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River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection

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EARTH SCIENCE INFORMATICS 2024年第6期17卷 5233-5249页

作者： Jeba, G. Selva Chitra, P. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai India Thiagarajar Coll Engn Dept Comp Applicat Madurai India

Floods, among the most destructive climate-induced natural disasters, necessitate effective prediction models for early warning systems. The proposed Multi-Attention encoder-decoder-based Temporal Convolutional Network (MA-TCN-ED) prediction model combines the strengths of the Temporal Convolutional Network (TCN), Multi-Attention (MA) mechanism, and encoder-decoder (ED) architecture along with filter-wrapper feature selection for optimal feature selection. This framework aims to improve flood prediction accuracy by effectively capturing temporal dependencies and intricate patterns in atmospheric and hydro-meteorological data. The proposed framework was pervasively assessed for predicting the real-world flood-related data of the river Meenachil, Kerala, and the results showed that MA-TCN-ED using a filter-wrapper feature selection approach achieved higher accuracy in flood prediction. Further the model was validated on the dataset of river Pamba, Kerala. The proposed model exhibits better performance with about 32% reduced MAE, 39% reduced RMSE, 12% increased NSE, 14% enhanced R-2, and 17% enhanced accuracy relative to the average performance of all the compared baseline models. The proposed work holds promise for enhancing early warning systems and mitigating the impact of floods and contributes to the broader understanding of leveraging deep learning models for effective climate-related risk mitigation.

关键词： Environmental modeling Deep learning Flood prediction Attention mechanism encoder-decoder Feature selection

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Estimation of rolling bearing remaining useful life using DReLU-RA-ConvGRU model based on the encoder-decoder structure

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 036001-036001页

作者： Wu, Qile Xiao, Ke Xiong, Wanquan Han, Yanfeng Song, Bin Chongqing Univ State Key Lab Mech Transmiss Adv Equipments Chongqing 400044 Peoples R China ROKAE Beijing Technol Co Ltd Beijing 100080 Peoples R China

Rolling bearings serve as indispensable core components in modern industrial equipment and they are critical for safety and reliability. Consequently, accurate prediction of their remaining useful life (RUL) is essential and has far-reaching implications. This paper proposes a novel RUL prediction model, referred to as the dynamic rectified linear unit-based residual additive attention ConvGRU (DReLU-RA-ConvGRU) model, which is built upon the encoder-decoder structure to accurately predict the RUL of bearings. To overcome the limitation of the original signal, characterized by a single feature and limited degradation information, three-domain features are employed and filtered as inputs to the model. The DReLU component in the proposed RUL prediction model effectively captures variable feature information within the degraded signal, while the ConvGRU component learns both temporal and spatial information with fewer parameters. Additionally, the RA component captures the significant contributors to RUL prediction, and the inclusion of residuals facilitates easier network learning. Furthermore, a three-dimensional visualization of the attention weights was conducted to enhance the interpretability of the network's prediction process. In order to verify the effectiveness of the method, RUL prediction was conducted using vibration data from the PRONOSTIA platform and compared against several existing methods. The results demonstrate the method's superior performance and feasibility, as indicated by high scores.

关键词： rolling bearing encoder-decoder dynamic ReLU (DReLU) residual additive attention (RA) RUL prediction three-domain features

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Automated tongue segmentation using deep encoder-decoder model

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第24期82卷 37661页

作者： Kusakunniran, Worapan Borwarnginn, Punyanuch Imaromkul, Thanandon Aukkapinyo, Kittinun Thongkanchorn, Kittikhun Wattanadhirach, Disathon Mongkolluksamee, Sophon Thammasudjarit, Ratchainant Ritthipravat, Panrasee Tuakta, Pimchanok Benjapornlert, Paitoon Mahidol Univ Fac Informat & Commun Technol Nakhon Pathom Thailand Srinakharinwirot Univ Fac Sci Dept Comp Sci Bangkok Thailand Mahidol Univ Fac Med Ramathibodi Hosp Dept Epidemiol & Biostat Bangkok Thailand Mahidol Univ Fac Engn Dept Biomed Engn Nakhon Pathom Thailand Mahidol Univ Fac Med Ramathibodi Hosp Dept Rehabil Med Bangkok Thailand

