检索结果-内蒙古大学图书馆

Dynamic modeling of post-combustion carbon capture process based on multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder network

引用

APPLIED THERMAL ENGINEERING 2025年 258卷

作者： Zheng, Cheng Sha, Peng Mo, Zhengyang Tang, Zihan Wang, Meihong Wu, Xiao Southeast Univ Natl Engn Res Ctr Power Generat Control & Safety Sch Energy & Environm Nanjing 210096 Peoples R China Univ Sheffield Dept Chem & Biol Engn Sheffield S1 3JD England

Solvent-based post-combustion carbon capture (PCC) technology is a promising, near-term solution for decarbonizing power generation and industrial facilities. Model-based process simulation is crucial for the optimal design and operation of the PCC process. Recently, data-driven models have gained attention due to their adaptability, efficient computation and high accuracy. However, the nonlinearity, strong couplings and multitime scale features of the PCC process pose significant challenges for model identification. To this end, this paper proposes a multi-gate mixture-of-experts incorporating dual-stage attention-based encoder-decoder (MMoE-DAED) network for dynamic modeling of the PCC process under wide operating conditions. An encoder-decoder composed of long short-term memory (LSTM) network is employed to extract features from the time-dependent input data and learn the complex dynamic interactions caused by the inertial and delay properties of the process. Dual-stage attention mechanism is incorporated into the encoder and decoder respectively to select the most relevant input features and their correlations within the time series data. To enhance multioutput prediction accuracy, multi-gate mixture-of-experts (MMoE) framework that considers correlations of multitask learning is implemented. Simulation results using operating data from a PCC experimental setup indicate that the proposed modeling approach accurately predicts the steady-state values and dynamic trends of the CO2 capture rate and stripper bottom temperature over a wide operating range. The RMSE, MAPE and R2 indices for the CO2 capture rate are 2.1592, 0.0295, 0.9641, respectively, and for the stripper bottom temperature are 0.1491, 0.0003, 0.9833, respectively. Validations on a PCC simulator further verify the accuracy and efficiency of the MMoE-DAED model, which enables an 80.87% reduction in computation time compared to the simulator. This paper points to a new direction for the data-driven dyna

关键词： Post-combustion carbon capture Dynamic modeling Multi-output prediction Multi-gate mixture-of-experts encoder-decoder Dual-stage attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Destination intention estimation-based convolutional encoder-decoder for pedestrian trajectory multimodality forecast

引用

MEASUREMENT 2025年 239卷

作者： Wang, Ruiping Lam, Siew-Kei Wu, Meiqing Hu, Zhijian Wang, Changshuo Wang, Jing Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore North China Univ Technol Sch Elect & Control Engn Beijing Peoples R China

Forecasting pedestrian trajectory is a vital area of research in smart urban mobility, which can be applied to intelligent transportation and intelligent surveillance. Current approaches employ conditional variational autoencoders to model future trajectory multimodality. However, these methods generate multi-modal trajectories for one single destination, ignoring the trajectory multimodality caused by the uncertainty of the pedestrians' destination intention. Besides, they can lead to mode collapse and training instability. To address this issue, we propose a novel destination intention estimation-based convolutional encoder-decoder framework for multimodal trajectory forecast. Specially, we design a destination intention estimator to forecast pedestrian future destination intentions at the last time step. Then, we devise a trajectory decoder module to forecast pedestrian trajectories at each time step with the assistance of the destination intentions. To evaluate our method, we perform experiments on publicly available benchmark datasets and demonstrate that our proposed method achieves the superior results compared with state-of-the-art approaches.

关键词： Trajectory multimodality Smart urban mobility encoder-decoder Social interactions Graph convolution

来源：评论

学校读者我要写书评

暂无评论

The unsupervised short text classification method based on GCN encoder-decoder and local enhancement

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Wei, Yingying Wang, Ze Li, Jianbin Li, Tao Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Hunan Peoples R China Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha 410004 Hunan Peoples R China

Like all fields of data science, short text classification seeks to achieve high-quality results with limited data. Although supervised learning methods have made notable progress in this area, they require much-labeled data to achieve adequate accuracy. However, in many practical applications, labeled data is scarce, and manual labeling is not only time-consuming and labor-intensive but also expensive and may require specialized expertise. Therefore, this paper addresses the challenge of insufficient labeled data through unsupervised methods while ensuring the effective extraction of semantic features from the text. Building on this objective, we propose a novel unsupervised short text classification method within the framework of autoencoders. Specifically, we first design the MRFasGCN encoder and derive the relationships between nodes in its hidden layers, thereby enhancing the capture of text features and semantic information. Furthermore, we construct a dual-node-based decoder that reconstructs the topology and node attributes unsupervised. This approach compensates for feature deficiencies from multiple perspectives, alleviating the issue of insufficient features in short texts. Finally, we propose a localized enhancement method that integrates node features and topology, strengthening the connections between relevant nodes. This improves the model's understanding of the text's local context while mitigating the overfitting issues caused by feature sparsity in short texts. Extensive experimental results demonstrate the pronounced superiority of our proposed UEDE model over existing methods on the dataset, validating its effectiveness in short-text classification. Our code is submitted in https://***/w123yy/UEDE.

