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Spatiotemporal Graph Contrastive Learning for Wind Power Forecasting

IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Guiyan Zhang, Yajuan Zhang, Ping Gu, Junhua Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation School of Artificial Intelligence Tianjin300401 China Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Artificial Intelligence Tianjin300401 China

Accurate and robust wind power forecasting plays a crucial role in ensuring the safety and stability of the power system. Hybrid spatiotemporal forecasting models based on graph convolutional networks have received widespread attention due to their advantages in spatial feature extraction. However, these methods are susceptible to the quality of the generated graph due to data noise and missing issues, resulting in suboptimal performance. In this paper, we propose a hybrid deep learning model based on spatiotemporal graph contrastive learning to address the above issues. Specifically, the model's encoder combines an adaptive graph convolutional network with LSTM to capture fine-grained spatiotemporal dependencies. To enhance the robustness of the encoder against data noise, we apply feature-level and topology-level data augmentation techniques to the model's input and design two contrastive learning auxiliary tasks from the temporal and spatial dimensions, respectively. Furthermore, to capture more comprehensive spatial correlations, we construct an adaptive graph by fusing the static graph with a learnable parameter matrix. Extensive experimental results on two realworld datasets demonstrate that our proposed model significantly outperforms other state-of-the-art methods. Code is available at https://***/lgyjayou/SGCLWPF. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Feature Selection Based on Streaming Labels Considering the Division of the Selected Feature Set

Feature Selection Based on Streaming Labels Considering the ...

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data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Ping Zhang Guanglei Wang Lingshu Kong School of Artificial Intelligence Hebei University of Technology Tianjin China Hebei province key laboratory of big data calculation Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Feature selection based on streaming labels has received extensive attention in recent years, which is a novel and challenging task for multi-label learning. It assumes that all labels are not given in advance and flow into the system gradually one by one over time. Existing information-theoretical-based feature selection methods with streaming labels use mutual information or conditional mutual information to measure the candidate feature correlation when a new label arrives. However, they do not consider the different influence of already-selected features for candidate feature correlation when facing different new labels. Furthermore, all already-selected features are calculated as having a redundant relationship with candidate features. In fact, some already-selected features are supplementary to candidate features with respect to different new labels. To address this issue, we first redivide the selected features to consider the different effects on the candidate feature correlation and redundancy for the newly arrived label. Second, a feature fusion strategy is used to fuse the subsets of features corresponding to all arrived labels. Finally, the proposed method compares to six other state-of-the-art feature selection methods on ten data sets and employs three evaluation metrics to verify the classification performance. Experimental results show that the proposed method is superior to other methods.

关键词： Measurement Correlation Fuses Termination of employment Feature extraction Complex systems Mutual information Optimization

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Unified Graph Augmentations for Generalized Contrastive Learning on Graphs 38

Unified Graph Augmentations for Generalized Contrastive Lear...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhuo, Jiaming Lu, Yintong Ning, Hui Fu, Kun Niu, Bingxin He, Dongxiao Wang, Chuan Guo, Yuanfang Wang, Zhen Cao, Xiaochun Yang, Liang Hebei Province Key Laboratory of Big Data Calculation School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Beijing JiaoTong University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

In real-world scenarios, networks (graphs) and their tasks possess unique characteristics, requiring the development of a versatile graph augmentation (GA) to meet the varied demands of network analysis. Unfortunately, most Graph Contrastive Learning (GCL) frameworks are hampered by the specificity, complexity, and incompleteness of their GA techniques. Firstly, GAs designed for specific scenarios may compromise the universality of models if mishandled. Secondly, the process of identifying and generating optimal augmentations generally involves substantial computational overhead. Thirdly, the effectiveness of the GCL, even the learnable ones, is constrained by the finite selection of GAs available. To overcome the above limitations, this paper introduces a novel unified GA module dubbed UGA after reinterpreting the mechanism of GAs in GCLs from a message-passing perspective. Theoretically, this module is capable of unifying any explicit GAs, including node, edge, attribute, and subgraph augmentations. Based on the proposed UGA, a novel generalized GCL framework dubbed Graph cOntrastive UnifieD Augmentations (GOUDA) is proposed. It seamlessly integrates widely adopted contrastive losses and an introduced independence loss to fulfill the common requirements of consistency and diversity of augmentation across diverse scenarios. Evaluations across various datasets and tasks demonstrate the generality and efficiency of the proposed GOUDA over existing state-of-the-art GCLs. © 2024 Neural information processing systems foundation. All rights reserved.

