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Related Concept Videos

Energy Losses in Transformers01:21

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the copper windings...
Source Transformation for AC Circuits01:11

Source Transformation for AC Circuits

The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
Line Protection with Impedance Relays01:27

Line Protection with Impedance Relays

Coordinating time-delay overcurrent relays in complex radial systems and directional overcurrent relays in multi-source transmission loops can be challenging. Impedance relays address these issues by responding to the voltage-to-current ratio, specifically measuring the apparent impedance of a line. These relays become more sensitive during faults as current increases and voltage decreases, thereby reducing the apparent impedance.
Under normal conditions, low load currents keep the measured...
Differential Relays01:20

Differential Relays

Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...

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Related Experiment Videos

Transformer-Based Multi-Source Transfer Learning for Intrusion Detection Models with Privacy and Efficiency Balance.

Baoqiu Yang1, Guoyin Zhang1, Kunpeng Wang2

  • 1College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces TrMulS, a novel intrusion detection framework enhancing cross-domain adaptability and privacy. The model effectively detects minority-class attacks by integrating federated learning, generative adversarial networks, and transformers.

Keywords:
cross-domain securityfederated learningintrusion detectionmulti-source transfertransformer

Related Experiment Videos

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Current intrusion detection systems struggle with cross-domain adaptability, privacy preservation, and detecting rare attacks.
  • Existing methods often fail to generalize across different network environments and protect sensitive data.

Purpose of the Study:

  • To propose a novel intrusion detection model framework, TrMulS, that addresses limitations in cross-domain adaptability, privacy, and minority-class attack detection.
  • To develop a robust system for enhanced security intelligence analysis.

Main Methods:

  • TrMulS integrates federated learning, generative adversarial networks (GANs) with multispace feature enhancement, and transformers with multi-source transfer.
  • Local feature extraction via CNNs, subset construction, and transformer-based attention mechanisms are employed.
  • Cross-domain transfer is achieved using an improved Exchange-GAN and Maximum Mean Discrepancy (MMD) to minimize feature distribution differences.
  • A federated transfer learning strategy with encrypted parameter aggregation ensures privacy and global model optimization.

Main Results:

  • Experiments on ISCX2012, KDD99, and NSL-KDD datasets demonstrate significant improvements in detection accuracy within cross-domain scenarios.
  • The TrMulS framework shows superior performance compared to existing methods in challenging detection environments.
  • The proposed method effectively balances efficiency, privacy, and detection accuracy.

Conclusions:

  • TrMulS offers a novel paradigm for cross-domain security intelligence analysis, overcoming key limitations of current intrusion detection methods.
  • The integration of advanced AI techniques provides a more adaptable, private, and effective solution for network security.
  • This framework represents a significant advancement in building resilient and intelligent intrusion detection systems.