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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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A Dual-Attention-Based Stock Price Trend Prediction Model With Dual Features

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IEEE ACCESS 2019年 7卷 148047-148058页

作者： Chen, Yingxuan Lin, Weiwei Wang, James Z. South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Clemson Univ Sch Comp Clemson SC 29631 USA Guangdong Luan Ind & Commerce Co Ltd Guangzhou 510520 Peoples R China

Modeling and predicting stock prices is an important and challenging task in the field of financial market. Due to the high volatility of stock prices, traditional data mining methods cannot identify the most relevant and critical market data for predicting stock price trend. This paper proposes a stock price trend predictive model (TPM) based on an encoder-decoder framework that predicting the stock price movement and its duration adaptively. This model consists of two phases, first, a dual feature extraction method based on different time spans is proposed to get more information from the market data. While traditional methods only extract features from information at some specific time points, this proposed model applies the PLR method and CNN to extract the long-term temporal features and the short-term spatial features from market data. Then, in the second phase of the proposed TPM, a dual attention mechanism based encoder-decoder framework is used to select and merge relevant dual features and predict the stock price trend. To evaluate our proposed TPM, we collected high-frequency market data for stock indexes CSI300, SSE 50 and CSI 500, and conducted experiments based on these three data sets. The experimental results show that the proposed TPM outperforms the existing state-of-art methods, including SVR, LSTM, CNN, LSTM_CNN and TPM_NC, in terms of prediction accuracy.

关键词： Stock price predictive models neural networks encoder-decoder dual feature extraction dual attention mechanism

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Lightweight semantic segmentation network with configurable context and small object attention

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2023年 17卷 1280640页

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Li, Jinzhao Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China Univ Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Beijing Peoples R China

The current semantic segmentation algorithms suffer from encoding feature distortion and small object feature loss. Context information exchange can effectively address the feature distortion problem, but it has the issue of fixed spatial range. Maintaining the input feature resolution can reduce the loss of small object information but would slow down the network's operation speed. To tackle these problems, we propose a lightweight semantic segmentation network with configurable context and small object attention (CCSONet). CCSONet includes a long-short distance configurable context feature enhancement module (LSCFEM) and a small object attention decoding module (SOADM). The LSCFEM differs from the regular context exchange module by configuring long and short-range relevant features for the current feature, providing a broader and more flexible spatial range. The SOADM enhances the features of small objects by establishing correlations among objects of the same category, avoiding the introduction of redundancy issues caused by high-resolution features. On the Cityscapes and Camvid datasets, our network achieves the accuracy of 76.9 mIoU and 73.1 mIoU, respectively, while maintaining speeds of 87 FPS and 138 FPS. It outperforms other lightweight semantic segmentation algorithms in terms of accuracy.

关键词： semantic segmentation lightweight network context feature enhancement small object attention encoder-decoder

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Linear Contrast Enhancement Network for Low-Illumination Image Enhancement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhou, Zhaorun Shi, Zhenghao Ren, Wenqi Xian Univ Technol Dept Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China

Images captured under low-illumination conditions usually suffer from severe degradations, such as fading and low contrast, drastically affecting the performance of systems relying on images under low-illumination conditions. To address such problems, this study proposes a linear contrast enhancement network (LCENet) for low-illumination image enhancement. It consists of three subnets: two encoder-decoder-based subnets for gradient map restoration and brightness enhancement, respectively, and a backbone network for adaptive brightness and contrast adjustment. In addition, a linear contrast enhancement adaptive instance normalization (LCEAIN) module with linear contrast enhancement ability is proposed in the backbone network, which can avoid the problem of ignoring contrast enhancement when enhancing image brightness. Considerable evaluations on both synthetic and real low-illumination images show that the proposed method performs favorably against other existing similar methods. Moreover, our method can handle complex low-illuminance conditions and has good generalization for low-illuminance scenes with backlighting, night scenes with light sources, as well as underwater scenes with low illuminance. Code: https://***/zhouzhaorun/LCENet.

关键词： Brightness Image restoration Lighting Image enhancement Deep learning Image color analysis Task analysis Convolutional neural network encoder-decoder linear contrast enhancement low-illumination image enhancement

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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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A semi-supervised deep learning model for ship encounter situation classification

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OCEAN ENGINEERING 2021年 239卷 109824-109824页

作者： Chen, Xiang Liu, Yuanchang Achuthan, Kamalasudhan Zhang, Xinyu Chen, Jinhai UCL Dept Civil Environm & Geomat Engn Chadwick Bldg London WC1E 6BT England UCL Dept Mech Engn Torrington Pl London WC1E 7JE England Dalian Maritime Univ Key Lab Maritime Dynam Simulat & Control Minist Transportat Dalian 116026 Peoples R China Jimei Univ Nav Coll Nationallocal Joint Engn Res Ctr Marine Nav Aids Serv Xiamen 361021 Peoples R China

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship's operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships' reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships' encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship's AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship's movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi Supervised Convolutional encoder-decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder-decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by o

关键词： Automatic Identification System (AIS) Semi-supervised learning Deep learning Convolutional neural network encoder-decoder Trajectory data Encounter situation classification

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FPA-Net: Frequency-Guided Position-Based Attention Network for Land Cover Image Segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第11期37卷 2354015-2354015页

作者： Rubel, Al Shahriar Shih, Frank Y. New Jersey Inst Technol Dept Informat Newark NJ 07102 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan

Land cover segmentation has been a significant research area because of its multiple applications including the infrastructure development, forestry, agriculture, urban planning, and climate change research. In this paper, we propose a novel segmentation method, called Frequency-guided Position-based Attention Network (FPA-Net), for land cover image segmentation. Our method is based on encoder-decoder improved U-Net architecture with position-based attention mechanism and frequency-guided component. The position-based attention block is used to capture the spatial dependency among different feature maps and obtain the relationship among relevant patterns across the image. The frequency-guided component provides additional support with high-frequency features. Our model is simple and efficient in terms of time and space complexities. Experimental results on the Deep Globe, GID-15, and Land Cover AI datasets show that the proposed FPA-Net can achieve the best performance in both quantitative and qualitative measures as compared against other existing approaches.

关键词： Land cover segmentation encoder-decoder attention mechanism residual connection frequency-guided component U-Net

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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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Network traffic prediction with Attention-based Spatial-Temporal Graph Network

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COMPUTER NETWORKS 2024年 243卷

作者： Peng, Yufei Guo, Yingya Hao, Run Xu, Chengzhe Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent Inf Fuzhou Peoples R China Minist Educ Engn Res Ctr Big Data Intelligence Beijing Peoples R China

Network traffic prediction plays a significant role in network management. Previous network traffic prediction methods mainly focus on the temporal relationship between network traffic, and used time series models to predict network traffic, ignoring the spatial information contained in traffic data. Therefore, the prediction accuracy is limited, especially in long-term prediction. To improve the prediction accuracy of the dynamic network traffic in the long term, we propose an Attention -based Spatial-Temporal Graph Network (ASTGN) model for network traffic prediction to better capture both the temporal and spatial relations between the network traffic. Specifically, in ASTGN, we exploit an encoder-decoder architecture, where the encoder encodes the input network traffic and the decoder outputs the predicted network traffic sequences, integrating the temporal and spatial information of the network traffic data through the Spatio-Temporal Embedding module. The experimental results demonstrate the superiority of our proposed method ASTGN in long-term prediction.

关键词： Network traffic prediction Attention mechanism Graph neural network Temporal and spatial encoder-decoder

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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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