engineering University of PAP, Xi'an, 710086, Shaanxi, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel lossless steganographic network method using model arithmetic operations. This technique securely embeds deep neural networks (DNNs) for covert transmission without compromising performance.

Keywords:
Arithmetic operationsCovert communicationDeep neural networksLosslessSteganography

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Area of Science:

  • Computer Science
  • Cryptography
  • Machine Learning

Background:

  • Deep neural networks (DNNs) are used for steganography, requiring secure transmission.
  • Existing methods for covertly transmitting DNNs can degrade the secret model's performance.
  • Ensuring the integrity of secret models during steganography is crucial for their functionality.

Purpose of the Study:

  • To propose a novel method for lossless steganographic transmission of DNNs.
  • To ensure the complete transfer and integrity of secret models during the steganography process.
  • To enable secure and covert communication of DNNs without performance degradation.

Main Methods:

  • Developed a lossless steganographic network using model arithmetic operations.
  • Hid secret model parameters within a stego model using arithmetic operations.
  • Employed an iterative training approach to optimize the stego model for effective steganography.

Main Results:

  • The proposed method achieves lossless steganographic transmission of DNNs.
  • The integrity and performance of the secret model are preserved throughout the process.
  • Experimental results demonstrate secure and efficient delivery of high-performing steganographic networks.

Conclusions:

  • The model arithmetic-based approach offers a secure and effective solution for covert DNN transmission.
  • This method overcomes limitations of existing techniques by ensuring model integrity.
  • The findings contribute to secure machine learning model deployment and communication.