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Thermo-mesh transformer network for generalizable three-dimensional temperature prediction with mechanism-based representation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： Chen, Jincheng Zhu, Feiding Han, Yuge Ren, Dengfeng Nanjing Univ Sci & Technol Sch Energy & Power Engn 200 Xiao Lingwei St Nan Jing City 210094 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Energy & Power Engn MIIT Key Lab Thermal Control Elect Equipment 200 Xiao Lingwei St Nan Jing City 210094 Jiangsu Provinc Peoples R China China Elect Technol Grp Corp Res Inst 28 1 Mu Xu Yuan East St Nanjing 210007 Jiangsu Peoples R China

The application of artificial intelligence for predicting physical fields holds significant potential in military and manufacturing domains. However, physical phenomenon prediction networks face challenges in accurately modeling physical laws across diverse scenarios while ensuring computational efficiency and generalization. This paper presents a novel Thermo-Mesh Transformer network (TMTN) for universal three-dimensional (3D) temperature reconstruction from a single two-dimensional (2D) image. To enhance the network's understanding of heat transfer phenomena, a multi-scale feature extraction module is proposed to effectively capture and integrate both local and global features from the mesh and the image of the complex thermodynamic scenario. A mesh- enhanced transformer block (METB) is designed to implicitly represent the energy conservation equation, combining a graph convolutional network (GCN) for modeling the diffusion term and a Transformer for modeling the source term. This innovative mechanism-based representation architecture directly integrates physical laws, enabling TMTN to predict heat transfer phenomena across various shapes and conditions, including untrained models and out-of-range conditions. Additionally, a parameter-efficient design based on an improved Laplacian matrix is proposed, leveraging the PointNet framework and farthest point sampling to efficiently represent global vertex relationships via Transformer with reduced parameters. Experimental results demonstrate that TMTN achieves a 38% reduction in error compared to state-of-art methods and exhibits robust performance under out-of-range and untrained conditions. TMTN offers valuable engineering applications in military infrared reconnaissance and the thermal management of civilian systems like energy storage and heating devices.

关键词： Radiative heat transfer Soft rasterizer graph convolutional neural network Physical phenomenon prediction network Temperature prediction via artificial intelligence

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PEPTIDE SEQUENCE TAG EXTRACTION BY graph CONVOLUTION neuralnetworkS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2025年第1期26卷 151-159页

作者： Bian, Xinye Xie, Dongmei Zhang, Di Xie, Xiaoyu Feng, Yuyue Zhou, Piyu He, Changjiu Lyu, Mingming Wang, Haipeng Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

The peptide sequence tag extraction method plays a vital role in tandem mass spectrometry-based protein identification engines. This approach faces two significant challenges in practical applications: first, the issue of fixed tag lengths, where shorter tags lack sufficient specificity, leading to an excessive recall of non-target peptide sequences, and longer tags experience a reduction in precision as tag length increases, potentially failing to recall target peptide sequences;second, the sensitivity and precision of tag extraction remain relatively low. To address these issues, a variable-length peptide sequence tag extraction algorithm, TagEx, based on graph convolutional networks, is proposed. This method begins by training a de novo peptide sequencing scoring model utilizing graph convolutional networks. It then constructs a spectral peak connection graph from the mass spectrum, employing a depth-first search strategy to extract variable-length peptide sequence tags, with the trained graph convolutional network model scoring amino acid connections during the extraction ***, tags are filtered based on length and scoring to obtain the final candidate peptide sequence tag set. To evaluate TagEx's performance, it was benchmarked against three representative tag extraction software tools: InsPect, PepNovo+, and DirecTag. The experimental results demonstrate that TagEx exhibits superior sensitivity, coverage, and precision, with improvements of 0.62-2.32, 3.22-11.14, and 3.29-8.31 percentage points, respectively, when retaining the top 100 tags.

关键词： proteomics peptide sequence tag graph convolutional neural network de novo sequencing tandem mass spec-trometry

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A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-24页

作者： Jiang, Ming Liu, Zhiwei Xu, Yan Fujian Univ Technol Sch Internet Econ & Business Fuzhou 350014 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350108 Peoples R China

Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natural, traffic data often contains missing values. Addressing the impact of missing data on traffic flow prediction has become a widely discussed topic in the academic community and holds significant practical importance. Existing spatiotemporal graph models typically rely on complete data, and the presence of missing values can significantly degrade prediction performance and disrupt the construction of dynamic graph structures. To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios-graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. GCRNODE uses ODE to model the evolution of traffic flow and updates the hidden states of the ODE through observed data. Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. This indicates that the proposed method has stronger adaptability and robustness in handling missing data and modeling spatiotemporal dependencies.