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PEFN: A Patches Enhancement and Hierarchical Fusion Network for Robust Vehicle Re-Identification

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IEEE Internet of Things Journal 2025年

作者： He, Wenying Wang, Feiyu Bai, Yude Xiong, Neal N. Xu, Guangquan Guo, Fei Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300130 China Tiangong University School of Software Tianjin300387 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK74464 United States Tianjin300350 China Central South University School of Computer Science and Engineering Changsha410083 China

Vehicle Re-Identification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of inter-class similarity and intra-class variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to re-identification. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a Patches Enhancement and hierarchical Fusion Network (PEFN) based on a multi-branch architecture, divided into a Global and Local Attention Supplement (GLAS) branch, and an Enhanced Hierarchical feature fusion (EnHi) branch. The GLAS branch, through the Identity-related Feature Remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle re-identification datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle re-identification approaches. Specifically, without utilizing extra data and re-ranking, our model achieves 85.15% mAP on the VeRi776 dataset. © 2014 IEEE.

关键词： Reamers

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Heterophily-Aware Graph Attention Network

arXiv

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arXiv 2023年

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Shen Yuan Honors College Beihang University Beijing100191 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Hebei University of Technology Tianjin300401 China

Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we first propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, enabling nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Graph Contrastive Learning with Joint Spectral Augmentation of Attribute and Topology 39

Graph Contrastive Learning with Joint Spectral Augmentation ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Liang Li, Zhenna Zhuo, Jiaming Liu, Jing Ma, Ziyi Wang, Chuan Wang, Zhen Cao, Xiaochun Hebei Province Key Laboratory of Big Data Calculation School of Artificial Intelligence Hebei University of Technology Tianjin China School of Computer Science and Technology Beijing JiaoTong University Beijing China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) School of Cybersecurity Northwestern Polytechnical University Xi’an China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-Sen University Shenzhen China

ISBN: (纸本)157735897X

As an essential technique for Graph Contrastive Learning (GCL), Graph Augmentation (GA) improves the generalization capability of the GCLs by introducing different forms of the same graph. To ensure information integrity, existing GA strategies have been designed to simultaneously process the two types of information available in graphs: node attributes and graph topology. Nonetheless, these strategies tend to augment the two types of graph information separately, ignoring their correlation, resulting in limited representation ability. To overcome this drawback, this paper proposes a novel GCL framework with a Joint spectrAl augMentation, named GCL-JAM. Motivated the equivalence between the graph learning objective on an attribute graph and the spectral clustering objective on the attribute-interpolated graph, the node attributes are first abstracted as another type of node to harmonize the node attributes and graph topology. The newly constructed graph is then utilized to perform spectral augmentation to capture the correlation during augmentation. Theoretically, the proposed joint spectral augmentation is proved to perturb more inter-class edges and noise attributes compared to separate augmentation methods. Extensive experiments on homophily and heterophily graphs validate the effectiveness and universality of GCL-JAM. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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A Modified Memetic Algorithm for Multi-depot Green Capacitated Arc Routing Problem 14th

A Modified Memetic Algorithm for Multi-depot Green Capacitat...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Cao, Bin Li, Ruichang Chen, Shuai Hebei University of Technology Tianjin300401 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi’an710071 China Hebei Provincial Key Laboratory of Big Data Calculation Tianjin300401 China

ISBN: (纸本)9789811534249

The capacitated arc routing problem (CARP) has important research significance in the area of vehicle routing services. We study a multi-depot green capacitated arc routing problem by minimizing economic cost, makespan and carbon emission cost, and then, present a modified memetic algorithm for multi-depot (MMAMD) that aims to solve the problem efficiently. According to the characteristics of multi-depots in this paper, a specific encoding scheme via the two-dimensional array and the dynamic adjustment strategy of boundary arcs between depots are proposed. By combining ant colony algorithm with local search, we present an ant colony local search strategy with extended neighborhood search for the memetic algorithm. The experimental results show that the proposed algorithm which is used to solve the model is effective, which can provide a reference for waste clearing decision. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Vehicle routing

