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ECG_SegNet: An ECG delineation model based on the encoder-decoder structure

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 145卷 105445-105445页

作者： Liang, Xiaohong Li, Liping Liu, Yuanyuan Chen, Dan Wang, Xinpei Hu, Shunbo Wang, Jikuo Zhang, Huan Sun, Chengfa Liu, Changchun Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Shandong Univ Tradit Chinese Med Coll Intelligence & Informat Engn Jinan 250355 Shandong Peoples R China Shandong First Med Univ Shandong Prov Hosp Dept Cardiol Electrocardiogram Room Jinan 250021 Shandong Peoples R China Linyi Univ Sch Informat Sci & Engn Linyi 276005 Shandong Peoples R China

With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is necessary to develop models and algorithms that can analyze the large amounts of ECG data obtained in real-time. Accurate ECG delineation is key to assisting cardiologists in diagnosing cardiac diseases. The main objective of this study is to design a delineation model based on the encoder-decoder structure to detect different heartbeat waveforms, including P-waves, QRS complexes, T-waves, and No waves (NW), as well as the onset and offset of these waveforms. First, the introduction of a standard dilated convolution module (SDCM) into the encoder path enabled the model to extract more useful ECG signal-informative features. Subsequently, bidirectional long shortterm memory (BiLSTM) was added to the encoding structure to obtain numerous temporal features. Moreover, the feature sets of the ECG signals at each level in the encoder path were connected to the decoder part for multi scale decoding to mitigate the information loss caused by the pooling operation in the encoding process. Finally, the proposed model was trained and tested on both QT and LU databases, and it achieved accurate results compared to other state-of-the-art methods. Regarding the QT database, the average accuracy of ECG waveform classification was 96.90%, and an average classification accuracy of 95.40% was obtained on the LU database. In addition, average F1 values of 99.58% and 97.05% were achieved in the ECG delineation task of the QT and LU databases, respectively. The results show that the proposed ECG_SegNet model has good flexibility and reliability when applied to ECG delineation, and it is a reliable method for analyzing ECG signals in real-time.

关键词： Electrocardiogram (ECG) encoder-decoder structure ECG delineation Bidirectional long short-term memory (BiLSTM)

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Empowering Regional Rainfall-Runoff Modeling Through encoder-decoder Based on Convolutional Neural Networks

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WATER 2025年第3期17卷 339-339页

作者： Jiang, Wei Dang, Xupeng Zhang, Rui North China Univ Water Resources & Elect Power Sch Informat Engn Zhengzhou 450046 Peoples R China

Regional rainfall-runoff modeling is a classic and significant research topic in hydrological sciences. Currently, the predominant modeling approach is developing data-driven models. This study proposes a rainfall-runoff model named ED-TimesNet (encoder-decoder-based TimesNet), which consists of convolutional neural networks. It transforms a one-dimensional time series into a two-dimensional matrix based on frequency-domain partitioning rules and subsequently employs a two-dimensional visual backbone to learn both local and global features of the hydrological time series. Compared to LSTM-based models and Transformer models, this model learns both intra-period and inter-period variations in hydrological series, simultaneously focusing on the relationships between adjacent and non-adjacent time points. It alleviates the temporal ambiguity problem inherent in attention mechanisms. This research validates the performance of the ED-TimesNet model in regional rainfall-runoff modeling tasks using the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset. The model achieves a median and mean NSE of 0.8049 and 0.7808, respectively, across 448 basins, outperforming the benchmark LSTM, VIC, and mHM models, and achieving comparable performance to the Transformer model. This paper does not address the model's performance on ungauged basins. The method of predicting runoff based on the periodic features of hydrological data provides a novel perspective for hydrological sciences.