关键词： Intelligent transportation Traffic prediction Missing data graph convolutional neural network neural ordinary differential equation

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A Production-Logistics prediction method integrating Spatial-Temporal features in flexible production workshop for buffer allocation problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Zhang, Qi Wang, Anmin Li, Jie Zheng, Longhui Bao, Jinsong Zhang, Dan Donghua Univ Coll Mech Engn Shanghai 201620 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong Peoples R China

To meet the demands of personalized manufacturing, characterized by customized production with varying batch sizes, logistics equipment such as Automated Guided Vehicles (AGVs) play a critical role in the manufacturing process. However, the distribution of multiple batches is influenced by various factors, with buffer zone capacity allocation emerging as a key challenge. Optimizing buffer zone allocation necessitates a thorough consideration of both spatial characteristics (e.g., shop floor layout and workpiece pathways) and temporal characteristics (e.g., the sequence of material distribution) to enhance resource allocation, reduce bottlenecks, and improve efficiency. This research proposes a novel logistics prediction method for flexible production plants, utilizing a graph attention network that integrates spatial-temporal features. The method first applies a multi-head attention mechanism to capture significant temporal information. Then, a graph convolutional network is employed to model the workshop layout topology and workpiece processing paths, thereby extracting the spatial features of logistics. This spatial information is sequentially processed through a gated recurrent unit and the multi-head attention mechanism to capture the dynamic temporal features of logistics. The proposed model is ultimately employed to predict production logistics in a flexible manufacturing workshop. The experimental results of the MA-T-GCN (Multi-head Attention Temporal graph Convolution network) model on production logistics prediction demonstrate an improvement over the best-performing baseline methods on standard benchmark metrics under varying experimental conditions.

关键词： Flexible production workshop Buffer resource allocation Production logistics forecasting Spatial-temporal analysis graph convolutional neural network

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Deep Learning-Based Community Detection Attack: Arctic Shipping Data as a Case

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SECURITY AND PRIVACY 2025年第1期8卷

作者： Li, Changrong Duan, Wei Li, Zhenfu Dalian Maritime Univ Coll Transportat Engn Dalian Peoples R China

This study explores advanced methods for community discovery attacks in Vehicular Ad hoc networks (VANETs) using deep learning techniques. It begins with a comprehensive analysis of VANET characteristics, classifications, routing protocols, and topological structures, followed by an exploration of deep learning applications in network community detection attacks, focusing on state-of-the-art algorithms and evaluation metrics. The core contribution is a novel community discovery attack method for VANETs that integrates graph convolutional neural networks with multi-head and bidirectional attention mechanisms. The proposed approach is rigorously evaluated through extensive experimentation and in-depth analysis of results. The study concludes with a critical summary of findings and outlines promising future research directions, aiming to provide innovative strategies for enhancing community discovery attacks in vehicular networks. This research has potential implications for improving communication efficiency, resource allocation, and performance in VANET environments.

关键词： Arctic shipping data community discovery attack graph convolutional neural network multi-head attention mechanism vehicle-mounted network

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Quantile Regression and GCN Ensembled Hybrid Interval Forecasting Model for Wind Power Generation 5th

Quantile Regression and GCN Ensembled Hybrid Interval Foreca...

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5th International Conference on neural Computing for Advanced Applications (NCAA)

作者： Shen, Xuehao Li, Haisheng Li, Chengdong Peng, Wei Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Ronghua Construct Grp Co Ltd Qingdao 266000 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Wind power forecasting plays a crucial role in enhancing the stability and reliability of power system operations. However, accurately wind power prediction is challenging due to its randomness and discontinuity. Most current methods tend to offer a singular predicted value, which inevitably imbued with errors, the errors precipitate a cascade of stability issues within the power grid. In order to address this issue, this study proposed a novel model performed both point and interval forecasting of wind power, leveraging the synergistic capabilities of graph convolutional neural networks (GCN), long short-term memory neural networks (LSTM) and quantile regression. Firstly, the adjacency matrix was constructed by leveraging the pearson correlation between historical wind power data and meteorological variables. Secondly, the matrix and original data served as the input of GCN. Thirdly, the output of the GCN was fused with the original data, and then, the hybrid data was taken as the input of LSTM. Finally, pinball loss was used as the training objective to obtain multiple quantile values of wind power. The experimental findings indicated that the proposed model not only achieved the lowest error rates in point forecasting but also provided the most precise and dependable prediction intervals across various confidence levels.

关键词： Wind power Interval forecasting Quantile regression graph convolutional neural network

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graph convolutional neural networks for Micro-Expression Recognition-Fusion of Facial Action Units for Optical Flow Extraction

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IEEE ACCESS 2024年 12卷 76319-76328页

作者： Yang, Xuliang Fang, Yong Rodolfo Jr, C. Raga Dongguan Univ Technol Inst Synergist Dev Urban Acad Dynam Dongguan 523419 Peoples R China Dongguan City Univ Sch Artificial Intelligence Dongguan 523419 Peoples R China Natl Univ CCIT Manila 999005 Philippines

Micro-expression recognition is an important problem in the field of computer vision and affective computing. To improve the accuracy of micro-expression recognition, the study proposes a novel graph convolutional neural network model oriented to micro-expression recognition, which divides facial features into regions and uses the optical flow method for feature extraction of facial action units. The model utilizes graph structure to encode facial features intuitively, while obtaining dynamic changes of facial features through the change information of optical flow, thus obtaining richer micro-expression feature information. The results show that the model performance can be maximized when the model is set to 5-way 5-shot and is taken as 1.4, at which time the model's accuracy on the dataset CAMSE II is 79.168%. The proposed algorithm performs well when compared with other algorithms in terms of accuracy and F1 score, the proposed algorithm realizes an accuracy of 0.795 on the CAMSE II dataset compared to the other algorithms which is up to 0.785. The accuracy of the proposed algorithm on the SAMM dataset is 0.738, which is only lower than that of the spatio-temporal recurrent convolutional neural network. The algorithm proposed in the study shows good performance in micro-expression recognition and promotes the development of the field of computer vision and affective computing.

