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Super resolution distribution network measurement considering distribution network topology reconstruction

ENERGY REPORTS 2022年 8卷 313-320页

作者： Qin, Liwen Yu, Xiaoyong Gui, Haitao Wu, Lifang Ou, Shifeng Guangxi Power Grid Co Ltd Elect Power Res Inst Mingzhu Rd 6-2 Nanning 530023 Peoples R China Guangxi Power Grid Co Ltd Guilin Power Supply Bur Shanghai Rd 15 Guilin 541002 Peoples R China

As large-scale distributed generation and electric vehicles are promoted, it enhances the randomness and uncertainty of distribution network. It challenges state estimation for distribution networks, which commonly have limited measurements. To improve distribution network state perception, it is critical to utilize limited measurement and distribution network topology reconstruction to achieve the overall distribution network situation awareness. This paper proposes a distribution network measurement super-resolution model based on graph convolution neural network (GCN). The limited measurements from distribution network and multiple topologies were used as training sets. IEEE 33-nodes distribution network was used to verify the proposed method and the testing results are compared and analyzed. The testing results demonstrate the proposed GCN model can achieve distribution network situation awareness with limited measurements. It shows that the model has a good performance while the distribution network topology changes. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Distribution network Sensor graph convolution neural network Topology reconfiguration Super-resolution

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Structure-based graph convolutional networks with frequency filter

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PATTERN RECOGNITION LETTERS 2022年 164卷 161-165页

作者： Wei, FeiFei Ping, Mingzhu Mei, KuiZhi Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Cent South Univ Changsha Peoples R China

Message-passing neural networks (MPNNs) have attracted a lot interesting in academia and industry and have been applied to various graph analytical problems of real-world applications and achieved prominent successes. For the most of current works on MPNNs, they mainly focus on two categories: spectral-based and spatial-based methods. The former seeks to distill useful information (e.g. low-pass and high-pass signals) and the later designs structural schemes. However, it is not enough to only utilize one of them. We tackle this drawback by proposing an efficient and elegant method of taking advantage of the structural graph neural network (GNN) for advanced spectral-based information filtering. Through the learnable frequency components and global virtual neighbor nodes, the proposed scheme is elaborated to obtain informative and structured messages of neighbor nodes and latent space correlational nodes. Experimental results show that our method outperforms the state-of-the-art performance in a series of datasets of graph tasks. (c) 2022 Elsevier B.V. All rights reserved.

关键词： network representation learning Node embeddings graph filtering graph convolution neural network

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Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis

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NEUROCOMPUTING 2022年 501卷 629-639页

作者： Shou, Yuntao Meng, Tao Ai, Wei Yang, Sihan Li, Keqin Cent South Univ Forestry & Technol Sch Comp & Informat Engn Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Multimodal Emotion Recognition for Conversation (ERC) is a challenging multi-class classification task that requires recognizing multiple speakers' emotions in text, audio, video, and other modalities. ERC has received considerable attention from researchers due to its potential applications in opinion mining, advertising, and healthcare. However, the syntactic structure characteristics of the text itself have not been considered in this study. Taking into account this, this paper proposes a conversational affective analysis model (DSAGCN) combining dependent syntactic analysis and graph convolutional neural networks. Since words that reflect emotional polarity are usually concentrated exclusively in limited regions, the DSAGCN model first employs a self-attention mechanism to capture the most effective words in the dialogue context and obtain a more accurate vector representation of the emotional semantics. Then, based on speaker relationships and dependent syntactic relationships, the multimodal sentiment relationship graphs are constructed. Finally, a graph convolutional neural network is used to complete the recognition of multimodal emotion. In extensive experiments on two real datasets, IEMOCAP and MELD, the DSAGCN model outperforms the existing models in terms of average accuracy and f1 values for multimodal emotion recognition, especially for emotions such as "happiness" and "anger". Thus, dependent syntactic analysis and self-attention mechanism can enhance the model's ability to understand emotions.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Dependency parsing Dialogue emotion recognition graph convolution neural network Self attention mechanism

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Optimal sensor placement in distribution network based on super resolution network

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ENERGY REPORTS 2022年 8卷 1050-1058页

作者： Lv, Qifeng Chen, Ying Yuan, Keyu Qin, Liwen Yu, Xiaoyong Gui, Haitao Tsinghua Univ Shuangqin Rd 30 Beijing 100084 Peoples R China Guangxi Power Grid Co Ltd Elect Power Res Inst Mingzhu Rd 6-2 Nanning 530023 Peoples R China Guangxi Power Grid Co Ltd Guilin Power Supply Bur Shanghai Rd 15 Guaira 541002 Peoples R China

