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学者姓名：胡永利
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Abstract ：
Formulating optimal bidding strategies is pivotal for market participants to enhance electricity market profits. The main challenge for finding optimal bidding strategies is how to deal with system uncertainty, which stems from the inherent unpredictability and fluctuation within the electricity market. In the previous works, deep reinforcement learning (DRL) is proved a promising approach in multi-agent system with uncertainty. But few works model the relevance between agents for processing system uncertainty, especially the dynamic correlation in the operation of market. For this purpose, this paper proposes to model the correlation between agents to cope with the system uncertainty in a representative centralized double-sided auction market by combining graph convolutional neural network (GCN) with deep deterministic policy gradient (DDPG) algorithm, which is not only able to deal with the system uncertainty by aggregating correlative information of neighboring agents, but also helps obtain superior bidding strategies for the market participants. The proposed algorithm is evaluated on 4-bus, 30-bus and 57-bus congested network, where both supply side and demand side with elastic demand are modeled as RL agents. The results demonstrate that the proposed algorithm achieves higher system profits than the DRL based algorithms without GCN.
Keyword ：
Optimal bidding strategy Optimal bidding strategy Electricity market Electricity market Graph convolutional neural network (GCN) Graph convolutional neural network (GCN) Deep reinforcement learning (DRL) Deep reinforcement learning (DRL) Multi-agent systems (MAS) Multi-agent systems (MAS)
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| GB/T 7714 | Weng, Haoen , Hu, Yongli , Liang, Min et al. Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning [J]. | APPLIED ENERGY , 2025 , 380 . |
| MLA | Weng, Haoen et al. "Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning" . | APPLIED ENERGY 380 (2025) . |
| APA | Weng, Haoen , Hu, Yongli , Liang, Min , Xi, Jiayang , Yin, Baocai . Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning . | APPLIED ENERGY , 2025 , 380 . |
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Abstract ：
In industrial manufacturing, how to accurately classify defective products and locate the location of defects has always been a concern. Previous studies mainly measured similarity based on extracting single-scale features of samples. However, only using the features of a single scale is hard to represent different sizes and types of anomalies. Therefore, the authors propose a set of memory banks of multi-scale features (MBMF) to enrich feature representation and detect and locate various anomalies. To extract features of different scales, different aggregation functions are designed to produce the feature maps at different granularity. Based on the multi-scale features of normal samples, the MBMF are constructed. Meanwhile, to better adapt to the feature distribution of the training samples, the authors proposed a new iterative updating method for the memory banks. Testing on the widely used and challenging dataset of MVTec AD, the proposed MBMF achieves competitive image-level anomaly detection performance (Image-level Area Under the Receiver Operator Curve (AUROC)) and pixel-level anomaly segmentation performance (Pixel-level AUROC). To further evaluate the generalisation of the proposed method, we also implement anomaly detection on the BeanTech AD dataset, a commonly used dataset in the field of anomaly detection, and the Fashion-MNIST dataset, a widely used dataset in the field of image classification. The experimental results also verify the effectiveness of the proposed method. The authors propose a novel approach for accurate classification and localisation of defects in industrial manufacturing. The method, called Memory Banks of Multi-Scale Features (MBMF), enriches feature representation by utilising multi-scale features extracted through different aggregation functions. The authors demonstrate the effectiveness of MBMF through competitive performance in image-level anomaly detection and pixel-level anomaly segmentation on the challenging MVTec AD dataset.image
Keyword ：
feature extraction feature extraction unsupervised learning unsupervised learning computer vision computer vision convolutional neural nets convolutional neural nets Gaussian distribution Gaussian distribution
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| GB/T 7714 | Sun, Yanfeng , Wang, Haitao , Hu, Yongli et al. MBMF: Constructing memory banks of multi-scale features for anomaly detection [J]. | IET COMPUTER VISION , 2023 , 18 (3) : 355-369 . |
| MLA | Sun, Yanfeng et al. "MBMF: Constructing memory banks of multi-scale features for anomaly detection" . | IET COMPUTER VISION 18 . 3 (2023) : 355-369 . |
| APA | Sun, Yanfeng , Wang, Haitao , Hu, Yongli , Jiang, Huajie , Yin, Baocai . MBMF: Constructing memory banks of multi-scale features for anomaly detection . | IET COMPUTER VISION , 2023 , 18 (3) , 355-369 . |
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Abstract ：
Our paper proposes a new feature extraction method, named as robust discriminant analysis (RDA), for data classification tasks. Based on linear discriminant analysis (LDA), RDA integrates the feature selection and feature extraction into a unified framework. The transformation matrix with l2,1-norm constraint is introduced to map original data feature into a discriminative low-dimensional subspace, in which the l2,1 sparsity regularizer can endow the feature selection with better interpretability. And, we use two different matrices (i.e., transformation matrix P and reconstruction matrix Q) for better data reconstruction, which can provide more freedom to ensure that the learned data representation holds the main variance and hence improve robustness to noises. To ensure that the learned features are optimal for classification, the structurally incoherent learning is introduced to add additional discriminant ability by minimizing the correlation of different classes. In other hand, the between-classes structural incoherence term is also equivalent to cosine distance metric, which is robust to noises and outliers. An efficient optimization algorithm is designed to solve the proposed optimization model. Extensive experiments conducted on all kinds of benchmark databases confirm the superiority of the proposed method.(c) 2022 Elsevier Inc. All rights reserved.
