检索结果-内蒙古大学图书馆

Establishment of CNN and encoder-decoder Models for the Prediction of Characteristics of Flow and Heat Transfer around NACA Sections

引用

ENERGIES 2022年第23期15卷 9204页

作者： Seo, Janghoon Yoon, Hyun-Sik Kim, Min-Il Pusan Natl Univ Dept Naval Architecture & Ocean Engn 2 Busandaehak Ro 63beon Gil Busan 46241 South Korea

The present study established two different models based on the convolutional neural network (CNN) and the encoder-decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well with the true data. The contours of the velocity components and the pressure coefficients reasonably explained the variation of the aerodynamic coefficients according to the geometric parameter of the NACA section. In order to physically interpret the heat transfer performance, more quantitative and qualitative information are needed owing to the lack of the correlation and the resolution of the thermal fields. Consequently, the present approaches will be useful to design the NACA section-based shape giving higher aerodynamic and heat transfer performances by quickly predicting the force and heat transfer coefficients. In addition, the predicted flow and thermal fields will provide the physical interpretation of the aerodynamic and heat transfer performances.

关键词： convolutional neural network encoder-decoder aerodynamics heat transfer NACA section computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Human Pose Estimation from Sparse IMUs through Temporal encoder and Regression decoder

引用

SENSORS 2023年第7期23卷 3547页

作者： Liao, Xianhua Dong, Jiayan Song, Kangkang Xiao, Jiangjian Ningbo Univ Sch Informat Sci & Engn Ningbo 315211 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China

Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) usually rely on dense and complex wearable sensors and time-consuming calibration, causing intrusions to the subject and hindering free body movement. The sparse IMUs-based method has drawn research attention recently. Existing sparse IMUs-based three-dimensional pose estimation methods use neural networks to obtain human poses from temporal feature information. However, these methods still suffer from issues, such as body shaking, body tilt, and movement ambiguity. This paper presents an approach to improve three-dimensional human pose estimation by fusing temporal and spatial features. Based on a multistage encoder-decoder network, a temporal convolutional encoder and human kinematics regression decoder were designed. The final three-dimensional pose was predicted from the temporal feature information and human kinematic feature information. Extensive experiments were conducted on two benchmark datasets for three-dimensional human pose estimation. Compared to state-of-the-art methods, the mean per joint position error was decreased by 13.6% and 19.4% on the total capture and DIP-IMU datasets, respectively. The quantitative comparison demonstrates that the proposed temporal information and human kinematic topology can improve pose accuracy.

关键词： three-dimensional human pose sparse IMUs encoder-decoder temporal convolutional encoder human kinematics hierarchy regression decoder

来源：评论

学校读者我要写书评

暂无评论

Improving soil moisture prediction using a novel encoder-decoder model with residual learning

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 195卷 106816-106816页

作者： Li, Qingliang Li, Zhongyan Wei Shangguan Wang, Xuezhi Lu Li Yu, Fanhua Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Guangdong Prov Key Lab Climate Change & Nat Disas Southern Marine Sci & Engn Guangdong Lab Zhuhai Guangzhou Guangdong Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

The skillful prediction of soil moisture can provide much help for many practical applications including ecosystem management and precision agriculture. It presents great challenges because the future variation of soil moisture has much uncertainty. Therefore, a novel encoder-decoder deep learning model with residual learning based on Long Short-Term Memory (EDT-LSTM) is developed in this study as an alternative data-intelligence tool. This model includes two layers: the encoder-decoder LSTM layer and LSTM with a fully connected layer, which is used to enhance the prediction ability by considering the intermediate time-series data between the input time step and the predictive time step. We tested EDT-LSTM for soil moisture prediction at the lead time of 1, 3, 5, 7 and 10 days by using data from FLUXNET sites. The result shows that the improvements brought by EDT-LSTM were about 7.95% (1 day), 10.10% (3 days), 12.68% (5 days), 15.49% (7 days) and 19.71% (10 days) in average according to the R-2 taking LSTM as the baseline. Furthermore, the predictability of soil moisture over various conditions (i.e., different hyper-parameters in EDT-LSTM, different predictive models, different climate regions and different sites) has been widely discussed for the understanding of models' behavior in this paper. The proposed EDT-LSTM offered a new tool to predict soil moisture better. The code of EDT-LSTM is publicly available at https://***/ljz1228/CLM-LSTM-soil-moisture-prediction.

