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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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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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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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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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Image Caption Method From Coarse to Fine Based On Dual encoder-decoder Framework

Image Caption Method From Coarse to Fine Based On Dual Encod...

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International Joint Conference on Neural Networks (IJCNN)

作者： Li, Zefeng Liu, Yuehu Song, Yonghong Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350359329;9798350359312

encoders are widely used in the field of image caption, but the statements generated by the current image caption method may miss the target and the generated description statements are not appropriate enough for the image content. In order to solve the above problems, we propose a coarse-fine image caption method based on dual encoder-decoder framework, which provides a mechanism for discovering and correcting omissions and enables the model to generate a complete image description. Firstly, an image feature extractor based on global and local information is designed, which can extract global information and local information of image and obtain more abundant image representation. Secondly, a dual encoder-decoder framework is designed, which consists of a coarse-grained encoder-decoder and a fine-grained encoder-decoder. Coarse-grained encoder-decoder requires only the original image features as input, which is processed by transformer to produce a coarse text description. In addition, an image feature auto-enhancement module is proposed to detect missing objects in coarse text and enhance their feature expression. Finally, the fine-grained encoder-decoder uses both the image feature and the coarse text caption as input, and generates the final fine-grained caption after multi-modal information fusion. Experimental results on MSCOCO datasets show that our proposed method outperforms previous image caption methods and achieves a performance of 39.7 BLEU-4 score and 121.6 CIDEr-A score.

关键词： Image caption encoder-decoder structure Feature auto-enhancement

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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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TCNet: tensor and covariance attention network for semantic segmentation

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SOFT COMPUTING 2024年第11-12期28卷 7575页

作者： Xu, Haixia Liu, Yanbang Wang, Wei Zhou, Wei Ding, Fanxun Han, Feng Peng, Wei Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Peoples R China Xiangtan Univ Sch Automat & Elect Informat Xiangtan Peoples R China

Non-local network provides a pioneering approach for capturing long-range dependency by aggregating query-specific global context into each query location;however, non-local network applies the identical weight to each channel of feature maps and ignores the differences from the different channels of features. We design a novel tensor attention module (TAM), which integrates the context information along spatial dimension and channel dimension by introducing a bias learnable parameters tensor, so that the feature at each location of each channel can aggregate the features from all other locations. Motivated by SE-Net, we propose a novel second-order covariance attention module (SCAM) to enhance the feature correlation between different channel maps through the second-order statistics and the local cross-channel interaction strategy. We take the encoder-decoder segmentation network DeepLabv3+ as baseline, and in the encoder develop the attention modules TAM and SCAM for semantic segmentation (TCNet). Experimental results on PASCAL VOC 2012 and Cityscapes datasets show that our proposed network has better performance than the other state-of-the-art segmentation networks.

关键词： Attention module TAM SCAM encoder-decoder structure

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Predicting water quality in municipal water management systems using a hybrid deep learning model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Luo, Wenxian Huang, Leijun Shu, Jiabin Feng, Hailin Guo, Wenjie Xia, Kai Fang, Kai Wang, Wei Zhejiang A&F Univ 666 Wusu St Hangzhou 311300 Zhejiang Peoples R China Quzhou Digital Rural Construct Ctr 139 Fushi Rd Quzhou 324000 Zhejiang Peoples R China Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Guangdong Hong Kong Macao Joint Lab Emot Intellige Shenzhen 518172 Guangdong Peoples R China

Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non -stationary characteristics of water quality data of the municipal water resources, it is challenging to achieve high prediction accuracy, especially for medium -term and long-term predictions. To address this issue, we propose a novel hybrid deep learning model to predict water quality multiple steps ahead. The proposed model adopts the encoder-decoder structure in the form of two long short-term memory (LSTM) networks, integrated with the attention mechanism and a convolutional neural network (CNN). The model extracts the complex correlation between multiple water quality features through the CNN, and uses the two LSTM networks to transfer historical information to predictions, with an attention layer assigning different weights to the different parts of the historical information. Using three years of water quality data collected from an urban river, we experimentally show that the proposed model outperforms the baseline models by 11%-34% in root mean squared error (RMSE) when predicting dissolved oxygen multiple steps ahead, and by 1%-7% when predicting total phosphorus. Similar improvement has also been found in Nash-Sutcliffeefficiency (NSE) and mean absolute error (MAE). The proposed model is a feasible solution for multi -step medium -term water quality prediction.

关键词： Water quality Multi-step prediction encoder-decoder structure Long short-term memory Convolutional neural network Attention mechanism

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