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A Symmetry-Aware Pitch Feature encoder-decoder network for Condition Monitoring of Pitch Bearings in Wind Turbines

IEEE SENSORS JOURNAL 2025年第8期25卷 14379-14392页

作者： Jiang, Guoqian Zhao, Kai Bai, Jiarong Yue, Jian Ding, Xuejuan Xie, Ping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Beijing Goldwind Smart Energy Technol Co Ltd Beijing 100176 Peoples R China

Pitch bearing is one of the critical components of the electric pitch system in wind turbines (WTs), and its early and reliable fault warning is of great significance to ensure the operational reliability and safety of the entire system. In this article, we develop a condition monitoring system for pitch bearing with available supervisory control data acquisition (SCADA) data. Motivated by the three-blade symmetrical structure of WTs, we propose a new symmetry-aware pitch feature encoder-decoder network named PitchNet. A group feature encoding strategy is first designed to extract intragroup variable-wise feature information. Then, a feature attention mechanism is applied to discover important intergroup features by dynamically assigning different weights to learned pitch representations. Finally, a feature decoder is used to reconstruct the pitch input features and derive the reconstruction residuals for early fault detection. The proposed PitchNet is evaluated using real cases of pitch-bearing cracks. Results have demonstrated that the proposed PitchNet presents better early fault warning ability and lower false alarm rates (FARs), which can provide promising and reliable monitoring results for wind farm operators.

关键词： Feature extraction Sensors Motors Inverters Wind speed Reliability Mathematical models Wind turbines Condition monitoring Blades encoder-decoder network feature attention group feature learning supervisory control data acquisition (SCADA) data wind turbine (WT) pitch bearing

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Laser line scanner based real-time geometry monitoring using encoder-decoder network during Directed Energy deposition

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MEASUREMENT 2025年 239卷

作者： Yang, Liu Wang, Boyu Liu, Peipei Jeon, Ikgeun Chen, Zhenyi Li, Mingkai Xiong, Yilei Cheng, Jack C. P. Sohn, Hoon Korea Adv Inst Sci & Technol KAIST Dept Civil & Environm Engn Daejeon 34141 South Korea Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Clear Water Bay Hong Kong Peoples R China Korea Adv Inst Sci & Technol KAIST Ctr 3D Printing Nondestruct Testing Daejeon 34141 South Korea Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China Korea Inst Machinery & Mat Dept Laser & Electron Beam Technol Daejeon 34103 South Korea

Directed Energy Deposition (DED) is a metal additive manufacturing technology that is gaining popularity for its ability to rapidly manufacture virtually any metal components no matter how complex the shapes and properties are. However, the current lack of real-time geometry monitoring and control is hindering the wider dissemination of DED in industries. This study developed and validated a geometry monitoring methodology which can achieve real-time inspection of the melt pool and newly solidified layer, and layer-wise inspection of the deposited layer during DED process. An encoder-decoder network was developed and applied to the profile images from the laser line scanner to obtain track profiles. A point cloud generation method was proposed to convert the obtained track profiles into 3D point cloud data using intrinsic/extrinsic calibration and printing position. Experiments have been successfully conducted to validate the proposed methodology by depositing multi-layer X-shape objects.

关键词： Additive manufacturing Directed energy deposition encoder-decoder network Real-time geometry monitoring Laser line scanning

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encoder-decoder network with Depthwise Atrous Spatial Pyramid Pooling for Automatic Brain Tumor Segmentation

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NEURAL PROCESSING LETTERS 2023年第2期55卷 1697-1713页

作者： AboElenein, Nagwa M. Piao, Songhao Zhang, Zhehong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Menoufia Univ Fac Comp & Informatd Shibin Al Kawm 32511 Egypt

The accurate automated brain tumor segmentation is important for disease analysis and control and increases the likelihood of survival. However, it faces significant challenges due to the low contrast of the tissue boundary and the small size of the tumor. Convolutional Neural networks are a common automated image evaluation technique that has greatly improved current state-of-the-art precision in the task of segmenting brain tumors. This paper presents an advanced encoder-decoder algorithm with Depthwise Atrous Spatial Pyramid Pooling network (EDD-Net). Firstly, Dilated-ResNet block with Squeeze-and Excitation is introduced in the encoder and decoder module to derive image features adaptively and focuses on the relevant characteristics of the brain segmentation task with fewer parameters. Then, the Depthwise atrous Spatial Pyramid Pooling(DSPP) technique is used as the transition and output layers of the network, to achieve the multi-scale extraction of the feature image and preserves more spatial information. Furthermore, to speed up learning we propose a down-sampling module, while the up-sampling module can more efficiently aggregate low- and high-level feature information. The proposed method is evaluated on the Brats 19 dataset. Experiments demonstrate that the EDD-Net provides high accuracy and robustness in small tumor segmentation. On the online validation set, the suggested ensemble achieved dice scores of 0.813, 0.873, and 0.866 for tumor enhancement, whole tumor enhancement, and tumor core enhancement, respectively, performing favorably compared with existing state-of-the-art architectures.