关键词： Soil moisture Machine learning LSTM encoder-decoder Residual learning

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder Based CNN Structure for Microscopic Image Identification 27th

Encoder-Decoder Based CNN Structure for Microscopic Image Id...

引用

27th International Conference on Neural Information Processing

作者： Polap, Dawid Wozniak, Marcin Korytkowski, Marcin Scherer, Rafal Silesian Tech Univ Fac Appl Math Kaszubska 23 PL-44100 Gliwice Poland Czestochowa Tech Univ Dept Intelligent Comp Syst Czestochowa Poland

ISBN: (纸本)9783030638290;9783030638306

The significant development of classifiers has made object detection and classification by using neural networks more effective and more straightforward. Unfortunately, there are images where these operations are still difficult due to the overlap of objects or very blurred contours. An example is images obtained from various microscopes, where bacteria or other biological structures can merge, or even have different shapes. To this end, we propose a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in such images. In our research, we have included elements such as very weak contours of shapes that may result from the merging of biological objects. The presented method was compared with others, such as a faster recurrent convolutional neural network to indicate the advantages of the proposed solution.

关键词： Convolutional neural networks encoder-decoder Bacteria shape detection

来源：评论

学校读者我要写书评

暂无评论

Context prior-based with residual learning for face detection: A deep convolutional encoder-decoder network

引用

SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 88卷 115948-115948页

作者： Zhou, Zexun He, Zhongshi Jia, Yuanyuan Du, Jinglong Wang, Lulu Chen, Ziyu Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hanshan Normal Univ Coll Comp & Informat Engn Chaozhou 521041 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder-decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder-decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://***/zzxcoder/EvaluationFHEDN.

关键词： Context prior Residual learning Deep convolutional neural network encoder-decoder Face detection

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder with Multi-scale Information Fusion for Semantic Image Segmentation 11

Encoder-Decoder with Multi-scale Information Fusion for Sema...

引用

11th International Conference on Graphics and Image Processing (ICGIP)

作者： Ma, Xinxin Liu, Kai Ding, Chongyang Yan, Lin Duan, Meiyu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781510635241

Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.

关键词： Semantic image segmentation multi-scale context module skip-layer encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Deep encoder-decoder Network-Based Wildfire Segmentation Using Drone Images in Real-Time

引用

REMOTE SENSING 2022年第24期14卷 6302-6302页

作者： Muksimova, Shakhnoza Mardieva, Sevara Cho, Young-Im Gachon Univ Dept IT Convergence Engn Sujeong Gu Seongnam 13120 Gyeonggi South Korea Gachon Univ Dept Comp Engn Sujeong Gu Seongnam 13120 Gyeonggi South Korea

Wildfire is a hazardous natural phenomenon that leads to significant human fatalities, catastrophic environmental damages, and economic losses. Over the past few years, the intensity and frequency of fires have increased worldwide. Studies have been conducted to develop distinctive solutions to minimize forest fires. Systems for distant fire detection and monitoring have been established, showing improvements in data collection and fire characterization. However, wildfires cover vast areas, making other proposed ground systems unsuitable for optimal coverage. Unmanned aerial vehicles (UAVs) have become the subject of active research in recent years. Deep learning-based image-processing methods demonstrate improved performance in various tasks, including detection and segmentation, which can be utilized to develop modern forest firefighting techniques. In this study, we established a novel two-pathway encoder-decoder-based model to detect and accurately segment wildfires and smoke from the images captured using UAVs in real-time. Our proposed nested decoder uses pre-activated residual blocks and an attention-gating mechanism, thereby improving segmentation accuracy. Moreover, to facilitate robust and generalized training, we prepared a new dataset comprising actual incidences of forest fires and smoke, varying from small to large areas. In terms of practicality, the experimental results reveal that our method significantly outperforms existing detection and segmentation methods, despite being lightweight. In addition, the proposed model is reliable and robust for detecting and segmenting drone camera images from different viewpoints in the presence of wildfire and smoke.