关键词： Short text classification Graph neural networks Unsupervised learning encoder-decoder Feature enhancement

来源：评论

学校读者我要写书评

暂无评论

A Highly Robust encoder-decoder Network with Multi-Scale Feature Enhancement and Attention Gate for the Reduction of Mixed Gaussian and Salt-and-Pepper Noise in Digital Images

引用

JOURNAL OF IMAGING 2025年第2期11卷 51-51页

作者： Tripathi, Milan Kongprawechnon, Waree Kondo, Toshiaki Thammasat Univ Sirindhorn Int Inst Technol Sch Informat Comp & Commun Technol Pathum Thani 12120 Thailand

Image denoising is crucial for correcting distortions caused by environmental factors and technical limitations. We propose a novel and highly robust encoder-decoder network (HREDN) for effectively removing mixed salt-and-pepper and Gaussian noise from digital images. HREDN integrates a multi-scale feature enhancement block in the encoder, allowing the network to capture features at various scales and handle complex noise patterns more effectively. To mitigate information loss during encoding, skip connections transfer essential feature maps from the encoder to the decoder, preserving structural details. However, skip connections can also propagate redundant information. To address this, we incorporate attention gates within the skip connections, ensuring that only relevant features are passed to the decoding layers. We evaluate the robustness of the proposed method across facial, medical, and remote sensing domains. The experimental results demonstrate that HREDN excels in preserving edge details and structural features in denoised images, outperforming state-of-the-art techniques in both qualitative and quantitative measures. Statistical analysis further highlights the model's ability to effectively remove noise in diverse, complex scenarios with images of varying resolutions across multiple domains.

关键词： image denoising Gaussian salt-and-pepper encoder-decoder multi-scale feature enhancement

来源：评论

学校读者我要写书评

暂无评论

A Novel encoder-decoder Model for Short-Term Multistep Wind Power Prediction

引用

IEEE ACCESS 2025年 13卷 78647-78663页

作者： Wang, Qiushi Wang, Dekuan Li, Yifan China Inst Water Resources & Hydropower Res Beijing 100038 Peoples R China Beijing IWHR Technol Co Ltd Beijing 100038 Peoples R China

Wind power has emerged as a vital renewable energy source. However, its inherent temporal variability and non-stationarity pose significant challenges for accurate forecasting. To solve this problem, this study proposes a novel multi-step wind power forecasting model based on an encoder-decoder architecture. It incorporates a multi-frequency attention mechanism into a multi-layer long short-term memory (LSTM) network. The former increases the ability of the model to capture long-term dependencies and global features, while the later focuses on short-term internal dynamics. Together, they allow the model to accurately extract critical frequency information across various time scales, and adaptively emphasize key features within the temporal domain. The experimental results show that the proposed model outperforms different benchmark methods in terms of many performance indicators, including the mean absolute error, mean squared error, and root mean squared error. This demonstrates its high potential to provide technical support for the precise wind power forecasting, and to contribute to the efficient integration of wind energy into power grids.

关键词： Wind power generation Feature extraction Forecasting Accuracy Predictive models Transformers Time series analysis Attention mechanisms Data mining Market research encoder-decoder multi-frequency attention multi-layer LSTM wind power forecast

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder recurrent network model for interactive character animation generation

引用

VISUAL COMPUTER 2017年第6-8期33卷 971-980页

作者： Wang, Yumeng Che, Wujun Xu, Bo Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

In this paper, we propose a generative recurrent model for human-character interaction. Our model is an encoder-recurrent-decoder network. The recurrent network is composed by multiple layers of long short-term memory (LSTM) and is incorporated with an encoder network and a decoder network before and after the recurrent network. With the proposed model, the virtual character's animation is generated on the fly while it interacts with the human player. The coming animation of the character is automatically generated based on the history motion data of both itself and its opponent. We evaluated our model based on both public motion capture databases and our own recorded motion data. Experimental results demonstrate that the LSTM layers can help the character learn a long history of human dynamics to animate itself. In addition, the encoder-decoder networks can significantly improve the stability of the generated animation. This method can automatically animate a virtual character responding to a human player.

