检索结果-内蒙古大学图书馆

A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting

JOURNAL OF HYDROLOGY 2023年第1期619卷

作者： Jahangir, Mohammad Sina You, John Quilty, John Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada

Deep neural network (DNN) models have become increasingly popular in the hydrology community. However, most studies are related to (rainfall-) runoff simulation and comparatively fewer studies have focused on runoff forecasting. In this study, quantile-based (q = 0.05, 0.5, 0.95) encoder-decoder (ED) models that use long short -term memory network (LSTM) and dense network (DN) blocks were developed for three and five days ahead runoff forecasting. Through linear (LW) and non-linear (NLW) wavelet selection, hybrid models LSTM-DN, LSTM-DN-LW, LSTM-DN-NLW, ED, ED-LW, and ED-NLW were developed. For each lead time (LT = 3, 5) and value of q, different model configurations were created using different input lag lengths (IL = 15, 45, 180). The developed models were tested for runoff forecasting using three basins (with different characteristics) from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset. The models were compared using deterministic (e.g., the Kling-Gupta efficiency [KGE] metric) and probabilistic (e.g., reliability) statistical metrics. While the models showed high variability in performance across the three basins (KGE = 0.308-0.979 for the q = 0.5 models), very high accuracy (up to KGE = 0.979) was achieved for one of the basins with high snowmelt. The ED-NLW model was found to generally outperform the other models. Although the LSTM-DN model had the highest median KGE (0.434 across all configurations), the ED and ED-NLW models had higher reliability than LSTM-DN (90% and 91%, respectively, considering a 90% confidence level). Models coupled with NLW performed superior to those that used LW. All ED models had high reliability despite two of the basins achieving median KGE values of similar to 0.390, highlighting that quantile-based models can generate reliable forecast intervals even when the KGE of the median forecast (q = 0.5) is low. An additional experiment generated synthetic precipitation forecasts with varying degrees

关键词： encoder-decoder Deep learning LSTM Wavelet decomposition Runoff forecasting Hydrological forecasting

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

ECRU: An encoder-decoder Based Convolution Neural Network (CNN) for Road-Scene Understanding

引用

JOURNAL OF IMAGING 2018年第10期4卷 116-116页

作者： Yasrab, Robail Univ Nottingham Comp Vis Lab Sch Comp Sci Nottingham NG8 1BB England Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230000 Anhui Peoples R China

This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an evolving research area, and semantic segmentation is the most recent method for visual recognition. Among vision-based smart systems, the driving assistance system turns out to be a much preferred research topic. The proposed model is an encoder-decoder that performs pixel-wise class predictions. The encoder network is composed of a VGG-19 layer model, while the decoder network uses 16 upsampling and deconvolution units. The encoder of the network has a very flexible architecture that can be altered and trained for any size and resolution of images. The decoder network upsamples and maps the low-resolution encoder's features. Consequently, there is a substantial reduction in the trainable parameters, as the network recycles the encoder's pooling indices for pixel-wise classification and segmentation. The proposed model is intended to offer a simplified CNN model with less overhead and higher performance. The network is trained and tested on the famous road scenes dataset CamVid and offers outstanding outcomes in comparison to similar early approaches like FCN and VGG16 in terms of performance vs. trainable parameters.

关键词： convolutional neural network (CNN) ReLU encoder-decoder CamVid pooling semantic segmentation VGG-19 ADAS

来源：评论

学校读者我要写书评

暂无评论

CapNet: An encoder-decoder based Neural Network Model for Automatic Bangla Image Caption Generation

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 752-759页

作者： Rahman, Rashik Saha, Aloke Kumar Murad, Hasan Al Masud, Shah Murtaza Rashid Rahman, Nakiba Nuren Momtaz, A. S. Zaforullah Univ Asia Pacific Comp Sci & Engn Dhaka Bangladesh Chittagong Univ Engn & Technol Comp Sci & Engn Chattogram Bangladesh

Automatic caption generation from images has become an active research topic in the field of Computer Vision (CV) and Natural Language Processing (NLP). Machine generated image caption plays a vital role for the visually impaired people by converting the caption to speech to have a better understanding of their surrounding. Though significant amount of research has been conducted for automatic caption generation in other languages, far too little effort has been devoted to Bangla image caption generation. In this paper, we propose an encoder-decoder based model which takes an image as input and generates the corresponding Bangla caption as output. The encoder network consists of a pretrained image feature extractor called ResNet-50, while the decoder network consists of Bidirectional LSTMs for caption generation. The model has been trained and evaluated using a Bangla image captioning dataset named BanglaLekhaImageCaptions. The proposed model achieved a training accuracy of 91% and BLEU-1, BLEU-2, BLEU-3, BLEU-4 scores of 0.81, 0.67, 0.57, and 0.51 respectively. Moreover, a comparative study for different pretrained feature extractors such as VGG-16 and Xception is presented. Finally, the proposed model has been deployed on an embedded device for analysing the inference time and power consumption.