Keyword ：
Robust data reconstruction Robust data reconstruction Feature selection Feature selection Feature extraction Feature extraction Structural incoherence Structural incoherence Linear discriminant analysis Linear discriminant analysis
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| GB/T 7714 | Guo, Jipeng , Sun, Yanfeng , Gao, Junbin et al. Robust discriminant analysis with feature selective projection and between-classes structural incoherence [J]. | DIGITAL SIGNAL PROCESSING , 2023 , 134 . |
| MLA | Guo, Jipeng et al. "Robust discriminant analysis with feature selective projection and between-classes structural incoherence" . | DIGITAL SIGNAL PROCESSING 134 (2023) . |
| APA | Guo, Jipeng , Sun, Yanfeng , Gao, Junbin , Hu, Yongli , Yin, Baocai . Robust discriminant analysis with feature selective projection and between-classes structural incoherence . | DIGITAL SIGNAL PROCESSING , 2023 , 134 . |
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Abstract ：
Traffic prediction is an important part of intelligent transportation system. Recently, graph convolution network (GCN) is introduced for traffic flow forecasting and achieves good performance due to its superiority of representing the graph traffic road structure network. Moreover, the dynamic GCN is put forward to model the temporal property of the traffic flow. Although great progress has been made, most GCN based traffic flow forecasting methods utilize a single graph for convolution, which is considered not enough to reveal the inherent property of traffic graph as it is influenced by many factors, for example weather, season and traffic accidents etc. In this paper, an exotic graph transformer based dynamic multiple graph convolution networks (GTDMGCN) is conceived for traffic flow forecasting. Instead of the single graph, multiple graphs are constructed to modulate the complex traffic network by the proposed graph transformer network. Additionally, a temporal gate convolution is proposed to get the temporal property of traffic flow. The proposed GTDMGCN model is evaluated on four real traffic datasets of PEMS03, PEMS04, PEMS07, PEMS08, and there are average increments of 9.78%, 7.80%, 5.96% under MAE, RMSE, and MAPE metrics compared with the current results.