This paper proposes a solution of tongue segmentation in images. The solution relies on a convolutional neural network, using deep U-Net with deep layers of encoder-decoder modules. The model is trained with a starting resolution of 512 x 512 pixels. To enhance the segmentation performances of the trained model across recording environments, three main types of data augmentations are added in the training process, including additive gaussian noise, multiply and add to brightness, and change color temperature. They could also handle an inadequate number of data samples in the limited datasets. The proposed method is evaluated based on four measurement metrics of Dice coefficient, mean IoU, Jaccard distance, and accuracy. The model is successfully trained on publicly available datasets, and then transferred to be tested with the self-collected dataset in the real-world environment.

关键词： Tongue segmentation Deep U-Net encoder-decoder

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Longs short-term based semi-supervised memory encoder-decoder for early prediction of failures in self-lubricating bearings

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Friction 2023年第1期11卷 109-124页

作者： Vigneashwara PANDIYAN Mehdi AKEDDAR Josef PROST Georg VORLAUFER Markus VARGA Kilian WASMER Laboratory for Advanced Materials Processing(LAMP) Swiss Federal Laboratories for Materials Science and Technology(Empa)Thun CH-3602Switzerland AC2T research GmbH Wiener Neustadt 2700Austria

The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service *** a result,self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial ***,wear mechanisms are still inevitable and occur progressively in self-lubricating bearings,as characterized by the loss of the lubrication film and ***,monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance *** article proposes a methodology for using a long short-term memory(LSTM)-based encoder-decoder architecture on interfacial force signatures to detect abnormal regimes,aiming to provide early predictions of failure in self-lubricating sliding contacts even before they *** sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer *** force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder-decoder architecture,so as to reconstruct any new signal of the normal regime with the minimum *** this semi-supervised training exercise,the force signatures corresponding to the abnormal regime could be differentiated from the normal regime,as their reconstruction errors would be very *** the validation procedure for the proposed LSTM-based encoder-decoder model,the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%.In addition,a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point,making it possible to be used for early predic

关键词： predictive maintenance in-situ sensing long short-term memory(LSTM) encoder-decoder wear monitoring tribology

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A Highly Robust encoder-decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images

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JOURNAL OF IMAGING 2025年第2期11卷 51-51页

作者： Tripathi, Milan Kongprawechnon, Waree Kondo, Toshiaki Thammasat Univ Sirindhorn Int Inst Technol Sch Informat Comp & Commun Technol Pathum Thani 12120 Thailand

Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder-decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images. HREDN integrates a multi-scale feature enhancement block in the encoder, allowing the network to capture features at various scales and handle complex noise patterns more effectively. To mitigate information loss during encoding, skip connections transfer essential feature maps from the encoder to the decoder, preserving structural details. However, skip connections can also propagate redundant information. To address this, we incorporate attention gates within the skip connections, ensuring that only relevant features are passed to the decoding layers. We evaluate the robustness of the proposed method across facial, medical, and remote sensing domains. The experimental results demonstrate that HREDN excels in preserving edge details and structural features in denoised images, outperforming state-of-the-art techniques in both qualitative and quantitative measures. Statistical analysis further highlights the model's ability to effectively remove noise in diverse, complex scenarios with images of varying resolutions across multiple domains.

关键词： image denoising Gaussian salt-and-pepper encoder-decoder multi-scale feature enhancement

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AdapNet: Adaptability Decomposing encoder-decoder Network for Weakly Supervised Action Recognition and Localization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 1852-1863页

作者： Zhang, Xiao-Yu Li, Changsheng Shi, Haichao Zhu, Xiaobin Li, Peng Dong, Jing Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao Peoples R China Chinese Acad Sci Inst Automation Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China

The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance. As a challenging problem for high-level video understanding, weakly supervised action recognition and localization in untrimmed videos have attracted intensive research attention. Knowledge transfer by leveraging the publicly available trimmed videos as external guidance is a promising attempt to make up for the coarse-grained video-level annotation and improve the generalization performance. However, unconstrained knowledge transfer may bring about irrelevant noise and jeopardize the learning model. This article proposes a novel adaptability decomposing encoder-decoder network to transfer reliable knowledge between the trimmed and untrimmed videos for action recognition and localization by bidirectional point process modeling, given only video-level annotations. By decomposing the original features into the domain-adaptable and domain-specific ones based on their adaptability, trimmed-untrimmed knowledge transfer can be safely confined within a more coherent subspace. An encoder-decoder-based structure is carefully designed and jointly optimized to facilitate effective action classification and temporal localization. Extensive experiments are conducted on two benchmark data sets (i.e., THUMOS14 and ActivityNet1.3), and the experimental results clearly corroborate the efficacy of our method.

