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PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 389卷 114399-114399页

作者： Ren, Pu Rao, Chengping Liu, Yang Wang, Jian-Xun Sun, Hao Northeastern Univ Dept Civil & Environm Engn Boston MA 02115 USA Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing 100872 Peoples R China MIT Dept Civil & Environm Engn 77 Massachusetts Ave Cambridge MA 02139 USA

Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep learning have shown the great potential of physics-informed neural networks (PINNs) to solve PDEs as a basis for data-driven modeling and inverse analysis. However, the majority of existing PINN methods, based on fully-connected NNs, pose intrinsic limitations to low-dimensional spatiotemporal parameterizations. Moreover, since the initial/boundary conditions (I/BCs) are softly imposed via penalty, the solution quality heavily relies on hyperparameter tuning. To this end, we propose the novel physics-informed convolutional-recurrent learning architectures (PhyCRNet and PhyCRNet-s) for solving PDEs without any labeled data. Specifically, an encoder-decoder convolutional long short-term memory network is proposed for low-dimensional spatial feature extraction and temporal evolution learning. The loss function is defined as the aggregated discretized PDE residuals, while the I/BCs are hard-encoded in the network to ensure forcible satisfaction (e.g., periodic boundary padding). The networks are further enhanced by autoregressive and residual connections that explicitly simulate time marching. The performance of our proposed methods has been assessed by solving three nonlinear PDEs (e.g., 2D Burgers' equations, the lambda-omega and FitzHugh Nagumo reaction-diffusion equations), and compared against the start-of-the-art baseline algorithms. The numerical results demonstrate the superiority of our proposed methodology in the context of solution accuracy, extrapolability and generalizability. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Convolutional-recurrent learning Partial differential equations encoder-decoder Physics-informed deep learning Residual connection Hard-encoding of I/BCs

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Recurrent Attention and Semantic Gate for Remote Sensing Image Captioning

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Li, Yunpeng Zhang, Xiangrong Gu, Jing Li, Chen Wang, Xin Tang, Xu Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

The remote sensing image captioning has attracted wide spread attention in remote sensing field due to its application potentiality. However, most existing approaches model limited interactions between image content and sentence and fail to exploit special characteristics of the remote sensing images. We introduce a novel recurrent attention and semantic gate (RASG) framework to facilitate the remote sensing image captioning in this article, which integrates competitive visual features and a recurrent attention mechanism to generate a better context vector for the images every time as well as enhances the representations of the current word state. Specifically, we first project each image into competitive visual features by taking the advantage of both static visual features and multiscale features. Then, a novel recurrent attention mechanism is developed to extract the high-level attentive maps from encoded features and nonvisual features, which can help the decoder recognize and focus on the effective information for understanding the complex content of the remote sensing images. Finally, the hidden states from the long short-term memory (LSTM) and other semantic references are incorporated into a semantic gate, which contributes to more comprehensive and precise semantic understanding. Comprehensive experiments on three widely used datasets, Sydney-Captions, UCM-Captions, and Remote Sensing Image Captioning Dataset, have demonstrated the superiority of the proposed RASG over a series of attentive models based on image captioning methods.

关键词： Feature extraction Semantics Visualization Remote sensing Logic gates Decoding Neural networks Attention mechanism encoder-decoder remote sensing image captioning semantic understanding

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Aspect-based sentiment analysis with attention-assisted graph and variational sentence representation

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KNOWLEDGE-BASED SYSTEMS 2022年 258卷

作者： Feng, Shi Wang, Bing Yang, Zhiyao Ouyang, Jihong Jilin Univ Coll Comp Sci & Technol Changchun 130022 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130022 Peoples R China

Aspect-based sentiment analysis (ABSA) is a fine-grained task that detects the sentiment polarities of particular aspect words in a sentence. With the rise of graph convolution networks (GCNs), current ABSA models mostly use graph-based methods. These methods construct a dependency tree for each sentence, and regard each word as a unique node. To be more specific, they conduct classification using aspect representations instead of sentence representations, and update them with GCNs. However, this kind of method relies too much on the quality of the dependency tree and may lose the global sentence information, which is also helpful for classification. To deal with these, we design a new ABSA model AG-VSR. Two kinds of representations are proposed to perform the final classification, Attention-assisted Graph-based Representation (A2GR) and Variational Sentence Representation (VSR). A2GR is produced by the GCN module, which inputs a dependency tree modified by the attention mechanism. Furthermore, VSR is sampled from a distribution learned by a VAE-like encoder-decoder structure. Extensive experiments show that our model AG-VSR achieves competitive results. Our code and data have been released in https://***/wangbing1416/VAGR.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Aspect-based sentiment analysis Graph neural network encoder-decoder Self-attention

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Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework

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SENSORS 2022年第15期22卷 5861-5861页

作者： Li, Yuxuan Yang, Chunjie Sun, Youxian Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.

