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A Radar Echo Extrapolation Model Based on a Dual-Branch encoder-decoder and Spatiotemporal GRU

ATMOSPHERE 2024年第1期15卷 104页

作者： Cheng, Yong Qu, Haifeng Wang, Jun Qian, Kun Li, Wei Yang, Ling Han, Xiaodong Liu, Min Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Math & Stat Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Appl Technol Nanjing 210044 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Nanjing Univ Informat Sci & Technol Sch Hydrol & Water Resources Nanjing 210044 Peoples R China

Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder-decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder-decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model's ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects.

关键词： radar echo extrapolation precipitation forecast encoder-decoder GRU

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State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture

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JOURNAL OF POWER SOURCES 2020年 449卷 227558-000页

作者： Bian, Chong He, Huoliang Yang, Shunkun Huang, Tingting Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient temperatures and cannot provide useful indications for battery state in real-world scenarios because batteries usually experience varying temperatures during operation. In this study, an encoder-decoder with bidirectional long short-term memory (LSTM) is proposed for estimating the SOC at different temperature conditions. This end-to-end model can learn sequential information from the measurement sequences to characterize battery dynamics for sequence estimation. Introducing the bidirectional LSTMs into the encoder-decoder enables the model to capture the long-term dependencies of the measurement sequences from both past and future directions to increase the estimation accuracy. The proposed method is evaluated on public battery datasets under dynamic loading profiles. Validation with an experimental dataset shows that this method of considering the sequential contexts and bidirectional dependencies of battery measurement data can accurately estimate the SOC at different ambient temperatures. In particular, the mean absolute errors are as low as 1.07% at varying temperatures. The proposed method can improve the reliability and availability of battery management systems for monitoring the battery state under varying ambient conditions.

关键词： Lithium-ion battery encoder-decoder Bidirectional long short-term memory State-of-charge sequence estimation

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Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End encoder-decoder Network

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2019年 7卷 1900110-1900110页

作者： Du, Xiuquan Yin, Susu Tang, Renjun Zhang, Yanping Li, Shuo Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China Western Univ Dept Med Imaging London ON N6A 3K7 Canada Digital Imaging Grp London London ON N6A 3K7 Canada

Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper, we propose an improved end-to-end encoder-decoder network for CBV segmentation from the pixel level view (Cardiac-DeepIED). In our framework, we explicitly solve the high variability of complex cardiac structures through an improved encoder-decoder architecture which consists of Fire dilated modules and D-Fire dilated modules. This improved encoder-decoder architecture has the advantages of being capable of obtaining semantic task-aware representation and preserving fine-grained information. In addition, our method can dynamically capture potential spatiotemporal correlations between consecutive cardiac MR images through specially designed convolutional long-term and short-term memory structure;it can simulate spatiotemporal contexts between consecutive frame images. The combination of these modules enables the entire network to get an accurate, robust segmentation result. The proposed method is evaluated on the 145 clinical subjects with leave-one-out cross-validation. The average dice metric (DM) is up to 0.96 (left ventricle), 0.89 (myocardium), and 0.903 (right ventricle). The performance of our method outperforms state-of-the-art methods. These results demonstrate the effectiveness and advantages of our method for CBV regions segmentation at the pixel-level. It also reveals the proposed automated segmentation system can be embedded into the clinical environment to accelerate the quantification of CBV and expanded to volume analysis, regional wall thickness analysis, and three LV dimensions analysis.

关键词： CBV segmentation deep learning encoder-decoder magnetic resonance images

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An encoder-decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

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SENSORS 2023年第13期23卷 6109页

作者： Gomez-Cardenes, Oscar Marichal-Hernandez, Jose Gil Son, Jung-Young Jimenez, Rafael Perez Rodriguez-Ramos, Jose Manuel Univ La Laguna Dept Ind Engn San Cristobal la Laguna 38200 Spain Konyang Univ Biomed Engn Dept Nonsan Si 320711 South Korea Univ Las Palmas Gran Canaria Inst Technol Dev & Innovat Commun Las Palmas Gran Canaria 35017 Spain Wooptix SL Res & Dev Dept San Cristobal la Laguna 38204 Spain

In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder-decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method's processing time is lower than the video acquisition time with a 1024 x 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.

