检索结果-内蒙古大学图书馆

LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 137卷 93-105页

作者： Lyu, Pingyang Chen, Ning Mao, Shanjun Li, Mei Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China

Gas is one of the most dangerous byproducts of coal in mines. Before gas accidents occur, an abnormally increased gas concentration can be observed. Therefore, a prediction of the gas concentration in coal mines is of great significance to prevent the gas accident and ensure the production safety in the mines. By calculating the Pearson correlation coefficient for the gas concentration of different sensors, the spatial correlation of the gas concentration that is monitored for each mining face is verified. We present multi-step prediction results for gas concentration time series based on the ARMA model, the CHAOS model and the encoder-decoder model (single-sensor and multi-sensor) and compare these results. The encoder-decoder model provides high robustness in a multi-step prediction and can predict the gas concentration for five different time steps. Its prediction error is significantly lower than those of the ARMA and the CHAOS models. The prediction accuracy is further improved through a fusion with information of other sensors. In this way, this study provides a novel concept and method for gas accident prevention. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： LSTM encoder-decoder Gas concentration Multi-Sensor information Multi-Step prediction

来源：评论

学校读者我要写书评

暂无评论

Attentive U-recurrent encoder-decoder network for image dehazing

引用

NEUROCOMPUTING 2021年 437卷 143-156页

作者： Yin, Shibai Wang, Yibin Yang, Yee-Hong Southwestern Univ Finance & Econ Dept Econ Informat Engn Chengdu 611130 Sichuan Peoples R China Sichuan Normal Univ Dept Engn Chengdu 610066 Sichuan Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

Haze removal is an important pre-processing step in many computer vision tasks. Convolutional neural networks, especially the U-shaped networks, have shown to be effective in image dehazing. Nevertheless, these networks have three main limitations. First, the relevant haze information, e.g. concentration of haze, is totally ignored. Second, spatial inconsistency and information dilution usually occur when the networks refine the dehazed results with a coarse-to-fine strategy. Third, the receptive field of the network is not large enough to capture structural information. Motivated by these problems, a new attentive U-recurrent encoder-decoder dehazing network is presented, which consists of an attentive recurrent network and a U-recurrent encoder-decoder network. By assuming that haze layers with different depths can be detected by multiple stages, we use an attentive recurrent network to generate the haze attention map for guiding the U-recurrent encoder-decoder network with the concentration of haze to better estimate the clear image. Meanwhile, the features for dehazing are further enhanced and the dehazing results are refined in the U-recurrent encoder-decoder network. This design not only enables spatial consistency but also reduces information dilution with short recurrent pathways. Furthermore, a novel residual pyramid pooling module is also proposed and used in the U-recurrent encoder-decoder network, which provides the network with structural information and with an enlarged receptive field. The experimental results demonstrate that our method outperforms state-of-the-art dehazing algorithms on both synthetic and real hazy images. Haze removal is an important pre-processing step in many computer vision tasks. Convolutional neural networks, especially the U-shaped networks, have shown to be effective in image dehazing. Nevertheless, these networks have three main limitations. First, the relevant haze information, e.g. concentration of haze, is totally ignore

关键词： Image dehazing Attention mechanism Recurrent network encoder-decoder Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

An Effective Classification Method for Hyperspectral Image With Very High Resolution Based on encoder-decoder Architecture

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1509-1519页

作者： Zhang, Zhen Jiang, Tao Liu, Chenxi Zhang, Linjing Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China

Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. However, low interclass spectral variability and high intraclass spectral variability in VHR-HSI, shadows, pedestrians, and low signal-to-noise ratio increase the fuzziness of different categories. To address the known challenges of VHR-HSI classification, an effective classification method based on encoder-decoder architecture is proposed. The proposed algorithm is an object-level contextual convolution neural network based on an improved residual network backbone with 3-D convolution, which fully considers the spatial-spectral and contextual features of HSIs. Two different spatial resolution aerial HSIs are used as experimental data. The results show that the overall accuracy of the proposed method is improved by 7.42% and 18.82%, respectively, compared to the pixelwise convolution neural network and DeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.