关键词： encoder-decoder network Dilated residual network Squeeze-and-excitation Atrous spatial pyramid pooling Brain tumor segmentation

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encoder-decoder network with RMP for tongue segmentation

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第5期61卷 1193-1207页

作者： Kusakunniran, Worapan Borwarnginn, Punyanuch Karnjanapreechakorn, Sarattha Thongkanchorn, Kittikhun Ritthipravat, Panrasee Tuakta, Pimchanok Benjapornlert, Paitoon Mahidol Univ Fac Informat & Commun Technol Nakhon Pathom Thailand Mahidol Univ Fac Engn Dept Biomed Engn Nakhon Pathom Thailand Mahidol Univ Fac Med Ramathibodi Hosp Dept Rehabil Med Bangkok Thailand

Tongue and its movements can be used for several medical-related tasks, such as identifying a disease and tracking a rehabilitation. To be able to focus on a tongue region, the tongue segmentation is needed to compute a region of interest for a further analysis. This paper proposes an encoder-decoder CNN-based architecture for segmenting a tongue in an image. The encoder module is mainly used for the tongue feature extraction, while the decoder module is used to reconstruct a segmented tongue from the extracted features based on training images. In addition, the residual multi-kernel pooling (RMP) is also applied into the proposed network to help in encoding multiple scales of the features. The proposed method is evaluated on two publicly available datasets under a scenario of front view and one tongue posture. It is then tested on a newly collected dataset of five tongue postures. The reported performances show that the proposed method outperforms existing methods in the literature. In addition, the re-training process could improve applying the trained model on unseen dataset, which would be a necessary step of applying the trained model on the real-world scenario.

关键词： Tongue segmentation encoder-decoder network RMP Separable convolution

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Lightweight multi-scale encoder- decoder network with locally enhanced attention mechanism for concrete crack segmentation

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 025021-025021页

作者： Dong, Shuai Cao, Jin Wang, Yunlong Ma, Jia Kuang, Zhiwei Zhang, Zhenhao Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Peoples R China Saarland Univ Inst Appl Mech D-66123 Saarbrucken Germany

Concrete surface crack detection and maintenance are crucial for ensuring structural safety. Deep learning-based techniques for detecting concrete cracks have become popular due to the quick advancement of artificial intelligence. However, the actual uses of these methods are limited due to issues like large model sizes and significant dependence on powerful computing hardware. To address these issues, this paper presents a lightweight multi-scale encoder-decoder network (LMED-Net) for crack detection of concrete structures. LMED-Net employs MobileNetV2 as the encoder for the initial feature extraction. A multi-scale feature extraction (MFE) module is developed and serially attached after the encoder for refining feature extraction. Finally, to strengthen the network's perception of pixels surrounding the cracks, a novel enhanced attention mechanism (EAM) is deployed in the decoder. By improving the network's attention to information within the crack regions, this mechanism keeps contextual information from being lost. Comparative experimental results show that the proposed network achieves an F1 score (F1) of 60.32% and a mean intersection over union (mIoU) of 71.04% on the crack forest dataset. On the DeepCrack dataset, the F1 and mIoU increase to 79.09% and 81.85% respectively. Notably, LMED-Net performs exceptionally well in crack segmentation since its model size and parameters count are much smaller than those of other image segmentation methods. Furthermore, ablation studies further validate the effectiveness of the proposed MFE module and EAM.

关键词： concrete crack detection semantic segmentation lightweight network encoder-decoder network local enhanced attention mechanism

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encoder-decoder network for Brain Tumor Segmentation on Multi-sequence MRI 5th

Encoder-Decoder Network for Brain Tumor Segmentation on Mult...

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5th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Iantsen, Andrei Jaouen, Vincent Visvikis, Dimitris Hatt, Mathieu Univ Brest LaTIM INSERM UMR 1101 Brest France

ISBN: (纸本)9783030466428;9783030466435

In this paper we describe our approach based on convolutional neural networks for medical image segmentation in a context of the BraTS 2019 challenge. We use the conventional encoder-decoder architecture enhanced with residual blocks, as well as spatial and channel squeeze & excitation modules. The present paper describes the general pipeline including the data pre-processing, the choices regarding the model architecture, the training procedure and the chosen data augmentation techniques. Our final results in the BraTS 2019 segmentation challenge are Dice scores equal to 0.76, 0.87 and 0.80 for enhanced tumor, whole tumor and tumor core sub-regions, respectively.