关键词： Radon transform scale-space methods multiscale DRT barcodes encoder-decoder pixelwise segmentation classical signal processing

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A medical report generation method integrating teacher-student model and encoder-decoder network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 94卷

作者： Zhang, Shujun Han, Qi Li, Jinsong Sun, Yukang Qin, Yuhua Qingdao Univ Sci & Technol Sch Data Sci Qingdao 266061 Shandong Peoples R China Qingdao Univ Sci & Technol Coll Informat Sci & Technol Qingdao 266061 Shandong Peoples R China

The automatic medical report generation task can reduce the burden of radiologists and improve the intelligence of auxiliary diagnosis, but still faces the following challenges: (1) The small lesions are easily overlooked, leading to loss of crucial information in the report and low accuracy;(2) The generated long text reports often suffer from jumbled word order and sentence order, resulting in poor fluency. Through simulation of the cognitive principle of professional physicians during their training and work, this paper put forward a medical report generation method integrating a teacher-student model with an encoder-decoder network. The core idea is to propose a cross -modal teacher (text) -student (image) model, adopting different supervision methods for different stages of report generation to improve the model's learning performance. A semantic space alignment mechanism is designed to enhance the cross -modal feature matching ability by contrasting the encoding methods of different modalities through adversarial learning, gradually optimizing and capturing the critical information. A layer-supervised decoder based on the Transformer hierarchical structure is proposed with the teacher model guiding the student model to decode layer by layer to increase the fluency of report generation. Comparative experiments are conducted on IU-X-ray and MIMIC-CXR datasets with various other methods, and the results show that the proposed method can effectively improve the quality of generated reports.

关键词： Medical report generation Teacher-student model encoder-decoder Transformer

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Displacement prediction model for high arch dams using long short-term memory based encoder-decoder with dual-stage attention considering measured dam temperature

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ENGINEERING STRUCTURES 2023年第1期280卷

作者： Huang, Ben Kang, Fei Li, Junjie Wang, Feng Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China

Structural health monitoring method can provide important information to evaluate operational status of con-crete dams, by establishing accurate models to predict concrete dam behavior with monitored data. This study proposed a model using encoder-decoder based on long short-term memory network with dual-stage attention mechanism (DALSTM) to predict the displacement of concrete arch dams. encoder-decoder based on long short -term memory network is a deep learning technique that can perform time series prediction, and dual-stage attention mechanism focuses on the key information in the dam displacement series to improve the perfor-mance. The effectiveness and accuracy of the proposed prediction model are analyzed on a high arch dam using measured temperature in the dam body instead of the seasonal functions to represent the thermal effect. Compared with traditional stepwise regression, multiple linear regression models, radial basis function networks, and other deep learning models, results show that the proposed approach performance is more accurate and robust for dam health monitoring.

关键词： Structural health monitoring Dam behavior prediction High arch dam encoder-decoder Long short -term memory networks Dual-stage attention mechanism

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LSTM enhanced by dual-attention-based encoder-decoder for daily peak load forecasting

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期208卷 1页

作者： Zhu, Kedong Li, Yaping Mao, Wenbo Li, Feng Yan, Jiahao China Elect Power Res Inst Power Automat Dept Nanrui 8 Nanjing 210003 Jiangsu Peoples R China

Daily peak load forecasting is a challenging problem in the filed of electric power load forecasting. Since the nonlinear and dynamic of influence factors and their sequential dependencies are significant for modeling daily peak load, a prediction model based on long short-term memory (LSTM) enhanced by dual-attention-based encoder-decoder is presented. Functioned as the specific encoder and decoder, LSTM is utilized to participate in the nonlinear dynamic temporal modeling. The encoder-decoder is used for information utilization of both the influence factors and daily peak load. Moreover, a dual-attention mechanism, which is inserted into the encoder decoder, is designed to take into account the effects of different influence factors and time nodes on the daily peak load simultaneously. It is benefit for the above mechanism design to analyze the characteristics of daily peak load precisely and to achieve more accurate prediction results. Comprehensive experiments are performed based on a real set of one provincial capital city in eastern China. The case study shows that the proposed methodology provides the most accurate results with an average MAPE 2.07%, an average RMSE 133 MW and an average MAE 326.6 MW.