关键词： encoder-decoder hyperspectral image (HSI) with high spatial resolution image classification 3-D convolution residual network

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Nested Residual encoder-decoder Network for Overhead Contact System Fastener Anomaly Detection

引用

IEEE ACCESS 2021年 9卷 74959-74968页

作者： Wei, Tiantian Guo, Qifan Zhang, Xuewu Zhang, Cheng Jing, Wenfeng Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China China Railway First Survey & Design Inst Grp Co L Xian 710043 Peoples R China

An overhead contact system (OCS) is key to providing power to high-speed railways. OCS detection is an important measure to ensure the safe operation of a high-speed railway. At present, OCS anomaly detection mainly relies on the manual analysis of the images regularly collected by the 4C system, which is very inefficient and can easily miss anomalies. Although some classification and object detection methods based on deep learning can be used for OCS anomaly detection, the effective training of deep networks can be difficult to support due to the small number of anomaly OCS image samples. Considering that most OCS faults are abnormal fasteners, we propose an abnormal detection method based on normal images, called the nested residual encoder-decoder network (NRE-Net). This network consists of two nested encoder-decoder networks, where the encoder is the shared part, and a residual structure is added to the encoding and decoding branches to enhance the feature expression ability. The experimental results show that the method can greatly improve the accuracy of anomaly detection for the CIFAR-10 dataset and OCS fastener dataset. Compared with the previous state-of-the-art approaches, the F-1 score of the proposed method for the two classes fastener in the OCS fastener dataset has increased by 10.8% and 11.9%, respectively.

关键词： OCS anomaly detection encoder-decoder distance measurement

来源：评论

学校读者我要写书评

暂无评论

ED-DRAP: encoder-decoder Deep Residual Attention Prediction Network for Radar Echoes

引用

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Che, Hongshu Niu, Dan Zang, Zengliang Cao, Yichao Chen, Xisong Southeast Univ Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China PLA Univ Sci & Technol Inst Meteorol & Oceanog Nanjing 211101 Peoples R China

Precipitation nowcasting is quite important and fundamental. It underlies various public services ranging from rainstorm warnings to flight safety. In order to further improve the prediction accuracy for the spatiotemporal sequence forecasting problem, we propose an encoder-decoder deep residual attention prediction network, which adaptively rescales the multiscale sequence- and spatial-wise features and achieves very deep trainable residual prediction by integrating global residual learning and local deep residual sequence and spatial attention blocks (RSSABs). Experiments in a real-world radar echo map dataset of South China show that compared with the ingenious PredRNN++, TrajGRU methods, and newly proposed Unet-based methods, our ED-DRAP network performs better on the precipitation nowcasting metrics, as well as occupies small GPU memory.

关键词： Feature extraction Spatiotemporal phenomena Radar Decoding Forecasting Three-dimensional displays Radar remote sensing Deep residual prediction encoder-decoder precipitation nowcasting sequence and spatial attention

来源：评论

学校读者我要写书评

暂无评论

Action-Aware encoder-decoder Network for Pedestrian Trajectory Prediction

引用

Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 20-27页

作者：傅家威赵旭 Department of Automation School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Accurate pedestrian trajectory predictions are critical in self-driving systems,as they are fundamental to the response-and decision-making of ego *** this study,we focus on the problem of predicting the future trajectory of pedestrians from a first-person *** existing trajectory prediction methods from the first-person view copy the bird’s-eye view,neglecting the differences between the *** this end,we clarify the differences between the two views and highlight the importance of action-aware trajectory prediction in the first-person *** propose a new action-aware network based on an encoder-decoder framework with an action prediction and a goal estimation branch at the end of the *** the decoder part,bidirectional long short-term memory(Bi-LSTM)blocks are adopted to generate the ultimate prediction of pedestrians’future *** method was evaluated on a public dataset and achieved a competitive performance,compared with other *** ablation study demonstrates the effectiveness of the action prediction branch.

