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Combining multi-level feature extraction algorithm with residual graph convolutional neural network for partial discharge detection

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MEASUREMENT 2025年 242卷

作者： Zhang, Chungfeng Fulnecek, Jan Yang, Lin Zhang, Yongjun Zheng, Jiehui South China Univ Technol Sch Elect Power Guangzhou 510665 Peoples R China VSB Tech Univ Ostrava Dept Elect Power Engn Ostrava 70800 Czech Republic

Partial discharge (PD) identification is critical for the insulation diagnosis of cable conductors;however, there is still scope for enhancement in the existing adaptive extraction capabilities and feature utilization for PD signals. In this context, this paper introduces a method that integrates a one-dimensional convolutional neural network (1D-CNN) with a residual graph convolutional neural network (ResGCN) to recognize PD signals. Noise analysis is performed using various combinations of mother wavelets, and Bayesian optimization is employed to mitigate background noise. Key features of PD signals are progressively abstracted through a 1D-CNN-based multilevel automatic feature learning method, while signal timing attributes are maintained to minimize manual intervention. The graph data is constructed using the signal feature matrix and the signal timing feature similarity matrix. This is followed by the development of a ResGCN utilizing a graph attention mechanism to integrate node feature information and the topology of the PD graph data. This approach aims to fully exploit the correlation between local regions of the feature space and the temporal numerical properties of the signals. Additionally, it jointly optimizes feature extraction and model classification to facilitate adaptive diagnosis. The method is validated with extensive real experimental data obtained from medium voltage overhead power lines. It demonstrates exceptional performance and practicality, achieving an accuracy rate of 97.3% and a recognition rate of 96.1% for PD samples, thus offering reliable theoretical support for effective PD detection.

关键词： Partial discharge convolutional neural network graph data graph attention mechanism graph convolutional neural network

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Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2025年第1期41卷 174-191页

作者： Yuan, Yongliang Yang, Qingkang Wang, Guohu Ren, Jianji Wang, Zhenxi Qiu, Feng Li, Kunpeng Liu, Haiqing Henan Polytech Univ Sch Mech & Power Engn Jiaozuo Peoples R China Zhengzhou Univ Ind Technol Sch Mech & Elect Engn Zhengzhou Peoples R China Henan Polytech Univ Sch Software Jiaozuo Peoples R China Jilin Univ Sch Commun Engn Changchun 130012 Peoples R China China South Ind Grp Corp Automat Res Inst Co Ltd Beijing Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore Singapore Fluorine Based New Mat Technol Co Ltd Jiaozuo Henan Peoples R China

Accurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep-learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi-strategy tuna swarm optimization-assisted graph convolutional neural network (IMTSO-GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high-dimensional. Inspired by "No Free Lunch (NFL)", IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO-GCN, and the experimental results show that the R2 of the IMTSO-GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

关键词： aero-engine graph convolutional neural network mutual information remaining useful life Tuna swarm optimization

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A graph convolutional neural network model based on fused multi-subgraph as input and fused feature information as output

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Lu, Junwen Chen, Zeji Deng, Xiaolong Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Beijing Univ Posts & Telecommun Sch Cyber Secur Beijing Peoples R China

The graph convolution neural network (GCN)-based node classification model tackles the challenge of classifying nodes in graph data through learned feature representations. However, most existing graph neural networks primarily focus on the same type of edges, which might not accurately reflect the intricate real-world graph structure. This paper introduces a novel graph neural network model, MF-GCN, which integrates subgraphs with various edge types as input and combines feature information from each graph convolutional neural network layer to produce the final output. This model learns node feature representations by separately feeding subgraphs with different edge types into the graph convolutional layer. It then computes the weight vectors for fusing various edge type subgraphs based on the learned node features. Additionally, to efficiently extract feature information, the outputs of each graph convolution layer, without an activation function, are weighted and summed to obtain the final node features. This approach resolves the challenges of determining fusion weights and effectively extracting feature information during subgraph fusion. Experimental results show that the proposed model significantly improves performance on all three datasets, highlighting its effectiveness in node representation learning tasks.

