检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder network with RMP for tongue segmentation

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND networkING 2019年第1期2019卷 1-18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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Adversarial Partial Discharge Signal Reconstruction and Denoising With an encoder-decoder network

引用

IEEE ACCESS 2024年 12卷 132271-132278页

作者： Firuzi, Keyvan Middle East Tech Univ METU Dept Elect & Elect Engn TR-06800 Ankara Turkiye

As the application of online partial discharge (PD) measurement increases the importance of denoising becomes increasingly obvious. Besides denoising a PD signal to detect and calculate discharge amplitude, pulse reconstructing is required to compute rise time, fall time and other features which play a crucial role in determining discharge severity and identifying defect types. In this paper, a deep learning method based on adversarial deep network by using encoder-decoder network as a generator is developed to perform a denoising and signal reconstruction task. The issue is to provide a database that contains both noisy and denoised data because every denoising method has its limitation, and it is not possible to train a network with real data and their perfect denoised pulses. Therefore, a synthetic database is developed, and signal deformation and noise added synthetically. The trained network's performance is evaluated under actual conditions in two distinct laboratories on various days, with differing noise levels and waveforms. The proposed method outperforms the wavelet method in denoising synthetic data and shows an improvement on real data, while successfully reconstructing the PD pulses. Enhancing the network's performance further, it underwent fine-tuning with actual noise, which led to a marked enhancement in its denoising ability and overall capabilities.

关键词： Partial discharges denoising signal reconstruction signal reconstruction wavelet denoising wavelet denoising encoder-decoder network encoder-decoder network adversarial network adversarial network encoder-decoder network adversarial network

来源：评论

学校读者我要写书评

暂无评论

Building Extraction of Aerial Images by a Global and Multi-Scale encoder-decoder network

引用

REMOTE SENSING 2020年第15期12卷

作者： Ma, Jingjing Wu, Linlin Tang, Xu Liu, Fang Zhang, Xiangrong Jiao, Licheng Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Int Res Ctr Intelligent Percept & Computat Minist EducSch Artificial IntelligenceKey Lab I Xian 710071 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Semantic segmentation is an important and challenging task in the aerial image community since it can extract the target level information for understanding the aerial image. As a practical application of aerial image semantic segmentation, building extraction always attracts researchers' attention as the building is the specific land cover in the aerial images. There are two key points for building extraction from aerial images. One is learning the global and local features to fully describe the buildings with diverse shapes. The other one is mining the multi-scale information to discover the buildings with different resolutions. Taking these two key points into account, we propose a new method named global multi-scale encoder-decoder network (GMEDN) in this paper. Based on the encoder-decoder framework, GMEDN is developed with a local and global encoder and a distilling decoder. The local and global encoder aims at learning the representative features from the aerial images for describing the buildings, while the distilling decoder focuses on exploring the multi-scale information for the final segmentation masks. Combining them together, the building extraction is accomplished in an end-to-end manner. The effectiveness of our method is validated by the experiments counted on two public aerial image datasets. Compared with some existing methods, our model can achieve better performance.

关键词： building extraction aerial image encoder-decoder network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：