关键词： pedestrian trajectory prediction first-person view action prediction encoder-decoder bidirectional long short-term memory(Bi-LSTM)

来源：评论

学校读者我要写书评

暂无评论

Multivariate time series forecasting via attention-based encoder-decoder framework

引用

NEUROCOMPUTING 2020年 388卷 269-279页

作者： Du, Shengdong Li, Tianrui Yang, Yan Horng, Shi-Jinn Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 10607 Taiwan

Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder-decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multivariate time series Temporal attention Multi-step forecasting encoder-decoder Deep learning Long short-term memory networks

来源：评论

学校读者我要写书评

暂无评论

An Improved and Robust encoder-decoder for Skin Lesion Segmentation

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第8期47卷 9861-9875页

作者： Hafhouf, Bellal Zitouni, Athmane Megherbi, Ahmed Chaouki Sbaa, Salim Univ Mohamed Khider Dept Elect Engn LESIA Lab Biskra Algeria Univ Mohamed Khider Dept Elect Engn LICCC Lab Biskra Algeria

Automatic segmentation of skin lesions is an important step in computer-aided diagnosis systems for melanoma detection. Although numerous methods have been proposed in the literature, this task is still a challenging issue due to the similarity between different lesions and complex visual characteristics that may be presented in the images. In this paper, we propose major modifications to the state-of-the-art U-Net structure to further improve its capability in skin lesion segmentation. These modifications are presented in both the encoding and the decoding paths. Instead of using only standard convolutional layers like U-Net, the proposed encoding path consists of 10 standard convolutional layers, which are inspired from the Visual Geometry Group (VGG16) network, followed by a pyramid pooling module and a dilated convolutional block. This combination enables to learn better representative feature maps and preserve more spatial resolution. Furthermore, dilated residual blocks are introduced in the decoding path to further refine the segmentation maps. The experimental results on three datasets including the IEEE International Symposium on Biomedical Imaging (ISBI) 2017, ISBI 2016, and PH2 showed that our proposed method has better performance than the basic U-Net, FCN, SegNet, and U-Net + + , and achieved the performance of state-of-the-art segmentation techniques, with minimum pre- and post-processing operations.

关键词： Dilated convolution Pyramid pooling Skin lesion segmentation Dermoscopy encoder-decoder U-Net

来源：评论

学校读者我要写书评

暂无评论

Band-Independent encoder-decoder Network for Pan-Sharpening of Remote Sensing Images

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第7期58卷 5208-5223页

作者： Liu, Chi Zhang, Yongjun Wang, Shugen Sun, Mingwei Ou, Yangjun Wan, Yi Liu, Xiu Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China

Pan-sharpening is a fundamental task for remote sensing image processing. It aims at creating a high-resolution multispectral (HRMS) image from a multispectral (MS) image and a panchromatic (PAN) image. In this article, a new band-independent encoder-decoder network is proposed for pan-sharpening. The network takes a single band of the MS (BMS) image, the PAN image, and the low-resolution PAN (LRPAN) image as inputs. The output of the network is the corresponding band of high-resolution MS (HRBMS) image. In this way, the network can process MS images with any number of bands. The overall structure of the network consists of two encoder-decoder modules at low-resolution and high-resolution, respectively. An auxiliary LRPAN image is used to speed up the training and improve the performance. The partly shared network and hierarchical structure for low-resolution and high-resolution enable a better fusion of features extracted from different scales. With a fast fine-tuning strategy, the trained model can be applied to images from different sensors. Experiments performed on different data sets demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.

关键词： Decoding Image resolution Task analysis Remote sensing Feature extraction Sensors Transforms Band-independent deep learning encoder-decoder pan-sharpening

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：