关键词： Medical imaging Tumor segmentation encoder-decoder network Soft Dice loss

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RED-Net: A Recurrent encoder-decoder network for Video-Based Face Alignment

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2018年第10期126卷 1103-1119页

作者： Peng, Xi Feris, Rogerio S. Wang, Xiaoyu Metaxas, Dimitris N. Rutgers State Univ Piscataway NJ 08854 USA IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Intellifusion Redmond WA USA

We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model. Our proposed model predicts 2D facial point heat maps regularized by both detection and regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model, instead of relying on traditional cascaded model ensembles. At the temporal level, we first decouple the features in the bottleneck of the network into temporal-variant factors, such as pose and expression, and temporal-invariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporal-variant features. We show that such feature disentangling yields better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state of the art and several variations of our method in standard datasets.

关键词： Recurrent learning encoder-decoder network Face alignment

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A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND networkING 2019年第1期2019卷 1页

作者： Zhu, Yonghua Zhang, Weilin Chen, Yihai Gao, Honghao Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Univ Shanghai Film Acad Shanghai 200072 Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China Shanghai Univ Ctr Comp Shanghai 200444 Peoples R China Shanghai ShangDa HaiRun Informat Syst Ltd Shanghai 200444 Peoples R China

Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing.

关键词： Workload prediction LSTM encoder-decoder network Attention mechanism Cloud environment

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CloudRaednet: residual attention-based encoder-decoder network for ground-based cloud images segmentation in nychthemeron

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第6期43卷 2059-2075页

作者： Shi, Chaojun Zhou, Yatong Qiu, Bo Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China

Obtaining accurate cloudage information through ground-based cloud observation is of great significance to astronomical telescope observatory site selection. This paper proposes a residual attention-based encoder-decoder network (CloudRAEDNet) for ground-based cloud image segmentation in nychthemeron. CloudRAEDNet uses ImageNet pre-trained ResNet50 as the encoder backbone network, which reduces the number of network training. The network decoder introduces residual modules to solve the problem of network degradation caused by the increase in the number of network layers. CloudRAEDNet connects encoder and decoder through attention gates to suppress the features of irrelevant areas and automatically focus on areas with prominent features. In addition, the segmentation performance of the network is further improved by the ranger optimizer. The comparative experimental results show that CloudRAEDNet can segment the local details of the ground-based cloud images more finely without increasing the time complexity. Compared with CloudSegNet, EFCN, CloudU-Net and CloudU-Netv2, CloudRAEDNet has the best segmentation performance. The results of ablation experiments show that the attention module contributes the most to CloudRAEDNet and the residual module contributes the least to CloudRAEDNet. In addition, the pre-training and Ranger optimizer also contribute to improving the segmentation performance of CloudRAEDNet.

关键词： Ground-based cloud segmentation encoder-decoder network attention gate residual network

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Composite encoder-decoder network for rapid bridge damage assessment using long-term monitoring acceleration data

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年第6期23卷 3387-3415页

作者： Yessoufou, Fadel Yang, Yibo Zhu, Jinsong Tianjin Univ State Key Lab Hydraul Engn Intelligent Construct & Tianjin Peoples R China CCCC First Highway Engn Grp Co Ltd Beijing Peoples R China Tianjin Univ Key Lab Coast Civil Struct Safety Minist Educ Tianjin Peoples R China Tianjin Univ State Key Lab Hydraul Engn Intelligent Construct & 135 Yaguan Rd Tianjin 300072 Peoples R China

In recent years, the deployment of structural health monitoring (SHM) systems has become paramount for safeguarding critical infrastructures. Notwithstanding, the development of an unsupervised deep learning framework capable of learning from long-term sensor data remains a critical challenge, particularly in accurately assessing the exact damage location. This study addresses this gap by proposing a novel approach for rapid bridge damage assessment. The proposed method employed a deep overcomplete encoder-decoder network (DOEDN) to reconstruct the acceleration data acquired from each sensor node on the bridge. The reconstruction losses generated by the DOEDN framework are then used as damage-sensitive features. Additionally, a damage indicator based on Gaussian processes is introduced to assess the damage location and evaluate its severity. The performance and sensitivity of the proposed DOEDN framework are evaluated through long-term monitoring acceleration data from a numerical highway bridge model and the well-known full-scale Z24 bridge. Furthermore, comparative assessments against the regular deep undercomplete encoder-decoder network are conducted using metrics including mean absolute error, coefficient of determination (R2), and mean intersection over union. The results show that the proposed DOEDN framework can reasonably assess the damage location and evaluate its severity across various structural scenarios in the bridge, even in the presence of temperature variations, thus providing a practical and effective solution for bridge health monitoring.

关键词： Structural health monitoring deep learning encoder-decoder network bridge damage assessment long-term monitoring data

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