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13th International Conference on graphics and Image Processing (ICGIP)

作者： Di, Zichao Zhou, Yue Chen, Kun Chen, Zongzhi Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China

This paper proposes a joint trajectory prediction algorithm based on graph neural networks and map scene information assistance. And proposed a kernel function to calculate the influence between different targets in t... 详细信息

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

关键词： graph convolutional neural network Kernel function Rasterization Mobilenet

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On the Design of Quantum graph convolutional neural network in the NISQ-Era and Beyond 40

On the Design of Quantum Graph Convolutional Neural Network ...

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IEEE 40th International Conference on Computer Design (ICCD)

作者： Hu, Zhirui Li, Jinyang Pan, Zhenyu Zhou, Shanglin Yang, Lei Ding, Caiwen Khan, Omer Geng, Tong Jiang, Weiwen George Mason Univ Fairfax VA 22030 USA Univ Rochester Rochester NY 14627 USA Univ Connecticut Storrs CT USA

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

The rapid growth in the size of graph convolutional neural networks (GCNs) encounters both computational- and memory-wall on classical computing platforms (e.g., CPU, GPU, FPGA, etc.). Quantum computing, on the other hand, provides extremely high parallelism for computation. Although quantum neural networks have been recently studied, the research on quantum graph neural networks is still in its infancy. The key challenge here is how to integrate both the graph topology information and the learning ability of GCNs into quantum circuits. In this work, we leverage the Givens rotations and its quantum implementation to encode graph information;in addition, we employ the widely used variational quantum circuit to bring the learnable parameters. On top of these, we present a full-quantum design of graph convolutional neural networks, namely "QuGCN", for semi-supervised learning on graph-structured data. Experiment results show our design is competitive with classical GCNs in terms of node classification accuracy on Cora sub-dataset. More importantly, we show the potential advantages that can be achieved by the proposed quantum GCN design when the number of features grows.

关键词： graph convolutional neural network Quantum circuit design Givens rotation NISQ

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A Robust graph convolutional neural network-Based Classifier for Automatic Modulation Recognition 18

A Robust Graph Convolutional Neural Network-Based Classifier...

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18th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Ghasemzadeh, Pejman Hempel, Michael Sharif, Hamid Univ Nebraska Lincoln Dept Elect & Comp Engn Adv Telecommun Engn Lab TEL Lincoln NE 68588 USA

ISBN: (纸本)9781665467490

The procedure of automatically recognizing the modulation scheme of the received signal without any knowledge of the communications parameters employed by the transmitter has gained tremendous attention for developing various applications such as dynamic spectrum access in next generation communications systems and in electronic warfare (EW) applications. Amongst the proposed approaches for this procedure, the feature-based approach working on the principles of deep learning models has consistently demonstrated more favorable properties for its integration with real-world applications and realtime operations. However, this approach faces the challenges of low performance efficiency caused by low classification accuracy and high computational complexity in environments characterized by low signal-to-noise (SNR) ratio conditions. Therefore, in this paper, we present our research findings on a deep learning-based classifier for Automatic Modulation Recognition (AMR) with robust classification accuracy and lower computational complexity, thus leading to higher overall performance efficiency. The feature extraction stage of the proposed classifier operates on the translation of received signal constellations into a graph, which then follows a process of extracting graph information. Mapping the extracted information into features is implemented by a convolutional block. This design employed in our feature extraction stage is the main element for the robustness of our proposed classifier. For our performance evaluations we selected 8QAM, 64QAM and 256QAM, representing candidates for low-, medium- and high-order modulation schemes, respectively. Our simulation results exhibit higher classification accuracy compared to related literature works, by an average of 11 percentage points (p.p.), 11.31 p.p. and 9.3 p.p., respectively, for the successful classification of 8QAM, 64QAM and 256QAM across an SNR range from -10 to 30 dB. The computational complexity analysis indicates t

关键词： Automatic modulation recognition (AMR) graph convolutional neural network robust classification low execution latency efficient performance

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Extracting multilane roads from OpenStreetMap through graph convolutional neural network and road mesh relationship analysis

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GEOCARTO INTERNATIONAL 2024年第1期39卷

作者： Wang, Andong Wu, Fang Gong, Xianyong Zhai, Renjian Qiu, Yue Qi, Yuyang Informat Engn Univ 62 Kexue St Zhengzhou Henan Peoples R China

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition.

关键词： Cartographic generalization road network multilane road extraction graph convolutional neural network

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Cellular network Fault Diagnosis Method Based on a graph convolutional neural network

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SENSORS 2023年第16期23卷 7042-7042页

作者： Amuah, Ebenezer Ackah Wu, Mingxiao Zhu, Xiaorong Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210003 Peoples R China

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on graph convolutional neural network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

关键词： 4G/5G network graph convolutional neural network heterogeneous network fault diagnosis

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graph contrast learning for recommendation based on relational graph convolutional neural network

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第8期36卷

作者： Liu, Xiaoyang Feng, Hanwen Zhang, Xiaoqin Zhou, Xia Bouyer, Asgarali Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China Chongqing Commun Design Inst Co Ltd Chongqing 400041 Peoples R China Chongqing Univ Technol Sch Management Chongqing 400054 Peoples R China Istinye Univ Fac Engn & Nat Sci Dept Software Engn TR-34010 Istanbul Turkiye