关键词： graph convolutional neural network Node classification Multi-type edges Subgraph fusion Feature extraction

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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Yin, Guimei Yuan, Jie Chen, Yanjun Guo, Guangxing Shi, Dongli Wang, Lin Zhao, Zilong Zhao, Yanli Zhang, Manjie Dong, Yuan Wang, Bin Tan, Shuping Taiyuan Normal Univ Sch Comp Sci & Technol Jinzhong 030619 Peoples R China Shanxi Prov Peoples Hosp Dept Radiol Taiyuan 030012 Peoples R China Taiyuan Normal Univ Inst Big Data Technol Anal & Applicat Jinzhong 030619 Peoples R China Sun Yat sen Univ Sch Chem Engn & Technol Zhuhai 519000 Peoples R China Beijing Huilongguan Hosp Psychiat Res Ctr Beijing 100096 Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Jinzhong 030600 Peoples R China

Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the interactions between nodes. To address this issue, a schizophrenia classification model based on a three-dimensional adaptive graph convolutional neural network (3D-AGCN) is proposed. Each subject's EEG data is divided into various segment lengths and frequency bands for the experiment. The attention mechanism is then used to integrate the node features in the spatial, feature, and frequency band dimensions. The resulting adaptive brain functional network features are then constructed and fed into the GAT + GCN model. This adaptive approach eliminates the human-specified criteria for feature selection and brain network construction. The trial results demonstrated that, when using a 6-second segment length and time-domain and frequency-domain features, patients with first-episode schizophrenia achieved the highest classification accuracy of 87.64% This method outperforms other feature selection and brain network modeling approaches, providing new insights and directions for the early diagnosis and recognition of schizophrenia.

关键词： Schizophrenia 3D spaces Attention mechanisms Adaptive brain networks graph convolutional neural network

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Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 150卷

作者： Feng, Feng Li, Yu-Bai Chen, Zhi-Hua Wu, Wei-Tao Peng, Jiang-Zhou Mei, Mei Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116023 Peoples R China Nanjing Univ Sci & Technol Key Lab Transient Phys Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sino French Engineer Sch Nanjing 210094 Peoples R China

The present study introduces a novel optimization framework that combines a graph convolutional neural network surrogate model with Genetic Algorithms (GCN-GA). This framework can optimize the heat sources layout in horizontal annuli, achieving the highest heat transfer efficiency. The predictive GCN surrogate model utilizes graph data as training input and enables it with sensitivity to geometric variations at the node level. This sensitivity allows achieving higher training and predictive accuracy compared to fully connected or convolu-tional neural networks. The optimization is achieved by GA with global search capabilities seeking the optimal spatial heat sources layout. The predicted average Nusselt numbers of the horizontal annuli by GCN are utilized as evaluation metrics. Simultaneously, geometric parameter features are targeted as optimization objectives, achieving optimization results consistent with numerical results across various cases. Furthermore, the GCN predicts temperature fields with average errors below 1.33% compared to numerical results. Specifically, in the cases validation involving single, double, and triple heat sources, the GCN-GA demonstrates faster optimization speeds, increased by 10, 29, and 110 times compared to numerical methods, respectively. Therefore, significant enhancements in optimization efficiency facilitated by the optimization framework built upon the GCN surrogate model.

关键词： Natural convection Optimization graph convolutional neural network Genetic algorithm

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SMGCN: Multiple Similarity and Multiple Kernel Fusion Based graph convolutional neural network for Drug-Target Interactions Prediction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第1期21卷 143-154页

作者： Wang, Wei Yu, Mengxue Sun, Bin Li, Juntao Liu, Dong Zhang, Hongjun Wang, Xianfang Zhou, Yun Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Peoples R China Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Henan Normal Univ Sch Math & Informat Sci Xinxiang 453007 Peoples R China Henan Polytech Univ Hebi Inst Engn & Technol Hebi 458030 Peoples R China Henan Inst Technol Coll Comp Sci & Technol Xinxiang 453000 Henan Peoples R China

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on graph convolutional network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

关键词： Drugs Kernel Proteins Predictive models Feature extraction convolutional neural networks Compounds Drug discovery drug-target interactions graph convolutional neural network multiple similarity

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The Digital Reform of Japanese Classroom Teaching Modes Under the graph convolutional neural network

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IEEE ACCESS 2024年 12卷 95118-95127页

作者： Jiang, Gongyi Gao, Weijun Tong, Mingjia Tourism Coll Zhejiang Foreign Languages Dept Hangzhou 310043 Peoples R China Univ Kitakyushu Fac Environm Engn Kitakyushu 8080135 Japan

