Modulation Recognition Algorithm Based on ResNet50 Multi-feature Fusion

Liu, Xianghui; Wu, Zhaocong; Tang, Chunyang

doi:10.1109/icitbs53129.2021.00171

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Mangesh et al [25] the discovery of new multidomain truncated hemoglobins and their distinct structural arrangement, which expands on the development and uses of the trHb family. Liu and Zhong [26] utilized multi-channel access to heterogeneous data sources, combined with multi-domain fusion methods to construct a medical knowledge graph, and realized an intelligent questioning model based on a medical knowledge graph covering the whole range of diseases. Within the domain of fault diagnosis, multidomain fusion also shows great potential for application.…”

Section: Multi-domain Fusionmentioning

confidence: 99%

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Xiao,

Yan,

Zhang

et al. 2024

Auton. Intell. Syst.

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The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

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Section: Multi-domain Fusionmentioning

confidence: 99%

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Xiao,

Yan,

Zhang

et al. 2024

Auton. Intell. Syst.

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“…This type of network has greater ease of optimization and can gain accuracy even with considerably greater depth. Several methods have been developed based on Resnet, such as [47][48][49][50], which were observed to eliminate noise inference in the images, and assist in identifying even complex features with better accuracy.…”

Section: Data Extractionmentioning

confidence: 99%

Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis

Silva

Nascimento²,

Jagatheesaperumal³

et al. 2022

Sensors

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Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected persons, divided into benign, malignant and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the bilateral algorithm together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection.

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A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification

Liu

et al. 2023

Electronics

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Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment.

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Modulation Recognition Algorithm Based on ResNet50 Multi-feature Fusion

Cited by 3 publications

References 11 publications

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis

A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification

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