Current knowledge graph-based recommendation methods heavily rely on high-quality knowledge graphs, often falling short in effectively addressing issues such as the cold start problem and heterogeneous noise in user interactions. This leads to biases in user interest and popularity. To overcome these challenges, this paper introduces a novel recommendation approach termed Knowledge-enhanced Perceptive graph Attention with graph Contrastive Learning (KPA-GCL), which leverages relational graph convolutional neural networks. The proposed method optimizes the triplet embedding representation of entity-item interactions based on relationships between adjacent entities in a heterogeneous graph. Subsequently, a graph convolutional neural network is employed for enhanced aggregation. Similarity scores from a contrastive view serve as the selection criterion for high-quality embedded representations, facilitating the extraction of refined knowledge subgraphs. Multiple adaptive contrast-loss optimization functions are introduced by combining Bayesian Personalized Ranking (BPR) and hard negative sampling techniques. Comparative experiments are conducted with ten popular existing methods using real public datasets. Results indicate that the KPA-GCL method outperforms compared methods in all datasets based on Recall, NDCG, Precision, and Hit-ratio measures. Furthermore, in terms of mitigating cold start and noise, the KPA-GCL method surpasses other ten methods. This validates the reasonability and effectiveness of KPA-GCL in real-world datasets.

关键词： Recommendation system graph convolutional neural network graph contrast learning Knowledge graph

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MVGCN: Multi-View graph convolutional neural network for Surface Defect Identification Using Three-Dimensional Point Cloud

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2023年第3期145卷 031004-031004页

作者： Wang, Yinan Sun, Wenbo Jin, Jionghua (Judy) Kong, Zhenyu (James) Yue, Xiaowei Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY 12180 USA Univ Michigan Transportat Res Inst Ann Arbor MI 48109 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Virginia Tech Grad Dept Ind & Syst Engn Blacksburg VA 24060 USA

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects' patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.

关键词： 3D point cloud analytics graph convolutional neural network defect identification sensing monitoring and diagnostics

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Space Target Material Identification Based on graph convolutional neural network

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REMOTE SENSING 2023年第7期15卷 1937-1937页

作者： Li, Na Gong, Chengeng Zhao, Huijie Ma, Yun Beihang Univ Inst Artificial Intelligence Beijing 100091 Peoples R China Minist Educ Key Lab Precis Optomechatron Technol Beijing 100091 Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Changguang Satellite Technol Co Ltd Changchun 130000 Peoples R China

Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85-90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness.

关键词： space target material identification complex illumination conditions graph convolutional neural network

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Accelerating aerodynamic design optimization based on graph convolutional neural network

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第1期35卷 2450007-2450007页

作者： Li, Tiejun Yan, Junjun Chen, Xinhai Wang, Zhichao Zhang, Qingyang Zhou, Enqiang Gong, Chunye Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha Hunan Peoples R China

Computational fluid dynamics (CFD) plays a critical role in many scientific and engineering applications, with aerodynamic design optimization being a primary area of interest. Recently, there has been much interest in using artificial intelligence approaches to accelerate this process. One promising method is the graph convolutional neural network (GCN), a deep learning method based on artificial neural networks (ANNs). In this paper, we propose a novel GCN-based aerodynamic design optimization acceleration framework, GCN-based aerodynamic design optimization acceleration framework. The framework significantly improves processing efficiency by optimizing data flow and data representation. We also introduce a network model called GCN4CFD that uses the GCF framework to create a compact data representation of the flow field and an encoder-decoder structure to extract features. This approach enables the model to learn underlying physical laws in a space-time efficient manner. We then evaluate the proposed method on an airfoil aerodynamic design optimization task and show that GCN4CFD provides a significant speedup compared to traditional CFD solvers while maintaining accuracy. Our experimental results demonstrate the robustness of the proposed framework and network model, achieving a speedup average of 3.0 x.

关键词： Computational fluid dynamics aerodynamic design optimization deep learning graph convolutional neural network

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Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2023年第1期216卷

作者： Peng, Jiang-Zhou Aubry, Nadine Li, Yu-Bai Mei, Mei Chen, Zhi-Hua Wu, Wei-Tao Nanjing Univ Sci & Technol Key Lab Transient Phys Nanjing 210094 Peoples R China Tufts Univ Dept Mech Engn Medford MA 02155 USA Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116023 Peoples R China Nanjing Univ Sci & Technol Sino French Engineer Sch Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

This paper presents a novel deep learning-based surrogate model for steady-state natural convection problem with variable geometry. Traditional deep learning based surrogate models are more or less limited by the requirement for large amounts of training data, loss of accuracy due to pixelization of the original data, the low accurate prediction near the boundaries and low geometry adaptive capability. To overcome the above challenges, the proposed natural convection prediction framework is mainly composed of a physics-informed neural network (PINN) and a graph convolutional neural network (GCN), called natural convection prediction model based on physics-informed graph convolutional network (NCV-PIGN). The GCN serves as the prediction module, inferring and predicting natural convection phenomena by considering the interactions between unstructured nodes and their neighbor;the PINN incorporates the governing equations of natural convection into the loss function of the neural network, allowing the predictions from GCN to satisfy the constraints imposed by the physical laws. The advantages of this framework are twofold: the operation principles of the GCN better align with the development of the temperature field in real situations, and the embedding of physical information strengthens the model's understanding of the flow field, accurately describing the variations of temperature gradients at the boundary positions while reducing the model's reliance on training data. Finally, to validate the superiority of the NCV-PIGN, we analyze its geometric adaptability and accuracy of prediction using single and dual heat source cases. We compare the model's prediction results at different sampling point quantities and contrast them with those of purely data-driven models. The results demonstrate that the excellent geometric adaptability and prediction capability of the proposed model can be achieved with only 20 training data and once the fully trained the model can solve nat

关键词： Natural convection Surrogate model Physics-informed neural network graph convolutional neural network

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