关键词： -Bangla image caption generation encoder-decoder bidirectional long short term memory (LSTM) bangla natural language processing (NLP)

来源：评论

学校读者我要写书评

暂无评论

Software Reliability Prediction through encoder-decoder Recurrent Neural Networks

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

引用

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2022年第3期7卷 325-340页

作者： Li, Chen Zheng, Junjun Okamura, Hiroyuki Dohi, Tadashi Kyushu Inst Technol Fac Comp Sci & Syst Engn Dept Biosci & Bioinformat Iizuka Fukuoka 8208502 Japan Ritsumeikan Univ Dept Informat Sci & Engn Kusatsu 5258577 Japan Hiroshima Univ Grad Sch Adv Sci Engn Higashihiroshima 7398527 Japan

With the growing demand for high reliability and safety software, software reliability prediction has attracted more and more attention to identifying potential faults in software. Software reliability growth models (SRGMs) are the most commonly used prediction models in practical software reliability engineering. However, their unrealistic assumptions and environment-dependent applicability restrict their development. Recurrent neural networks (RNNs), such as the long short-term memory (LSTM), provide an end-to-end learning method, have shown a remarkable ability in time-series forecasting and can be used to solve the above problem for software reliability prediction. In this paper, we present an attention-based encoder-decoder RNN called EDRNN to predict the number of failures in the software. More specifically, the encoder-decoder RNN estimates the cumulative faults with the fault detection time as input. The attention mechanism improves the prediction accuracy in the encoder-decoder architecture. Experimental results demonstrate that our proposed model outperforms other traditional SRGMs and neural network-based models in terms of accuracy.

关键词： Software reliability Recurrent neural networks (RNNs) Long short-term memory (LSTM) encoder-decoder Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Adding visual attention into encoder-decoder model for multi-modal machine translation

JOURNAL OF ENGINEERING RESEARCH

引用

JOURNAL OF ENGINEERING RESEARCH 2023年第2期11卷

作者： Xu, Chun Yu, Zhengqing Shi, Xiayang Chen, Fang Xinjiang Univ Finance & Econ Coll Informat Management Urumqi Peoples R China Zhengzhou Univ Light Ind Zhenghzou Peoples R China Henan Univ Tradit Chinese Med Med Dept Zhengzhou Peoples R China

Multi-modal neural machine translation (MNMT) aims to integrate visual and textual information to translate source sentences into target and attracts a lot of attentions. Existing methods contribute a lot for capturing interactions between visual and textual features to improve the performance of neural machine translation (NMT). However, most of them don't consider the multi-modal consistency for MNMT. In fact, the image provides the semantic global consistency between different languages. We believe that adding bilingual-visual agreement into the encoder and decoder simultaneously can obtain bilingual representations and is useful for NMT. In this paper, we propose to simultaneously integrate visual information in encoder and decoder to learn the interactions between visual and textual features, in this paper called the model VMNMT. As the visual information provide global context, the encoder and decoder can learn the bilingual representations. Besides, we introduce a new bilingual-visual agreement decoder to learn to better representations of corresponding imagesentence pairs. In the experiment, the improvement was 2.02 BLEU on the English-German16 dataset and 1.9 BLEU on the English-German17 dataset. The results show that our method can outperform baselines on several widely-used datasets in terms of various metrics.

关键词： Natural language processing Multi-modal machine translation Bilingual-visual encoder-decoder Bilingual representations

来源：评论

学校读者我要写书评

暂无评论

A full-scale hierarchical encoder-decoder network with cascading edge-prior for infrared and visible image fusion

引用

PATTERN RECOGNITION 2024年 148卷

作者： Luo, Xiaoqing Wang, Juan Zhang, Zhancheng Wu, Xiao-jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jiangnan Univ Inst Adv Technol Wuxi 214122 Peoples R China Suzhou Univ Sci & Technol Coll Elect & Informat Engn Suzhou 215009 Peoples R China