关键词： Videos Feature extraction Knowledge transfer Task analysis Adaptation models Data models Neural networks Action recognition encoder-decoder knowledge transfer point process temporal action localization

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An encoder-decoder model with embedded attention-mechanism for efficient meshfree prediction of slope failure

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2023年第10期32卷 1164-1187页

作者： Chen, Jun Wang, Dongdong Deng, Like Ying, Jijun Xiamen Univ Inst Artificial Intelligence Dept Civil Engn Fujian Key Lab Digital Simulat Coastal Civil Engn Xiamen Peoples R China Xiamen Univ Dept Civil Engn Xiamen 361005 Fujian Peoples R China

The particle-based meshfree methods provide an effective means for large deformation simulation of the slope failure. Despite the advances of various efficient meshfree algorithmic developments, the computational efficiency still limits the application of meshfree methods for practical problems. This study aims at accelerating the meshfree prediction of the slope failure through introducing an encoder-decoder model, which is particularly enhanced by the attention-mechanism. The encoder-decoder model is designed to capture the long sequence character of meshfree slope failure analysis. The discretization flexibility of meshfree methods offers an easy match between the meshfree particles and machine learning samples and thus the resulting surrogate model for meshfree slope failure prediction has a quite wide applicability. In the meantime, the embedding of the attention-mechanism into the encoder-decoder neural network not only enables a significant reduction of the number of meshfree model parameters, but also maintains the key features of meshfree simulation and effectively alleviates the information dilution issue. It is shown that the proposed encoder-decoder model with embedded attention mechanism gives a more favorable prediction on the meshfree slope failure simulation in comparison to the general encoder-decoder formalism.

关键词： Meshfree method large deformation slope failure encoder-decoder attention mechanism

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Dual attention guided deep encoder-decoder network for change analysis in land use/land cover for Dakshina Kannada District, Karnataka, India

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ENVIRONMENTAL EARTH SCIENCES 2023年第1期82卷 1-21页

作者： Naik, Nitesh Chandrasekaran, Kandasamy Sundaram, Venkatesan Meenakshi Panneer, Prabhavathy Natl Inst Technol Dept Comp Sci & Engn Mangalore 575025 Karnataka India Natl Inst Technol Dept Comp Sci & Engn Karaikal 609609 Puducherry India Vellore Insitute Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India

The Earth is frequently changed by natural occurrences and human actions that have threatened our environment to a certain extent. Therefore, accurate and timely monitoring of transformations at the surface of the Earth is crucial for precisely facing their harmful effects and consequences. This paper aims to perform a change detection (CD) analysis and assessment of the Dakshina Kannada region, being one of the coastal districts of Karnataka, India. The spatial and temporal variations in land use and land cover (LULC) are being monitored and examined from the data received as LULC maps from the National Remote Sensing Agency, Indian Space Research Organization, India. The time-series data from advanced wide-field sensor (AWiFS) Resourcesat2 satellite as LULC maps (1:250k) are analyzed using a deep learning approach with an encoder-decoder architecture with dual-attention modules for the change analysis. The model provides an overall accuracy and meanIOU(intersection over union) of 94.11% and 74.1%. The LULC maps from 2005 to 2018 (13 years) are utilized to decide the variations in the LULC, including urban development, agricultural variations, vegetation dynamics, forest areas, barren land, littoral swamp, and water bodies, current fallow, etc. The multiclass area-wise changes in terms of percentage show a decline in most LULC classes, which raises a point of concern for the environmental safety of the considered area, which is highly exposed to coastal flooding due to increased urbanization.