关键词： regional rainfall-runoff modeling convolutional neural networks encoder-decoder structure two-dimensional variation

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A Joint Trajectory Completion Method Combined Spatiotemporal Motion for Long-Span Bridge Surveillance System With Dead Zone

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Guo, Xiaoli Zhang, Haodu Liu, Wanquan Zhang, Rui Wang, Ping Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 510275 Peoples R China Jinan Special Equipment Inspect & Res Inst Jinan 250014 Peoples R China

Current surveillance systems used on long-span bridges have monitoring dead zones, resulting in incomplete vehicle trajectories, whereas abnormal vehicle trajectories occurring within these dead zones are highly prone to traffic accidents. Obtaining complete vehicle trajectories within dead zones is challenging, yet crucial for identifying abnormal trajectories. To tackle this challenge, this article presents a joint trajectory completion (JTC) framework, which enables the completion of vehicle trajectories in dead zones with similar vehicle appearances and diverse camera views. An appearance-spatiotemporal fusion (ASTF) module is utilized to integrate motion spatiotemporal and appearance information to enhance vehicle representations. A Transformer-based bi-directional trajectory completion (TBTC) module with two encoder-decoder structures is further constructed to complete trajectories. Finally, a mathematical assessment strategy is established according to traffic rules to identify abnormal trajectories. Sufficient experimental results on the Hangzhou Bay dataset demonstrate the effectiveness of the proposed JTC method, which achieves an improvement in mAP of 7.4% for vehicle re-identification (ReID) and reduces the average RMSE by at least 0.2123 m for completion of vehicle trajectories. The completion of trajectories within dead zones facilitates the subsequent effective identification of abnormal trajectories and rapid accident warnings.

关键词： Trajectory Cameras Surveillance Bridges Feature extraction Roads Risk management Target tracking Videos Accidents encoder-decoder structure motion spatiotemporal modeling (MSTM) surveillance system vehicle re-identification (ReID)

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I Know How You Move: Explicit Motion Estimation for Human Action Recognition

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1665-1676页

作者： Shen, Zhongwei Wu, Xiao-Jun Li, Hui Xu, Tianyang Wu, Cong Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England

Enabled by hierarchical convolutions and nonlinear mappings, recent action recognition studies have continuously boosted performance with spatiotemporal modelling. In general, motion clues are essential in video-oriented tasks, while existing approaches aggregate the spatial and temporal signatures via specially designed modules in the middle or output stages. To highlight the privilege provided by temporal motions, in this paper, we propose a simple but effective MOTion Estimator (MOTE) to generate the motion patterns from every single frame, avoiding complex dense-frame input. In particular, MOTE follows an encoder-decoder structure, which takes the short-term motion features generated by the pretrained dense-frame network as the learning target. The spatial information of a single frame is utilized to estimate the instantaneous motion appearance. It can support the expression of vulnerable regions, such as the 'hand' in 'waving hands,' which would otherwise be suppressed in the feature maps as the 'hand' suffers from motion blur. The training process of MOTE is independent of the action recognition system. Therefore, the trained MOTE can be transplanted to the input-end of existing action recognition methods to provide instantaneous motion estimation as feature enhancement according to practical requirements. Our experiments performed on Something-Something V1, V2, Kinetics-400, and Diving48 verify the effectiveness of the proposed method.

关键词： Training Feature extraction Computational modeling Three-dimensional displays Spatiotemporal phenomena Costs Convolutional neural networks Short-term motion motion estimation encoder-decoder structure action recognition

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Research on Building Energy Consumption Prediction Based on Hybrid GRU Neural Network

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-15页

作者： Gao, Zhiyuan Zhang, Xuewei Wang, Changsheng Xing, Jianchun Deng, Zhongkai Chen, Tao Army Engn Univ PLA Natl Def Engn Coll Nanjing Peoples R China Natl Def Mobilizat Off Command Informat Assurance Zhangjiakou Peoples R China