Keyword ：
intelligent transportation systems intelligent transportation systems traffic information systems traffic information systems
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| GB/T 7714 | Hu, Yongli , Peng, Ting , Guo, Kan et al. Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting [J]. | IET INTELLIGENT TRANSPORT SYSTEMS , 2023 , 17 (9) : 1835-1845 . |
| MLA | Hu, Yongli et al. "Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting" . | IET INTELLIGENT TRANSPORT SYSTEMS 17 . 9 (2023) : 1835-1845 . |
| APA | Hu, Yongli , Peng, Ting , Guo, Kan , Sun, Yanfeng , Gao, Junbin , Yin, Baocai . Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting . | IET INTELLIGENT TRANSPORT SYSTEMS , 2023 , 17 (9) , 1835-1845 . |
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Abstract ：
基于跨模态多粒度交互融合的长文档分类方法及装置，能够有效弥补现有方法对视觉信息的忽视，通过引入特征偏移网络在不同粒度实现跨模态的交互和融合，控制计算复杂度，达到分类准确率和分类效率的平衡。方法包括：(1)输入一个长文档中对应的文本序列，以及对应的单张或多张图片；(2)分别通过预训练编码器BERT和VGG‑16提取对应模态的多粒度特征表示；(3)使用多模态协同池化模块，在视觉信息和文本信息的协同引导下池化细粒度文本特征；(4)使用跨模态特征偏移网络，分别在4个不同的粒度组合下实现跨模态特征的交互和融合；(5)使用特征聚合网络实现多空间特征的融合，并获得最终的长文档分类结果。
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| GB/T 7714 | 胡永利 , 刘腾飞 , 孙艳丰 et al. 基于跨模态多粒度交互融合的长文档分类方法及装置 : CN202310301100.7[P]. | 2023-03-24 . |
| MLA | 胡永利 et al. "基于跨模态多粒度交互融合的长文档分类方法及装置" : CN202310301100.7. | 2023-03-24 . |
| APA | 胡永利 , 刘腾飞 , 孙艳丰 , 尹宝才 . 基于跨模态多粒度交互融合的长文档分类方法及装置 : CN202310301100.7. | 2023-03-24 . |
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Abstract ：
基于层次多粒度交互图卷积网络的长文档分类方法及装置，在控制模型计算复杂度的情况下，能够构建网络以刻画长文档完备的层次结构化信息，以及进行图间信息交互。方法包括：(1)获得长文档层次化多粒度表示；(2)执行多层层次叠加的段落图卷积、句子图卷积和单词图卷积，以及相应的图间交互；(3)为了融合不同粒度不同尺度的语义信息，使用最大池化分别聚合段落图的终层输出，以及句子图和单词图每一层的输出。
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| GB/T 7714 | 胡永利 , 刘腾飞 , 孙艳丰 et al. 基于层次多粒度交互图卷积网络的长文档分类方法及装置 : CN202310316635.1[P]. | 2023-03-24 . |
| MLA | 胡永利 et al. "基于层次多粒度交互图卷积网络的长文档分类方法及装置" : CN202310316635.1. | 2023-03-24 . |
| APA | 胡永利 , 刘腾飞 , 孙艳丰 , 尹宝才 . 基于层次多粒度交互图卷积网络的长文档分类方法及装置 : CN202310316635.1. | 2023-03-24 . |
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Abstract ：
本发明公开了一种面向引文数据聚类的多图卷积聚类方法，构造蕴含原始数据底层结构信息的K最近邻图，为了提取原始数据的底层结构信息，对每一个样本数据，将计算样本数据与不同样本数据之间的余弦相似度。基于构建K最近邻图，获取到原始数据的底层结构信息，用自编码模块增强的数据表证，引入更多已经构建的关系图；经过多图卷积模块，得到了经过不同图卷积操作的特征表示。受到自注意力机制的启发，寻求在每一个节点之间学习相对应的自适应权重。通过融合各个视图的信息来获得丰富的判别信息，同时使得聚类效果得到提升。本发明通过利用多图数据，降低了对单图质量的依赖性，与现有的图卷积聚类方法相比，模型更具鲁棒性。
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| GB/T 7714 | 王博岳 , 王一凡 , 贺霞霞 et al. 一种面向引文数据聚类的多图卷积聚类方法 : CN202310183587.3[P]. | 2023-03-01 . |
| MLA | 王博岳 et al. "一种面向引文数据聚类的多图卷积聚类方法" : CN202310183587.3. | 2023-03-01 . |
| APA | 王博岳 , 王一凡 , 贺霞霞 , 刘洋 , 胡永利 . 一种面向引文数据聚类的多图卷积聚类方法 : CN202310183587.3. | 2023-03-01 . |
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Abstract ：
本发明公开了基于弱化低质量负样本的时序知识图谱补全方法，为了平衡负样本多样性和负样本质量，该方法使用高质量和中等质量的负样本以增强模型判别能力，即弱化低质量负样本产生的影响。该方法提出的低质量负样本选择和弱化模块可以挑选出低质量负样本并调节它们的分数以弱化低质量负样本的消极影响。在交叉熵损失中引入了自适应加权负样本损失正则化项，该正则化项计算了每个负样本的损失值，并自适应地为每个负样本损失值分配不同的权重，以充分利用不同质量负样本的潜在信息。自适应加权负样本损失正则化项与低质量负样本选择和弱化模块都起到了积极影响。
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| GB/T 7714 | 王博岳 , 胡思敏 , 王家普 et al. 基于弱化低质量负样本的时序知识图谱补全方法 : CN202310266322.X[P]. | 2023-03-20 . |
| MLA | 王博岳 et al. "基于弱化低质量负样本的时序知识图谱补全方法" : CN202310266322.X. | 2023-03-20 . |
| APA | 王博岳 , 胡思敏 , 王家普 , 赵岚 , 胡永利 . 基于弱化低质量负样本的时序知识图谱补全方法 : CN202310266322.X. | 2023-03-20 . |