关键词： Land use and land cover encoder-decoder Deep learning Dual-attention

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Multi-dimensional feature extraction-based deep encoder-decoder network for automatic surface defect detection

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NEURAL COMPUTING & APPLICATIONS 2023年第4期35卷 3263-3282页

作者： Uzen, Huseyin Turkoglu, Muammer Hanbay, Davut Bingol Univ Engn Fac Comp Engn Dept Bingol Turkey Samsun Univ Engn Fac Software Engn Dept Samsun Turkey Inonu Univ Engn Fac Comp Engn Dept Malatya Turkey

The control of surface defects is of critical importance in manufacturing quality control systems. Today, automatic defects detection using imaging and deep learning algorithms has produced more successful results than manual inspections. Thanks to these automatic applications, manufacturing systems will increase the production quality, and thus financial losses will be prevented. However, since the appearance and dimensions of the defects on the surface are very variable, automatic surface defect detection is a complex problem. In this study, multi-dimensional feature extraction-based deep encoder-decoder network (MFE-DEDNet) network developed to solve such problems. An effective encoder-decoder model with lower parameters compared to the state-of-the-art methods is developed using the depthwise separable convolutions (DSC) layers in the proposed model. In addition, the 3D spectral and spatial features extract (3DFE) module of the proposed model is developed to extract deep spectral and spatial features, as well as deep semantic features. During the combination of these features, the multi-input attention gate (MIAG) module is used so that important details are not lost. As a result, the proposed MFE-DEDNet model based on these structures enabled the extraction of powerful and effective features for defect detection in surface datasets containing few images. In experimental studies, MVTec and MT datasets were used to evaluate the performance of the MFE-DEDNet. The experimental results achieved 80.01% and 56% mean intersection-over-union (mIoU) scores for the MT and MVTec datasets, respectively. In these results, it was observed that the proposed model produced higher success compared to other state-of-the-art methods.

关键词： Surface defect detection Deep learning 3D spectral and spatial features extract encoder-decoder

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STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT

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MEDICAL PHYSICS 2023年第7期50卷 4443-4458页

作者： Zhu, Linlin Han, Yu Xi, Xiaoqi Fu, Huijuan Tan, Siyu Liu, Mengnan Yang, Shuangzhan Liu, Chang Li, Lei Yan, Bin PLA Strateg Support Force Informat Engn Univ Henan Key Lab Imaging & Intelligent Proc Zhengzhou Peoples R China Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou Peoples R China PLA Strateg Support Force Informat Engn Univ Henan Key Lab Imaging & Intelligent Proc Zhengzhou 450001 Peoples R China

BackgroundLow-dose computed tomography (LDCT) can reduce the dose of X-ray radiation, making it increasingly significant for routine clinical diagnosis and treatment planning. However, the noise introduced by low-dose X-ray exposure degrades the quality of CT images, affecting the accuracy of clinical *** noises, artifacts, and high-frequency components are similarly distributed in LDCT images. Transformer can capture global context information in an attentional manner to create distant dependencies on targets and extract more powerful features. In this paper, we reduce the impact of image errors on the ability to retain detailed information and improve the noise suppression performance by fully mining the distribution characteristics of image information. Purpose MethodsThis paper proposed an LDCT noise and artifact suppressing network based on Swin Transformer. The network includes a noise extraction sub-network and a noise removal sub-network. The noise extraction and removal capability are improved using a coarse extraction network of high-frequency features based on full convolution. The noise removal sub-network improves the network's ability to extract relevant image features by using a Swin Transformer with a shift window as an encoder-decoder and skip connections for global feature fusion. Also, the perceptual field is extended by extracting multi-scale features of the images to recover the spatial resolution of the feature maps. The network uses a loss constraint with a combination of L1 and MS-SSIM to improve and ensure the stability and denoising effect of the network. ResultsThe denoising ability and clinical applicability of the methods were tested using clinical datasets. Compared with DnCNN, RED-CNN, CBDNet and TSCN, the STEDNet method shows a better denoising effect on RMSE and PSNR. The STEDNet method effectively removes image noise and preserves the image structure to the maximum extent, making the reconstructed image closest to the NDCT

关键词： deep learning encoder-decoder low-dose CT Swin transformer

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