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Multi-objective Pick-up Point Location Optimization Based on a Modified Genetic Algorithm 14th

Multi-objective Pick-up Point Location Optimization Based on...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Chen, Shuai Cao, Bin Li, Ruichang Hebei University of Technology Tianjin300401 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xi’an710071 China Hebei Provincial Key Laboratory of Big Data Calculation Tianjin300401 China

ISBN: (纸本)9789811534249

To solve the logistics terminal distribution problem, we construct the multi-objective location allocation model of pick-up point. The model considers the characteristics of express logistics system, enterprise demand and customer demand for delivery distance, and two objectives of minimizing location cost and maximizing customer distance satisfaction are included. We set the segmentation distance function according to the distance of the user from the pick-up point, and calculate the satisfaction function. To solve the model, we modify the non-dominated sorting genetic algorithm with elite strategy algorithm (NSGA-II). Consider the needs of the enterprise or user, we modify the crossover operator, and use the tournament method to evaluate the offspring population generated by genetic operations to reduce the loss of elite individuals. We also verify the effectiveness of the model and the modified algorithm through experiments. Experimental results show that the modified algorithm can obtain a better Pareto optimal solution set for the multi-objective location-allocation problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Location

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Energy-optimal problem of multiple nonholonomic wheeled mobile robots via distributed event-triggered optimization algorithm

引用

Chinese Physics B 2019年第3期28卷 122-129页

作者： Ying-Wen Zhang Jin-Huan Wang Yong Xu De-Dong Yang School of Science Hebei Province Key Laboratory of Big Data CalculationHebei University of TechnologyTianjin 300401China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China

The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the state of the hand position and represents battery energy consumption. By coordinate transformation, the dynamics of the hand positions can be formulated into two groups of first-order integrators. Then the distributed event-triggered optimization algorithm is designed such that the states of robots' hand positions exponentially converge to the optimizer of the global cost ***, the velocity and orientation of each robot are ensured to reach zero and a certain constant, respectively. Moreover, the inter-execution time is lower bounded and the Zeno behavior is therefore naturally avoided. Numerical simulations show the effectiveness of the proposed algorithm.

关键词： distributed optimization nonholonomic robots event-triggered energy-optimal consensus

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2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables

arXiv

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arXiv 2025年

作者： Zhang, Yajuan Jiang, Jiahai Yan, Yule Yang, Liang Zhang, Ping School of Artificial Intelligence Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China Hebei Province Key Laboratory of Big Data Calculation Hebei University of Technology Tianjin China

Accurate wind power forecasting can help formulate scientific dispatch plans, which is of great significance for maintaining the safety, stability, and efficient operation of the power system. In recent years, wind power forecasting methods based on deep learning have focused on extracting the spatiotemporal correlations among data, achieving significant improvements in forecasting accuracy. However, they exhibit two limitations. First, there is a lack of modeling for the inter-variable relationships, which limits the accuracy of the forecasts. Second, by treating endogenous and exogenous variables equally, it leads to unnecessary interactions between the endogenous and exogenous variables, increasing the complexity of the model. In this paper, we propose the 2DXformer, which, building upon the previous work’s focus on spatiotemporal correlations, addresses the aforementioned two limitations. Specifically, we classify the inputs of the model into three types: exogenous static variables, exogenous dynamic variables, and endogenous variables. First, we embed these variables as variable tokens in a channel-independent manner. Then, we use the attention mechanism to capture the correlations among exogenous variables. Finally, we employ a multi-layer perceptron with residual connections to model the impact of exogenous variables on endogenous variables. Experimental results on two real-world large-scale datasets indicate that our proposed 2DXformer can further improve the performance of wind power forecasting. The code is available in this repository: https://***/jseaj/2DXformer. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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