关键词： Facial action feature extraction graph convolutional neural network micro-expression recognition

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graph convolutional neural networks Sensitivity Under Probabilistic Error Model

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER networkS 2024年 10卷 788-803页

作者： Wang, Xinjue Ollila, Esa Vorobyov, Sergiy A. Aalto Univ Dept Informat & Commun Engn Espoo 02150 Finland

graph neural networks (GNNs), particularly graph convolutional neural networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data. This paper proposes an analysis framework to investigate the sensitivity of GCNNs to probabilistic graph perturbations, directly impacting the graph shift operator (GSO). Our study establishes tight expected GSO error bounds, which are explicitly linked to the error model parameters, and reveals a linear relationship between GSO perturbations and the resulting output differences at each layer of GCNNs. This linearity demonstrates that a single-layer GCNN maintains stability under graph edge perturbations, provided that the GSO errors remain bounded, regardless of the perturbation scale. For multilayer GCNNs, the dependency of system's output difference on GSO perturbations is shown to be a recursion of linearity. Finally, we exemplify the framework with the graph Isomorphism network (GIN) and Simple graph Convolution network (SGCN). Experiments validate our theoretical derivations and the effectiveness of our approach.

关键词： Perturbation methods Filters Sensitivity analysis Probabilistic logic Convolution Laplace equations Vectors Information processing Analytical models Accuracy graph convolutional neural network graph shift operator sensitivity analysis structural perturbation

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Characteristics and prediction of urban interaction networks from the perspective of traffic flow and text information flow

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ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE 2025年第2期52卷 430-456页

作者： Liu, Lin Sun, Yi Li, Wanwu Shandong Univ Sci & Technol Qingdao Peoples R China Ocean Univ China Qingdao Peoples R China Ocean Univ China Coll Marine Geosci 238 Songling Rd Qingdao 266100 Peoples R China

The degree of urban development depends on the degree of closeness between cities, which is reflected in the strength of interactions between cities, such as traffic and information flows. In this research, we compare and analyze the characteristics of urban interaction networks (UINs) at two spatial scales in Shandong Province and the whole country from two different perspectives of traffic flow and information flow and validate the spatial interaction characteristics reflected in the material space traffic flow from the perspective of textual spatial information flow. The UIN is constructed based on Tencent migration big data, and the assortative coefficient method is introduced to explore the assortative and interaction characteristics between core cities and edge cities in the traffic flow network. Introducing deep learning methods on a larger scale, the GCN_CD model is proposed for semi-supervised classification of nodes to realize community discovery for both traffic flow and information flow networks. The spatial interaction intensity prediction model GAT _ d beta is constructed taking into account geographic features, which improves the prediction accuracy. The results show that from the perspectives of traffic flow and text information flow, the urban interactive network in Shandong Province has the characteristics of Scale-Free and Small-World, showing certain homogeneity and strong spatial interaction. Shandong Province's UIN formed Jinan, Qingdao, and other cities as the core, satellite cities around, the east and west ends of the remote echo structure. From the perspective of traffic flow, the national urban interactive network presents a jumping distribution, with a single core city as the dominant distribution structure. From the perspective of information flow, the dividing line between the eastern and western communities is obvious, and the internal aggregation of the community is strong. Distance attenuation effects have an impact on the strength o

关键词： Spatial interaction traffic flow network toponym co-occurrence network graph convolutional neural network interaction intensity prediction

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graph convolutional neural networks with AM-Actor-Critic for Minimum Vertex Cover Problem 33

Graph Convolutional Neural Networks with AM-Actor-Critic for...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Tian, Hong Li, Dazi Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781665440899

At present, deep reinforcement learning (DRL) methods are mostly used to solve the problems in Euclidean space. Typical evaluation environment of algorithm performance includes Atari pixel game with image as input, or single inverted pendulum system with specific data input shape, etc. However, most of the practical problems exist in non-Euclidean space, and the features of topological data need to be extracted. In this paper, a new framework combining DRL and graph neural network (GNN) is proposed, which aims to reduce the over-estimation problem, and obtain better performance results than before in solving NP-hard problems. NP-hard problem is a problem with high time complexity. The proposed method is used to solve a typical NP-hard problem named the Minimum Vertex Cover problem (MVC) and is compared with the common Actor-Critic-GNN_(AC-GNN) method, a heuristic strategy and a random strategy, the proposed algorithm has the best performance. At the same time, the experiment also show the superiority of our new method in generalization ability, which is a feature that the previous DRL methods do not have.

关键词： Deep Reinforcement Learning graph convolutional neural network NP-hard Problem

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