关键词： LSTM semi-supervised learning FeO content soft sensor encoder-decoder dynamic feature extraction

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Deep convolutional neural networks for semantic segmentation of cracks

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STRUCTURAL CONTROL & HEALTH MONITORING 2022年第1期29卷 e2850-e2850页

作者： Wang, Jia-Ji Liu, Yu-Fei Nie, Xin Mo, Y. L. Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Univ Houston Dept Civil & Environm Engn Houston TX 77204 USA

A large crack detection dataset of 2446 manually labeled images is established to cover a wide range of noise and to evaluate the performance of end-to-end deep convolutional networks in detecting cracking. Five state-of-the-art end-to-end deep computer vision architectures for semantic segmentation are trained and evaluated, including Fully Convolutional Network (FCN), Global Convolutional Network (GCN), Pyramid Scene Parsing Network (PSPNet), UPerNet, and DeepLabv3+. For the backbones, the VGG, ResNet, and DenseNet are adopted. Based on the comparison of test set metrics, DeepLabv3+ with the ResNet101 backbone achieved the highest IoU of 0.6298, the highest recall of 0.6834, and the highest F1 score of 0.7732. The influence of database choice and image noise on crack detection performance is reported. Based on the comparison of predicted images, UperNet with ResNet101 backbone shows the highest performance for images with shadings, while DeepLabv3+ with ResNet101 backbone shows the best performance for images with blemishes. The research outcome can provide reference for the application of fast and accurate detection of cracks in civil engineering.

关键词： computer vision convolutional neural network crack deep learning encoder-decoder semantic segmentation

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Asymmetric cross-modal activation network for RGB-T salient object detection

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KNOWLEDGE-BASED SYSTEMS 2022年 258卷

作者： Xu, Chang Li, Qingwu Zhou, Qingkai Jiang, Xiongbiao Yu, Dabing Zhou, Yaqin Hohai Univ Coll Internet Things Engn Changzhou 213022 Jiangsu Peoples R China Hohai Univ Jiangsu Key Lab Power Transmiss & Distribut Equipm Changzhou 213022 Jiangsu Peoples R China

RGB-thermal salient object detection (RGB-T SOD) has unique advantages in terms of handling challenging scenes with cluttered backgrounds, low illumination, and low contrast. However, because they do not consider the significant differences between different imaging mechanisms and inherent characteristics of thermal images, existing RGB-T SOD methods are generally unable to handle diverse feature fusion demands and may yield unsatisfactory performance. To overcome this problem and achieve more effective RGB-T SOD, we propose an asymmetric cross-modal activation network to exploit the interactions of modality-specific features based on an asymmetric feature fusion strategy. Specifically, a two-stream asymmetric feature aggregation encoder module is proposed to fuse multimodality features adaptively and extract complementary information. The self-attention of multimodality features is leveraged to guide cross-modal interactions, which can propagate long-range contextual dependencies and extract effective saliency cues. Furthermore, a multitask decoder is proposed to achieve SOD and thermal image reconstruction in a unified framework. Salient objects can be located and segmented accurately based on reconstructed high-resolution feature representations. Extensive experiments on public RGB-T and RGB-D SOD datasets demonstrate the superiority of the proposed network and ablation experiments highlight the effectiveness of each component. Our code and saliency maps are available at: ***/xanxuso/ACMANet.(c) 2022 Elsevier B.V. All rights reserved.