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Abstract ：
本发明公开了一种基于多阶段模态表示的情感预测方法，分为编码阶段、模态互补阶段和预测阶段；本方法在特征表示学习阶段，改进了多模态输入之间的互信息，以过滤掉与任务无关的模态特定随机噪声，在所有模态中保留尽可能多的模态不变内容。其次，在特征融合阶段，训练鉴别器区分这些融合表示来自哪些模态，并保持模态彼此独立。在预测阶段，对融合后的不同特征之间的距离进行约束，并将它们投影到不同的特征空间。本发明所提出的网络模型取得了更好的性能；同时也做了相关的消融实验，最终实验证明，本发明所提出的基于多阶段模态表示的情感预测方法能使得多模态情感预测任务达到最优值。
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| GB/T 7714 | 尹宝才 , 李旭浩 , 胡永利 . 一种基于多阶段模态表示的情感预测方法 : CN202310209523.6[P]. | 2023-03-07 . |
| MLA | 尹宝才 et al. "一种基于多阶段模态表示的情感预测方法" : CN202310209523.6. | 2023-03-07 . |
| APA | 尹宝才 , 李旭浩 , 胡永利 . 一种基于多阶段模态表示的情感预测方法 : CN202310209523.6. | 2023-03-07 . |
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Abstract ：
Self-expressiveness based subspace clustering methods have received wide attention for unsupervised learning tasks. However, most existing subspace clustering methods consider data features as a whole and then focus only on one single self-representation. These approaches ignore the intrinsic multi-attribute information embedded in the original data feature and result in one-attribute self-representation. This paper proposes a novel multi-attribute subspace clustering (MASC) model that understands data from multiple attributes. MASC simultaneously learns multiple subspace representations corresponding to each specific attribute by exploiting the intrinsic multi-attribute features drawn from original data. In order to better capture the high-order correlation among multi-attribute representations, we represent them as a tensor in low-rank structure and propose the auto-weighted tensor nuclear norm (AWTNN) as a superior low-rank tensor approximation. Especially, the non-convex AWTNN fully considers the difference between singular values through the implicit and adaptive weights splitting during the AWTNN optimization procedure. We further develop an efficient algorithm to optimize the non-convex and multi-block MASC model and establish the convergence guarantees. A more comprehensive subspace representation can be obtained via aggregating these multi-attribute representations, which can be used to construct a clustering-friendly affinity matrix. Extensive experiments on eight real-world databases reveal that the proposed MASC exhibits superior performance over other subspace clustering methods.
Keyword ：
Subspace clustering Subspace clustering multi-attribute representation learning multi-attribute representation learning auto-weighted tensor nuclear norm auto-weighted tensor nuclear norm non-convex low-rank tensor approximation non-convex low-rank tensor approximation
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| GB/T 7714 | Guo, Jipeng , Sun, Yanfeng , Gao, Junbin et al. Multi-Attribute Subspace Clustering via Auto-Weighted Tensor Nuclear Norm Minimization [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2022 , 31 : 7191-7205 . |
| MLA | Guo, Jipeng et al. "Multi-Attribute Subspace Clustering via Auto-Weighted Tensor Nuclear Norm Minimization" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 31 (2022) : 7191-7205 . |
| APA | Guo, Jipeng , Sun, Yanfeng , Gao, Junbin , Hu, Yongli , Yin, Baocai . Multi-Attribute Subspace Clustering via Auto-Weighted Tensor Nuclear Norm Minimization . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2022 , 31 , 7191-7205 . |
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