Most existing deep learning-based infrared and visible image fusion methods always fail to consider the full-scale long-range correlation and the prior knowledge, resulting in the fused images with low-contrast salient objects and blurred edge details. To overcome these drawbacks, a full-scale hierarchical encoder-decoder network with cascading edge-prior for infrared and visible image fusion is proposed. First, a top-down encoder extracts the hierarchical representations from source image. Then, to inject edge priors into the network and capture the progressive semantic correlations, a triple fusion mechanism is proposed including edge image fusion based on maximum fusion strategy, single-scale shallow layer fusion and full-scale semantic layer fusion based on dualattention fusion (DAF) strategy. The fused full-scale semantic features (F2SF) are obtained by capturing the long-range affinities of the full-scale. At the same time, a cascading edge-prior branch (CEPB) is designed to embed the fused edge knowledge into fused single-scale shallow features, jointly guiding the decoder to focus on abundant details layer-by-layer on the basis of F2SF, thus recovering the edge and texture details of the fused image well. Finally, a novel loss function consisting of SSIM, intensity and edge loss is constructed to further maintain the network with better edge representation and reconstruction capability. Compared with existing state-of-the-art fusion methods, the proposed method has better performance in terms of both visual evaluation and objective evaluation on public datasets. The source code is available at https://***/lxq-jnu/FSFu sion.

关键词： Full-scale Long-range Progressive semantic Edge-prior encoder-decoder Infrared and visible image fusion

来源：评论

学校读者我要写书评

暂无评论

A method of face repair based on encoder-decoder and dual discrimination network

A method of face repair based on encoder-decoder and dual di...

引用

第三十九届中国控制会议

作者： Cui Can Zhao Jun Xiong Xingzhong Nathaniel O.Edwards Sichuan University of Science ＆ Engineering

Neural networks have made significant achievements in the field of image restoration. To efficiently repair facial images with large areas damaged, a decoder-encoder structured convolutional neural network is used as a generative model and skip-connection is added between some of its layers to enhance the structure prediction ability of the generated model and well suppressed the problem that the repair network is easy to over-fitting. The global discrimination network mostly uses the image’s edge structure and feature information to ensure that the repaired image, which is the output from the repair network, conforms to visual connectivity, while the local discriminators, not only recognize local consistency but also optimize more details. The network structure proposed in this paper combines the encoder-decoder, skip-connection, and dual discriminator networks to improve the effect of face completion. The experimental results on the CelebA show that the proposed method is superior to other methods in repairing images with large areas of damage.

关键词： Generative Adversarial Network（GAN） face inpainting skip-connection encoder-decoder global and local discriminators

来源：评论

学校读者我要写书评

暂无评论

An Odor Labeling Convolutional encoder-decoder for Odor Sensing in Machine Olfaction

引用

SENSORS 2021年第2期21卷 388-388页

作者： Wen, Tengteng Mo, Zhuofeng Li, Jingshan Liu, Qi Wu, Liming Luo, Dehan Guangdong Univ Technol Dept Electromech Engn Guangzhou Higher Educ Mega Ctr 100 Waihuan Rd W Guangzhou 510006 Peoples R China

Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we propose an odor labeling convolutional encoder-decoder (OLCE) for odor identification in machine olfaction. OLCE composes a convolutional neural network encoder and decoder where the encoder output is constrained to odor labels. An electronic nose was used for the data collection of gas responses followed by a normative experimental procedure. Several evaluation indexes were calculated to evaluate the algorithm effectiveness: accuracy 92.57%, precision 92.29%, recall rate 92.06%, F1-Score 91.96%, and Kappa coefficient 90.76%. We also compared the model with some algorithms used in machine olfaction. The comparison result demonstrated that OLCE had the best performance among these algorithms.

关键词： machine olfactions odor identifications electronic nose neural networks encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

基于encoder-decoder框架的城镇污水厂出水水质预测

引用

中国农村水利水电 2023年第11期 93-99页

作者：史红伟陈祺王云龙李鹏程长春理工大学电子信息工程学院吉林长春130022 长春水务投资发展集团有限公司吉林长春130022

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encod... 详细信息

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encoder-decoder结构的神经网络预测水质。结果显示,所提结构对LSTM和GRU网络预测能力都有一定提升,对长期预测能力提升更加显著,ED-GRU模型效果最佳,短期预测中的4个出水水质指标均方根误差(RMSE)为0.7551、0.2197、0.0734、0.3146,拟合优度(R2)为0.9013、0.9332、0.9167、0.9532,可以预测出水质局部变化,而长期预测中的4个指标RMSE为1.7204、1.7689、0.4478、0.8316,R2为0.4849、0.5507、0.4502、0.7595,可以预测出水质变化趋势,与顺序结构相比,短期预测RMSE降低10%以上,R2增加2%以上,长期预测RMSE降低25%以上,R2增加15%以上。研究结果表明,基于encoder-decoder结构的神经网络可以对污水厂出水水质进行准确预测,为污水处理工艺改进提供技术支撑。

关键词：污水厂出水 encoder-decoder 多指标水质预测 GRU模型

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：