With the development of smart grids and emerging measurement technologies, the massive growth of distribution grid data may impact the reliable, economic, and safe operation of the distribution network. For a large-scale distribution network state estimation, the strategy of measuring data for a distribution grid is critical to its economy and reliability. This paper proposed a distribution network state estimation model based on a graph convolutional neural network. The proposed algorithm uses a genetic algorithm to optimize the sensor locations, frequency of sensors in the distribution network and configures of devices to guarantee the proposed state estimation data accuracy. With minimizing costs of investment and operation, the proposed graph convolutional neural network provides super-resolution state estimation data of the distribution network by using low-resolution measurement data. The proposed method is tested and verified by the IEEE33 distribution network system and the testing result demonstrates the feasibility and effectiveness of the proposed model and algorithm. (c) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Distribution network Sensor graph convolution neural network Genetic algorithm Temporal resolution Super-resolution

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End-to-End Hyperspectral Image Classification Using Hybrid GCNN with SWIN Transformer 1

End-to-End Hyperspectral Image Classification Using Hybrid G...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Ghotekar, Rahul Shaw, Kailash Rout, Minakhi Kiit University Department of Computer Science and Engineering Odisha India Department of Artificial Intelligence and Machine Learning Maharashtra Pune Lavale India

ISBN: (纸本)9798350316469

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces graph convolutional neural networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional Machine Learning and Deep Learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets. © 2023 IEEE.

关键词： Deep Learning graph convolution neural network Hybrid Model Hyperspectral Image Classification SWIN Transformer

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NACNet: A histology context-aware transformer graph convolution network for predicting treatment response to neoadjuvant chemotherapy in Triple Negative Breast Cancer

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COMPUTERIZED MEDICAL IMAGING AND graphICS 2024年 118卷 102467-102467页

作者： Li, Qiang Teodoro, George Jiang, Yi Kong, Jun Georgia State Univ Dept Math & Stat Atlanta GA 30303 USA Univ Fed Minas Gerais Dept Comp Sci BR-31270 Belo Horizonte MG Brazil Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA

Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet). Our deep learning method identifies the histopathological labels on individual image tiles from WSIs, constructs a spatial TME graph, and represents each node with features derived from tissue texture and social network analysis. It predicts NAC response using a transformer graph convolution network model enhanced with graph isomorphism network layers. We evaluate our method with WSIs of a cohort of TNBC patient (N=105) and compared its performance with multiple state-of-the-art machine learning and deep learning models, including both graph and non-graph approaches. Our NACNet achieves 90.0% accuracy, 96.0% sensitivity, 88.0% specificity, and an AUC of 0.82, through eight-fold cross-validation, outperforming baseline models. These comprehensive experimental results suggest that NACNet holds strong potential for stratifying TNBC patients by NAC response, thereby helping to prevent overtreatment, improve patient quality of life, reduce treatment cost, and enhance clinical outcomes, marking an important advancement toward personalized breast cancer treatment.

关键词： Whole slide image Pathological complete response graph convolution neural network Transformer Triple negative breast cancer Tumor microenvironment

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A PiRNA-disease association model incorporating sequence multi-source information with graph convolutional networks

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APPLIED SOFT COMPUTING 2024年 157卷

作者： Wang, Lei Li, Zheng-Wei Hu, Jing Wong, Leon Zhao, Bo-Wei You, Zhu-Hong Zaozhuang Univ Coll Informat Sci & Engn Zaozhuang 277100 Peoples R China Guangxi Acad Sci Big Data & Intelligent Comp Res Ctr Nanning 530007 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710129 Shaanxi Peoples R China

There is growing evidence that PIWI-interacting RNA (piRNA) is widely involved in the proliferation, invasion, and metastasis of malignant tumors, playing an important regulatory role in numerous human physiological and pathological processes. Disease-associated piRNAs are expected to be biomarkers and novel therapeutic targets for early diagnosis and prognosis of malignant tumors. However, most previous computational models did not fully focus on the rich representation ability of multiple sources of information in piRNA sequences, which affected their performance in predicting piRNA-disease associations (PDAs). In this work, we propose a model, iSG-PDA, which combines the multi-source information of piRNA sequences with graph convolutional neural networks to predict potential PDAs. More specifically, we first fuse multi-source information including piRNA sequences and disease semantics to enhance the expressiveness of data, then deeply mine the advanced hidden features of PDA using graph convolutional networks, and finally exploit random forest to accurately determine the associations between piRNAs and diseases. In the golden standard dataset, the proposed model realized a prediction accuracy of 91.96% at the AUC of 0.9184. In ablation experiments and comparisons with other different models, iSG-PDA exhibits strong competitiveness. Moreover, the results of the case study indicate that 17 of the top 20 PDAs in the proposed model predictive score were confirmed. These preliminary results reveal that iSG-PDA is an effective computational method for predicting PDAs and can provide reliable disease candidate piRNAs for biological experiments.