The accurate prediction of building energy consumption provides technology and data support for the construction of intelligent building energy systems. Moreover, it is also a crucial means of responding to the national "Carbon Peaking and Carbon Neutrality Goals." Traditional methods can yield poor results because they fail to consider the nonlinear, nonstationary, and multi-seasonal characteristics of the building energy consumption data. To overcome these limitations, this paper proposes an asymmetric energy consumption prediction approach based on the encoder-decoder architecture. The proposed approach employs the CEEMDAN algorithm for data preprocessing to enhance the reliability of building energy consumption data. Subsequently, the convolutional gated recurrent unit (Conv-GRU) model is utilized to extract high-dimensional features and capture nonlinear relationships from the input energy consumption data. Finally, by employing the GRU-Attention algorithm to assign feature weights, this approach enhances the accuracy of building energy consumption prediction. Experimental evaluations conducted on real datasets demonstrate the superiority of the proposed approach over the existing classic methods.

关键词： Building energy consumption prediction Deep learning encoder-decoder structure Attention mechanism Building energy analysis

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Pixel-level crack segmentation of tunnel lining segments based on an encoder–decoder network

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Frontiers of Structural and Civil Engineering 2024年第5期18卷 681-698页

作者： Shaokang HOU Zhigang OU Yuequn HUANG Yaoru LIU State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing 100084China China Renewable Energy Engineering Institute Beijing 100120China State Key Laboratory of Stimulation and Regulation of Water Cycles in River Basins China Institute of Water Resources and Hydropower ResearchBeijing 100038China Hunan Provincial Water Resources Development&Investment Co. Ltd.Changsha 410007China

Regular detection and repair for lining cracks are necessary to guarantee the safety and stability of *** development of computer vision has greatly promoted structural health *** study proposes a novel encoder–decoder structure,CrackRecNet,for semantic segmentation of lining segment cracks by integrating improved VGG-19 into the U-Net *** image acquisition equipment is designed based on a camera,3-dimensional printing(3DP)bracket and two laser rangefinders.A tunnel concrete structure crack(TCSC)image data set,containing images collected from a double-shield tunnel boring machines(TBM)tunnel in China,was *** data preprocessing operations,such as brightness adjustment,pixel resolution adjustment,flipping,splitting and annotation,2880 image samples with pixel resolution of 448×448 were *** model was implemented by Pytorch in PyCharm processed with 4 NVIDIA TITAN V *** the experiments,the proposed CrackRecNet showed better prediction performance than U-Net,TernausNet,and *** paper also discusses GPU parallel acceleration effect and the crack maximum width quantification.

关键词： tunnel lining segment crack detection semantic segmentation convolutional neural network encoder-decoder structure

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JAED-Net: joint attention encoder-decoder network for road extraction from remote sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2023年第2期17卷

作者： Qi, Ranran Tuerxun, Palidan Qian, Yurong Tang, Bochuan Yang, Guangqi Wan, Yaling Liu, Hui Xinjiang Univ Sch Software Urumqi Peoples R China Key Lab Signal Detect & Proc Xinjiang Uygur Autono Urumqi Peoples R China Xinjiang Univ Key Lab Software Engn Urumqi Peoples R China Xinjiang Normal Univ Urumqi Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi Peoples R China

In road extraction from remote sensing images, the road environment is complex and blocked by trees, buildings, and other objects, making it impossible to extract practical (continuous and complete) road information. We propose a joint attention encoder-decoder network (JAED-Net) for road extraction from remote sensing images to solve these problems. First, JAED-Net encodes a modified residual network as the backbone for road feature extraction. A joint attention module is added to the encoder to enhance the network's ability to learn and express road features. Then, strip convolution is added to the decoder, so the network retains more spatial features, such as the width and connectivity of roads during upsampling. Finally, a hybrid weighted loss function is introduced to train the network and ensure stability because of the unbalanced ratio of road and background pixels in remote sensing images. Experimental validation of the proposed network is performed on three publicly available datasets.