关键词： Dual-attention mechanism Daily peak load forecasting encoder-decoder Long short-term memory (LSTM)

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Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional encoder-decoder With Spatial Pyramid Pooling

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IEEE ACCESS 2019年 7卷 128774-128786页

作者： Liu, Yaohui Gross, Lutz Li, Zhiqiang Li, Xiaoli Fan, Xiwei Qi, Wenhua China Earthquake Adm Inst Geol Beijing 100029 Peoples R China Univ Queensland Sch Earth & Environm Sci Brisbane Qld 4072 Australia China Earthquake Networks Ctr Beijing 100045 Peoples R China

Automatic extraction of buildings from remote sensing imagery plays a significant role in many applications, such as urban planning and monitoring changes to land cover. Various building segmentation methods have been proposed for visible remote sensing images, especially state-of-the-art methods based on convolutional neural networks (CNNs). However, high-accuracy building segmentation from high-resolution remote sensing imagery is still a challenging task due to the potentially complex texture of buildings in general and image background. Repeated pooling and striding operations used in CNNs reduce feature resolution causing a loss of detailed information. To address this issue, we propose a light-weight deep learning model integrating spatial pyramid pooling with an encoder-decoder structure. The proposed model takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information and of the ability of encoder-decoder networks to restore losses of information. The proposed model is evaluated on two publicly available datasets;the Massachusetts roads and buildings dataset and the INRIA Aerial Image Labeling Dataset. The experimental results on these datasets show qualitative and quantitative improvement against established image segmentation models, including SegNet, FCN, U-Net, Tiramisu, and FRRN. For instance, compared to the standard U-Net, the overall accuracy gain is 1.0% (0.913 vs. 0.904) and 3.6% (0.909 vs. 0.877) with a maximal increase of 3.6% in model-training time on these two datasets. These results demonstrate that the proposed model has the potential to deliver automatic building segmentation from high-resolution remote sensing images at an accuracy that makes it a useful tool for practical application scenarios.

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

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A fully-convolutional residual encoder-decoder neural network to localize breast cancer on histopathology images

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 147卷 105698-105698页

作者： Farajzadeh, Nacer Sadeghzadeh, Nima Hashemzadeh, Mahdi Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz Iran Azarbaijan Shahid Madani Univ Artificial Intelligence & Machine Learning Res Lab Tabriz Iran Azarbaijan Shahid Madani Univ Azarshahr Rd Tabriz *** Iran

Cancer detection in its early stages may allow patients to receive the proper treatment and save lives along with recovering the routine lifestyles. Breast cancer is of the top leading causes of mortality among women all around the globe. A source to find these cancerous nuclei is through analyzing histopathology images. These images, however, are very complex and large. Thus, locating the cancerous nuclei in them is very challenging. Hence, if an expert fails to diagnose their patients via these images, the situation may be exacerbated. Therefore, this study aims to introduce a method to mask as many cancer nuclei on histopathology images as possible with a high visual aesthetic to make them distinguishable by experts easily. A tailored residual fully convolutional encoderdecoder neural network based on end-to-end learning is proposed to issue the matter. The proposed method is evaluated quantitatively and qualitatively on ER + BCa H&E-stained dataset. The average detection accuracy achieved by the method is 98.61%, which is much better than that of competitors.

关键词： Breast cancer nuclei End-to-End learning Fully convolutional neural networks Image masking encoder-decoder Residual networks

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Using Machine Learning to Identify Hydrologic Signatures With an encoder-decoder Framework

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WATER RESOURCES RESEARCH 2023年第3期59卷 e2022WR033091-e2022WR033091页

作者： Botterill, Tom E. E. McMillan, Hilary K. K. San Diego State Univ Dept Geog San Diego CA 92182 USA

Hydrologic signatures are quantitative metrics that describe a streamflow time series. Examples include annual maximum flow, baseflow index and recession shape descriptors. In this paper, we use machine learning (ML) to learn encodings that are optimal ML equivalents of hydrologic signatures, and that are derived directly from the data. We compare the learned signatures to classical signatures, interpret their meaning, and use them to build rainfall-runoff models in otherwise ungauged watersheds. Our model has an encoder-decoder structure. The encoder is a convolutional neural net mapping historical flow and climate data to a low-dimensional vector encoding, analogous to hydrological signatures. The decoder structure includes stores and fluxes similar to a classical hydrologic model. For each timestep, the decoder uses current climate data, watershed attributes and the encoding to predict coefficients that distribute precipitation between stores and store outflow coefficients. The model is trained end-to-end on the U.S. CAMELS watershed data set to minimize streamflow error. We show that learned signatures can extract new information from streamflow series, because using learned signatures as input to the process-informed model improves prediction accuracy over benchmark configurations that use classical signatures or no signatures. We interpret learned signatures by correlation with classical signatures, and by using sensitivity analysis to assess their impact on modeled store dynamics. Learned signatures are spatially correlated and relate to streamflow dynamics including seasonality, high and low extremes, baseflow and recessions. We conclude that process-informed ML models and other applications using hydrologic signatures may benefit from replacing expert-selected signatures with learned signatures.

关键词： hydrologic signature flow metric flow indices machine learning deep learning encoder-decoder

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