关键词： PIWI-interacting RNA PiRNA-disease association Multi -source information fusion graph convolution neural network

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Predicting melting temperature of inorganic crystals via crystal graph neural network enhanced by transfer learning

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COMPUTATIONAL MATERIALS SCIENCE 2024年 234卷

作者： Kim, Jaesun Jung, Jisu Kim, Sookyung Han, Seungwu Seoul Natl Univ Dept Mat Sci & Engn Seoul 08826 South Korea Palo Alto Res Ctr PARC Palo Alto CA 94304 USA Korea Inst Adv Study Seoul 02455 South Korea Seoul Natl Univ Dept Mat Sci & Engn Seoul 08826 South Korea

The melting temperature, a crucial material property, is particularly challenging to measure accurately for inorganic crystals. The data-driven approach emerges as a potential solution, although its effectiveness is hindered by the limitation of available data. To counter this challenge, we implement transfer learning, leveraging a vast computational database of atomization energy. We first pre-train a geometric-informationenhanced crystal graph neural network (GeoCGNN) using atomization energies of approximately 36,000 materials that are computed by the density functional theory. Subsequently, the pre-trained model is finetuned using melting temperatures measured for 799 crystals, encompassing 83 elements, ranging from unary to quaternary systems. This transfer learning strategy decreases the root mean square error from 407 to 218 K, attesting to a marked improvement in prediction accuracy. Furthermore, transfer learning significantly mitigates error variability across unary, binary, and ternary (or higher-order) systems, thereby enhancing the reliability of predictions across a broader range of crystals. We also show that transfer learning allows effective task adaptation by leveraging representation learned from pre-training. Therefore, it can achieve better prediction performance even with a limited number of data for predicting melting points.

关键词： graph convolution neural network Melting temperature Transfer learning Material informatics

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graphformer: Adaptive graph correlation transformer for multivariate long sequence time series forecasting

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KNOWLEDGE-BASED SYSTEMS 2024年 285卷

作者： Wang, Yijie Long, Hao Zheng, Linjiang Shang, Jiaxing Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Accurate long sequence time series forecasting (LSTF) remains a key challenge due to its complex timedependent nature. Multivariate time series forecasting methods inherently assume that variables are interrelated and that the future state of each variable depends not only on its history but also on other variables. However, most existing methods, such as Transformer, cannot effectively exploit the potential spatial correlation between variables. To cope with the above problems, we propose a Transformer -based LSTF model, called graphformer, which can efficiently learn complex temporal patterns and dependencies between multiple variables. First, in the encoder's self -attentive downsampling layer, graphformer replaces the standard convolutional layer with an dilated convolutional layer to efficiently capture long-term dependencies between time series at different granularity levels. Meanwhile, graphformer replaces the self -attention mechanism with a graph self -attention mechanism that can automatically infer the implicit sparse graph structure from the data, showing better generality for time series without explicit graph structure and learning implicit spatial dependencies between sequences. In addition, graphformer uses a temporal inertia module to enhance the sensitivity of future time steps to recent inputs, and a multi -scale feature fusion operation to extract temporal correlations at different granularity levels by slicing and fusing feature maps to improve model accuracy and efficiency. Our proposed graphformer can improve the long sequence time series forecasting accuracy significantly when compared with that of SOTA Transformer -based models.

关键词： Long sequence time series forecasting Transformer Dilated causal convolution Self-attention graph convolution neural network

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network Link Connectivity Prediction Based on GCN and Differentiable Pooling Model 14

Network Link Connectivity Prediction Based on GCN and Differ...

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14th IEEE International Conference on Advanced Infocomm Technology (ICAIT)

作者： Kuang, Naixue Zuo, Yanzhi Huo, Yonghua Jiao, Libin Gong, Xingle Yang, Yang Beijing Inst Tracking & Telecommun Technol Beijing Peoples R China CETC Res Inst 54 Sci & Technol Commun Networks Lab Shijiazhuang Hebei Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (数字)9781665471565

ISBN: (纸本)9781665471565

graph neural networks (GNN) are widely used in node classification and link prediction, and have achieved very good results. However, the current GNN does not have a deep hierarchical representation of learning graphs, and is mostly a simple weighted superposition. This paper proposes a link prediction algorithm based on graph convolutional neural network (GCN) and an improved differentiable pooling model, which combines GCN and differentiable pooling to extract deep-level graph features. At the same time, the Principal Component Analysis (PCA) dimensionality reduction method is used to improve the assignment matrix generation method is proposed to improve link prediction accuracy;an improved dual distance node labeling is proposed to mark nodes to construct a node information matrix;a common neighbor method with pruning is proposed to extract subgraphs while reducing link data the complexity. The simulation results show that the link prediction algorithm based on the graph convolutional neural network and the improved differentiable pooling model performs better in link prediction.

关键词： link prediction graph convolution neural network differentiable pooling node labeling

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