关键词： drone encoder-decoder forest fire and smoke segmentation deep-learning

来源：评论

学校读者我要写书评

暂无评论

An Automatic Grammar Error Correction Model Based on encoder-decoder Structure for English Texts

引用

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2022年第1期10卷

作者： Wang, Jiahao Huang, Guimin Wang, Yabing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Guangxi Peoples R China Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China

The role of information transmission in social life is irreplaceable, and language is a very important information carrier. Among all kinds of languages, English always occupies an important position. In the process of English learning, grammar error has become a difficult problem for most learners. In this paper, we propose an automatic grammar error correction model based on encoder-decoder structure. Different from traditional encoders, we design a dual-encoder structure to capture the information of source sentence and context sentence separately. The decoder is designed with a gated structure, it can effectively integrate output information of encoders. At the same time, the self-attention mechanism is combined to better solve the problem of long-distance information extraction. In addition, we propose a dynamic beam search algorithm to improve the accuracy of the word prediction process, and achieve dynamic extraction of the decoder output by combining kernel sampling techniques. We add a penalty factor to reduce the probability of generating repeated words, while suppressing the model's preference for generating shorter sentences. Finally, the proposed method is validated on the official English grammar error correction dataset. Experiments show that the dual encoder model in this paper has a good performance.

关键词： encoder-decoder Grammar Error Correction (GEC) deep neural network attention mechanism beam search

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

PixISegNet: pixel-level iris segmentation network using convolutional encoder-decoder with stacked hourglass bottleneck

引用

IET BIOMETRICS 2020年第1期9卷 11-24页

作者： Jha, Ranjeet Ranjan Jaswal, Gaurav Gupta, Divij Saini, Shreshth Nigam, Aditya Indian Inst Technol Sch Comp & Elect Engn Mandi Himachal Prades India Indian Inst Technol Jodhpur Dept Elect Engn Jodhpur Rajasthan India

In this paper, we present a new iris ROI segmentation algorithm using a deep convolutional neural network (NN) to achieve the state-of-the-art segmentation performance on well-known iris image data sets. The authors' model surpasses the performance of state-of-the-art Iris DenseNet framework by applying several strategies, including multi-scale/ multi-orientation training, model training from scratch, and proper hyper-parameterisation of crucial parameters. The proposed PixISegNet consists of an autoencoder which primarily uses long and short skip connections and a stacked hourglass network between encoder and decoder. There is a continuous scale up-down in stacked hourglass networks, which helps in extracting features at multiple scales and robustly segments the iris even in an occluded environment. Furthermore, cross-entropy loss and content loss optimise the proposed model. The content loss considers the high-level features, thus operating at a different scale of abstraction, which compliments the cross-entropy loss, which considers pixel-to-pixel classification loss. Additionally, they have checked the robustness of the proposed network by rotating images to certain degrees with a change in the aspect ratio along with blurring and a change in contrast. Experimental results on the various iris characteristics demonstrate the superiority of the proposed method over state-of-the-art iris segmentation methods considered in this study. In order to demonstrate the network generalisation, they deploy a very stringent TOTA (i.e. train-once-test-all) strategy. Their proposed method achieves $E_1$E1 scores of 0.00672, 0.00916 and 0.00117 on UBIRIS-V2, IIT-D and CASIA V3.0 Interval data sets, respectively. Moreover, such a deep convolutional NN for segmentation when included in an end-to-end iris recognition system with a siamese based matching network will augment the performance of the siamese network.

关键词： image segmentation iris recognition feature extraction image coding decoding convolutional neural nets entropy codes image classification image restoration image matching siamese matching network salient iris features pixel-level iris segmentation network stacked hourglass bottleneck nonregular reflections deep convolutional neural network stacked hourglass network cross-entropy loss pixel-to-pixel classification loss deep convolutional NN encoder-decoder image segmentation performance content loss optimisation hyper-parameterisation Iris-DenseNet framework iris image data sets iris ROI image segmentation algorithm biometric segmentation research multiscale-multiorientation training Pix-SegNet train-once-test-all strategy TOTA strategy CASIA V3 0 interval data sets IIT-D interval data sets UBIRIS-V2 interval data sets

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：