This work aims to explore reform methods for Japanese classroom teaching modes in an informationized environment. Addressing issues such as monotonous classroom content and low student engagement in traditional teaching, this work introduces deep learning technology and constructs a personalized recommendation model based on the Bidirectional Encoder Representations from Transformer (BERT) fused with the graph convolutional neural network (GCNN). Furthermore, this model is applied to Japanese flipped classroom teaching, and a personalized recommendation system-supported Japanese flipped classroom mode is designed. The performance of the constructed model is finally verified. The results show that the personalized recommendation model constructed here has achieved good application effects in Japanese teaching, with a recommendation recognition accuracy of 95.96% and an F1 value of 90.12%. The constructed model exceeds the precision of the baseline algorithm convolutional neural network (CNN) by over 4%. Through questionnaire surveys and empirical analysis of controlled experimental designs, it is found that the Japanese flipped classroom mode supported by the personalized recommendation system demonstrates significant advantages in enhancing student learning abilities and satisfaction. The improvement rates of experimental group students in various indicators all exceed 18%. Therefore, the Japanese teaching mode proposed can significantly improve the teaching effectiveness of Japanese classrooms and provide experimental references for educational mode reforms.

关键词： Education Vectors convolutional neural networks Electronic learning Online services Encoding Recommender systems Deep learning Learning systems graph convolutional networks Teaching modes deep learning personalized recommendation Japanese classroom flipped classroom graph convolutional neural network

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graph convolutional neural network based on the Combination of Multiple Heterogeneous graphs 22

Graph Convolutional Neural Network based on the Combination ...

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22nd IEEE International Conference on Data Mining (ICDM)

作者： Mu, Caihong Huang, Heyuan Liu, Yi Luo, Jiashen Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9798350346091

Collaborative filtering algorithms based on graph neural networks have achieved good performance in many scenarios of recommender systems (RSs), most of which utilize the raw user-item interaction information for recommendation. However, user-item interaction information is usually sparse, limiting the recommendation performance of the algorithms. Therefore, how to fully mine the sparse user-item interaction information and improve the accuracy of the recommendation algorithms is an important issue worth studying. To address this issue, this paper proposes a graph convolutional neural network algorithm based on the combination of multiple heterogeneous graphs (GCN-MHG), which constructs a heterogeneous fully adjacent graph by connecting user-user and item-item nodes to each other (i.e., homogeneous nodes are connected to each other), and design a heterogeneous full adjacency auxiliary (HFAA) layer to aggregate information on this graph. The HFAA layer not only expands the influence of distant nodes on the target node, making the target node can receive global information, but also alleviates the information bottleneck phenomenon. In addition, we find that retaining the node features in the process of aggregating information at some layers (i.e., adopting self-connection of nodes at partial layers) will not only avoid the damage to the algorithms' performance caused by the features without semantic information such as ID of nodes in the scenarios of RSs, but also strengthen the influence of non-one-hop neighbor nodes. Experiments on three real datasets demonstrate that the proposed GCN-MHG performs better than the state-of-the-art algorithms. At the same time, several ablation experiments are also designed, and the rationality of GCN-MHG is analyzed theoretically and experimentally. Codes are available at https://***/huangheyuanbj/GCN-MHG.

关键词： recommendation graph convolutional neural network multiple heterogeneous graphs

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EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2024年 18卷 1416494页

作者： Chen, Wei Liao, Yuan Dai, Rui Dong, Yuanlin Huang, Liya Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing Peoples R China Nanjing Univ Posts & Telecommun Coll Flexible Elect Future Technol Nanjing Peoples R China

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism graph convolutional neural network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

关键词： attention mechanism EEG electrode channels emotion recognition frequency bands graph convolutional neural network transformer

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Data Privacy Protection Model Based on graph convolutional neural network

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MOBILE networkS & APPLICATIONS 2024年第5期29卷 1433-1440页

作者： Gu, Tao Yang, Lin Wang, Hua Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu Peoples R China Southwestern Univ Finance & Econ Inst Chinese Financial Studies Chengdu Peoples R China Guangdong Univ Finance & Econ Sch Finance Guangzhou Peoples R China

In order to solve the problem of leakage of user data privacy in social network scenarios, the author proposes a data privacy protection model based on graph convolutional neural network. This method analyzes in detail the privacy threat to user data caused by the graph convolutional neural network prediction model in the social network scenario, according to the homogeneity principle of social networks, using the attribute features and social relationships disclosed by social users, a graph convolutional neural network classification model is constructed through a semi-supervised learning method, the hidden private attribute categories of target users are inferred, and the accuracy and robustness of the method are finally evaluated on real social network datasets. Experimental results show that: The prediction accuracy rate continues to increase steadily, all in the range of 60%, which is very close to the real prediction accuracy rate, and the data utility is high. Conclusion: The data privacy protection model based on the graph convolutional neural network can better realize the privacy protection, and at the same time ensure the data has high data utility.

关键词： graph convolutional neural network Differential privacy Privacy inference Privacy protection Social network

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