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相关概念视频

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Energy and Power Signals01:17

Energy and Power Signals

In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
Instrument Transformers01:23

Instrument Transformers

Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
Overcurrent Relays01:26

Overcurrent Relays

Overcurrent relays, crucial for circuit protection, are connected to the secondary current of a current transformer. There are two primary types of overcurrent relays: instantaneous and time-delay.
Instantaneous overcurrent relays activate immediately when the input current exceeds a predetermined value, known as the pickup current, instantly energizing the circuit breaker trip coil. This rapid response is vital for addressing severe faults quickly.
Time-delay overcurrent relays, on the other...
Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...

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相关实验视频

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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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SignalFormer:基于无人机射频信号的自动无人机识别混合变压器

Xiang Yan1, Bing Han1, Zhigang Su1

  • 1Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

本研究介绍了SignalFormer,这是用于自动无人机识别 (ADI) 的混合变压器模型. SignalFormer显著提高了无人机信号识别准确度,即使有噪音和干扰.

科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 信号处理 信号处理

背景情况:

  • 在民用应用中越来越多地使用无人机,需要强大的自动无人机识别 (ADI) 系统.
  • 目前用于ADI的卷积神经网络 (CNN) 方法由于卷积运算符的局部连接性而面临局限性,阻碍了射频 (RF) 信号识别.
  • 恶意无人机活动带来了重大安全风险,需要先进的检测和识别技术.

研究的目的:

  • 开发一种创新的混合变压器模型,用于增强无人机自动识别 (ADI).
  • 为了解决CNN在捕获无人机检测RF信号中的全球时间/频率相关性的局限性.
  • 在具有挑战性的环境条件下提高无人机信号识别的准确性和可靠性.

主要方法:

  • 提出了一种混合变压器模型,SignalFormer,集成基于CNN的代币化方法,用于生成与本地环境相关的时间频率 (T-F) 代币.
  • 在变压器内采用了一种高效的封闭式自我注意机制,以捕捉T-F令牌之间的全球相关性.
  • 将相位信息纳入输入数据,以提高模型性能.
  • 在高斯白噪声和同频信号干扰条件下对两个公共数据集进行模型评估.

主要成果:

  • SignalFormer实现了高识别精度:97.57%和98.03%的粗粒度任务,和97.48%和98.16%的细粒度任务.
关键词:
自动无人机识别 无人机自动识别深度学习是一种深度学习.无人机的互联网 无人机的互联网时间频率分析

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  • 通过课堂增量学习评估,在处理以前未见的无人机信号类别方面表现出能力.
  • 该模型显示了对噪声和信号干扰的弹性,这对于现实世界的ADI应用至关重要.

    • 结论:

    • 拟议的SignalFormer模型在无人机自动识别技术方面取得了重大进展.
    • 混合方法有效地将局部特征提取 (CNN) 与全球上下文建模 (变压器) 结合起来,用于优异的射频信号分析.
    • SignalFormer为缓解恶意无人机飞行带来的威胁提供了一个有希望和可靠的解决方案.