A real-time transformer discharge pattern recognition method based on CNN-LSTM driven by few-shot learning

Zheng, Qinghe; Wang, Ruoyu; Tian, Xinyu; Yu, Zhiguo; Wang, Hongjun; Elhanashi, Abdussalam; Saponara, Sergio

doi:10.1016/j.epsr.2023.109241

Cited by 18 publications

(5 citation statements)

References 37 publications

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“…CNN-LSTM [ 46 ]: The input for identifying PDs consists of a dual-channel image jointly constructed with PRPD and PRPS signals. This novel approach utilizes the dual-channel spectrum of discharge signals as a joint driver to optimize the neural network for feature extraction, employing a hybrid deep learning model of CNN and LSTM.…”

Section: Resultsmentioning

confidence: 99%

“…The diagnostic accuracy is considerably improved compared with that of SVM before optimization. Effective processing of small-sample PD data through the powerful flourishing capability of GA-SVM produces a recognition accuracy of up to 80%; CNN-LSTM [46]: The input for identifying PDs consists of a dual-channel image jointly constructed with PRPD and PRPS signals. This novel approach utilizes the dualchannel spectrum of discharge signals as a joint driver to optimize the neural network for feature extraction, employing a hybrid deep learning model of CNN and LSTM.…”

Section: Comparative Identification Experimentsmentioning

confidence: 99%

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PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Deng,

Liu,

Zhu

et al. 2024

Sensors

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Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

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Section: Resultsmentioning

confidence: 99%

Section: Comparative Identification Experimentsmentioning

confidence: 99%

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Deng,

Liu,

Zhu

et al. 2024

Sensors

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“…Furthermore, as a key technology 3 for dynamic spectrum access (DSA), AMC plays a significant role in enhancing spectrum efficiency and expanding communication capacity 4 . In complex electromagnetic environments 5 , AMC technology further demonstrates its potential in monitoring unauthorized devices or signals, particularly in applications such as UAV identification 6 , interference recognition 7 , and electronic countermeasures 8 . In summary, AMC plays a crucial role in optimizing UAV communication performance and ensuring communication security.…”

Section: Introductionmentioning

confidence: 99%

An Ultra Lightweight Neural Network for Automatic Modulation Classification in Drone Communications

Wang,

Fang,

Fan

et al. 2024

Preprint

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Automatic Modulation Classification (AMC) is crucial for unmanned aerial vehicle (UAV) systems in non-cooperative communications. It enables UAVs to effectively identify and track signals transmitted by other communication devices. Deep Learning (DL) has been successfully applied to AMC to improve the accuracy of signal classification. Despite this, many DL-based AMC methods, due to their large number of parameters and high computational complexity, cannot be directly applied to UAV platforms with limited computing power and storage space. To address this challenge, we propose an ultra-lightweight neural network (ULNN). This network incorporates a lightweight convolutional structure, attention mechanism, and cross-channel feature fusion technique. Additionally, we introduce data augmentation (DA) based on signal phase offsets during the model training process, aimed at improving the model’s generalization ability and preventing overfitting. Through experimental validation on the public dataset RML2016.10A, our proposed ULNN network achieves an average precision of 62.83% with only 8,815 parameters and reaches a peak classification accuracy of 92.11% at SNR = 10dB. This demonstrates that our proposed ULNN network maintains high recognition accuracy while keeping the model lightweight, making it highly suitable for deployment in resource constrained environments.

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“…AMC is able to automatically select the optimal modulation scheme based on channel conditions and the distance between drones and ground stations, thereby improving the data transmission rate and signal coverage [3]. In complex electromagnetic environments [4,5], AMC can also effectively identify and eliminate interference from other wireless devices [6], maintaining a stable and reliable connection for drone communications. As the key technique for dynamic spectrum access (DSA) [7], AMC helps to enhance spectrum utilization and communication capacity, which are particularly important for the efficient operation of drone communication systems in the crowded radio spectrum.…”

Section: Introductionmentioning

confidence: 99%

MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

Zheng,

Tian,

et al. 2023

Drones

Self Cite

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Nowadays, automatic modulation classification (AMC) has become a key component of next-generation drone communication systems, which are crucial for improving communication efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency of current methods hinders the practical application of AMC in drone communication systems. In this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer (MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning redundant weights based on information entropy, so it can learn robust modulation knowledge from multimodal signal representations for the AMC task. To the best of our knowledge, this is the first attempt in which the pruning technique and a lightweight transformer model are integrated and applied to processing temporal signals, ensuring AMC accuracy while also improving its inference efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at +18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of MobileRaT to hyper-parameters and signal representations. All the experimental results indicate the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less demanding communication scenarios.

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A real-time transformer discharge pattern recognition method based on CNN-LSTM driven by few-shot learning

Cited by 18 publications

References 37 publications

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

An Ultra Lightweight Neural Network for Automatic Modulation Classification in Drone Communications

MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

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