关键词： RGB-T salient object detection Multimodality Feature fusion encoder-decoder Long-range dependence

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Rethinking Table Structure Recognition Using Sequence Labeling Methods 16th

Rethinking Table Structure Recognition Using Sequence Labeli...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Li, Yibo Huang, Yilun Zhu, Ziyi Pan, Lemeng Huang, Yongshuai Du, Lin Tang, Zhi Gao, Liangcai Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Huawei AI Applicat Res Ctr Huawei Peoples R China

ISBN: (纸本)9783030863319

Table structure recognition is an important task in document analysis and attracts the attention of many researchers. However, due to the diversity of table types and the complexity of table structure, the performances of table structure recognition methods are still not well enough in practice. Row and column separators play a significant role in the two-stage table structure recognition and a better row and column separator segmentation result can improve the final recognition results. Therefore, in this paper, we present a novel deep learning model to detect row and column separators. This model contains a convolution encoder and two parallel row and column decoders. The encoder can extract the visual features by using convolution blocks;the decoder formulates the feature map as a sequence and uses a sequence labeling model, bidirectional long short-term memory networks (BiLSTM) to detect row and column separators. Experiments have been conducted on PubTabNet and the model is benchmarked on several available datasets, including Pub-TabNet, UNLV ICDAR13, ICDAR19. The results show that our model has a state-of-the-art performance than other strong models. In addition, our model shows a better generalization ability. The code is available on this site (www ***/L597383845/row-col-table-recognition).

关键词： Table structure recognition encoder-decoder Row and column separators segmentation Sequence labeling model

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HRLINKNET: LINKNET WITH HIGH-RESOLUTION REPRESENTATION FOR HIGH-RESOLUTION SATELLITE IMAGERY

HRLINKNET: LINKNET WITH HIGH-RESOLUTION REPRESENTATION FOR H...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wu, Muyu Shu, Zhen Zhang, Jinming Hu, Xiangyun Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781665403696

Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating. However, due to the general texture of the building and the complexity of the image background, high-precision building segmentation from high-resolution sensing image is still a challenging task. Existing state-of-the-art frameworks use repeated pooling and step operations leading to the loss of detailed information. Thus, high-resolution representations are essential for building extraction. On this basis, our proposed network, named as HRLinkNet, maintains high-resolution representations through the whole process based on the LinkNet. We tested it on WHU Building dataset. Experimental results show that the proposed HRLinkNet is superior to the LinkNet, UNet, DLinkNet, segnet and so on.

关键词： Deep learning high-resolution remote sensing imagery building extraction semantic segmentation encoder-decoder fully convolutional networks

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Deep learning-based multi-output quantile forecasting of PV generation

Deep learning-based multi-output quantile forecasting of PV ...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Dumas, Jonathan Cointe, Colin Fettweis, Xavier Cornelusse, Bertrand Univ Liege Dept Comp Sci Liege Belgium Univ Liege Dept Elect Engn Liege Belgium Univ Liege Dept Geog Liege Belgium

ISBN: (纸本)9781665435970

This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. It tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi-output PV quantiles forecasts to efficiently capture the time correlation. The models are trained using quantile regression, a non-parametric approach that assumes no prior knowledge of the probabilistic forecasting distribution. The case study is composed of PV production monitored on-site at the University of Liege (ULiege), Belgium. The weather forecasts from the regional climate model provided by the Laboratory of Climatology are used as inputs of the deep learning models. The forecast quality is quantitatively assessed by the continuous ranked probability and interval scores. The results indicate this architecture improves the forecast quality and is computationally efficient to be incorporated in an intraday decision-making tool for robust optimization.

关键词： Quantile forecasting probabilistic PV forecasting LSTM deep learning encoder-decoder

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Extractive-Abstractive Summarization of Judgment Documents Using Multiple Attention Networks 4th

Extractive-Abstractive Summarization of Judgment Documents U...

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4th International Conference on Logic and Argumentation (CLAR)

作者： Gao, Yan Liu, Zhengtao Li, Juan Guo, Fan Xiao, Fei Cent South Univ Sch Automat Changsha 410083 Peoples R China Cent South Univ Sch Law Changsha Peoples R China

ISBN: (纸本)9783030893910;9783030893903

Judgment documents contain rich legal information, they are simultaneously lengthy with complex structure. This requires summarizing judgment documents in an effective way. By analyzing the structural features of Chinese judgment documents, we propose an automatic summarization method, which consists of an extraction model and an abstraction model. In the extraction model, all the sentences are encoded by a Self-Attention network and are classified into key sentences and non-key sentences. In the abstraction model, the initial summarization is refined into a final summarization by a unidirectional-bidirectional attention network. Such a summarization could help improve the efficiency in case handling and make judgment documents more accessible to the general readers. The experimental results on CAIL2020 dataset are satisfactory.

关键词： Judgment documents Automatic summarization Attention network encoder-decoder

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