关键词： remote sensing image road extraction joint attention encoder-decoder structure

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A&I-ED-TSP: Association and Integration encoder-decoder for Traveling Shortest Path Planning

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IEEE ACCESS 2024年 12卷 129601-129610页

作者： Hu, Bin Zhang, Renjun Hebei Petr Univ Technol Sci & Technol Dept Chengde 067000 Peoples R China Hebei Petr Univ Technol Dept Foreign Languages & Tourism Chengde 067000 Peoples R China

Traveling shortest path planning, encompassing the Traveling Salesman Problem (TSP) in graph theory, holds profound significance. The motivation for addressing the TSP stems from its critical application in real-world scenarios, such as logistics, where optimal routing can substantially reduce costs and improve service efficiency. Furthermore, TSP-like challenges play a pivotal role in assisting travelers to chart the optimal itinerary, encompassing all landmarks in the least distance or time and concluding at the departure site. This optimization not only streamlines travel routes but also economizes time and energy, ensuring maximal sightseeing within a confined timeframe. Recognizing the limitations of current solutions in achieving high efficiency and accuracy simultaneously, we propose an innovative Association & Integration-based encoder-decoder structure tailored for solving the Traveling Salesman Problem, i.e., A&*** proposed structure comprises four blocks: the information linkage space, dual-path integration encoder, node encoder, and representation decoder. Specifically, the information linkage space constructs associations among hidden information between input sequence samples. The dual-path integration encoder extracts and merges the original representations of the sequence with associated representations. The node encoder extracts current sequence representations, while the representation decoder block computes the probability distribution of sequence samples, completing the combinatorial optimization of the entire sequence. In the experimental evaluation, we utilized three different metrics: Average Tour Length (ATL), Optimality Gap (OG), and Evaluation Time (ET). We compared the proposed method with classical approximation methods and various state-of-the-art deep learning approaches. The experimental results show that our A&I-ED-TSP structure achieved the best ATLs of 5.704, 12.770, 17.981, 21.979, and 25.293 for TSP instances of TSP50, TS

关键词： Heuristic algorithms Decoding Optimization Approximation algorithms Couplings Transformers Encoding Deep learning Traveling salesman problems Path planning TSP encoder-decoder structure deep learning travel planning

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A Dual-encoder-Single-decoder Based Low-Dose CT Denoising Network

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第7期26卷 3251-3260页

作者： Han, Zefang Shangguan, Hong Zhang, Xiong Zhang, Pengcheng Cui, Xueying Ren, Huiying Taiyuan Univ Sci & Technol Shanxi Prov China Taiyuan 030024 Peoples R China North Univ China Shanxi Prov China Taiyuan 030051 Peoples R China

Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity;Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator;Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://***/hanzefang/DESDGAN.

关键词： Feature extraction Noise reduction Computed tomography Image edge detection Bioinformatics Testing X-ray imaging encoder-decoder structure Generative adversarial network hierarchical-split ResNet image denoising LDCT

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Attention Aggregation encoder-decoder Network Framework for Stereo Matching

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 760-764页

作者： Zhang, Yaru Li, Yaqian Kong, Yating Liu, Bin Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

In the stereo matching networks based on deep learning, current cost aggregation networks lack the means to aggregate cost volume to the utmost extent. Therefore, different from the standard encoder-decoder structures, we propose an attention aggregation encoder-decoder network framework for stereo matching that contains three modules. Specifically, we design a sub-branch and cross-stage aggregation encoding module, which aggregate context information of different sub-branches and cross-stages to achieve the mutual utilization of different deep cost volumes. Meanwhile, we introduce a three-dimensional attention recoding module to obtain the robust discriminative cost volume through recalibrating the high-level semantic information of the sub-branches. In addition, we construct a stepwise aggregation decoding module to decode the cost volume via the stepwise fusion upsampling strategy, which further enhances the learning ability of the network model. The experimental results on Scene Flow and KITTI benchmark datasets show that the proposed network framework is superior to other similar methods in aggregating information.

关键词： Three-dimensional displays Encoding Training Semantics Decoding Feature extraction Convolution Attention mechanism deep learning encoder-decoder structure stereo matching

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