关键词： Human-character interaction Long short-term memory encoder-decoder Character animation Recurrent neural network Motion capture data

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder With Cascaded CRFs for Semantic Segmentation

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第5期31卷 1926-1938页

作者： Ji, Jian Shi, Rui Li, Sitong Chen, Peng Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

When dealing with semantic segmentation, how to locate the object boundary information more accurately is a key problem to distinguish different objects better. The existing methods lose some image information more or less in the process of feature extraction, which also includes the boundary and context information. At present, some semantic segmentation methods use CRFs (conditional random fields) to obtain boundary information, but they usually only deal with the final output of the model. In this article, inspired by the skip connection of FCN (Fully convolution network) and the good boundary refinement ability of CRFs, a cascaded CRFs is designed and introduced into the decoder of semantic segmentation model to learn boundary information from multi-layers and enhance the ability of the model in object boundary location. Furthermore, in order to supplement the semantic information of images, the output of the cascaded CRFs is fused with the output of the last decoder, so that the model can enhance the ability of locating the object boundary and get more accurate semantic segmentation results. Finally, a number of experiments on different datasets illustrate the feasibility and efficiency of our method, showing that our method enhances the model's ability to locate target boundary information.

关键词： Semantic segmentation encoder-decoder fully convolution network conditional random fields boundary location

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs

引用

PEERJ COMPUTER SCIENCE 2024年 10卷 e2178页

作者： Newson, Kiri S. Benoit, David M. Beavis, Andrew W. Univ Hull Dept Phys & Math Kingston Upon Hull England Univ Hull EA Milne Ctr Astrophys Dept Phys & Math Kingston Upon Hull England Hull Univ Teaching Hosp NHS Trust Queens Ctr Oncol Med Phys Dept Cottingham England Univ London Univ Coll London Med Phys & Biomed Engn London England Univ Hull Hull York Med Sch Kingston Upon Hull England

This work presents the application of an encoder-decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real- world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks. We demonstrate a high-quality automated segmentation prediction of thoracic CT scans that correctly delineates the infected regions of the lungs. This segmentation automation can be used as a tool to speed up the contouring process, either to check manual contouring in place of a peer checking, when not possible or to give a rapid indication of infection to be referred for further treatment, thus saving time and resources. In contrast, manual contouring is a time-consuming process in which a professional would contour each patient one by one to be later checked by another professional. The proposed model uses approximately 49 k parameters while others average over 1,000 times more parameters. As our approach relies on a very compact model, shorter training times are observed, which make it possible to easily retrain the model using other data and potentially afford "personalised medicine" workflows. The model achieves similarity scores of Specificity (Sp) = 0.996 +/- 0.001, Accuracy (Acc) = 0.994 +/- 0.002 and Mean absolute error (MAE) = 0.0075 +/- 0.0005.

关键词： encoder-decoder Machine learning Automated segmentation CNN COVID-19 Lung CT Simple segmentation Lung segmentation Autoencoder

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder neural networks in interpretation of X-ray spectra

引用

JOURNAL OF ELECTRON SPECTROSCOPY AND RELATED PHENOMENA 2024年 277卷

作者： Passilahti, Jalmari Vladyka, Anton Niskanen, Johannes Univ Turku Dept Phys & Astron Vesilinnantie 5 Turku 20014 Finland

encoder-decoder neural networks (EDNN) condense information most relevant to the output of the feedforward network to activation values at a bottleneck layer. We study the use of this architecture in emulation and interpretation of simulated X-ray spectroscopic data with the aim to identify key structural characteristics for the spectra, previously studied using emulator-based component analysis (ECA). We find an EDNN to outperform ECA in covered target variable variance, but also discover complications in interpreting the latent variables in physical terms. Asa compromise of the benefits of these two approaches, we develop a network where the linear projection of ECA is used, thus maintaining the beneficial characteristics of vector expansion from the latent variables for their interpretation. These results underline the necessity of information recovery after its condensation and identification of decisive structural degrees of freedom for the output spectra fora justified interpretation.

关键词： X-ray spectroscopy Machine learning Neural networks encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

encoder-decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第9期32卷 4217-4230页

作者： Li, Lianfa Fang, Ying Wu, Jun Wang, Jinfeng Ge, Yong Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Univ Calif Irvine Dept Environm & Occupat Hlth Irvine CA 92697 USA

Although increasing hidden layers can improve the ability of a neural network in modeling complex nonlinear relationships, deep layers may result in degradation of accuracy due to the problem of vanishing gradient. Accuracy degradation limits the applications of deep neural networks to predict continuous variables with a small sample size and/or weak or little invariance to translations. Inspired by residual convolutional neural network in computer vision, we developed an encoder-decoder full residual deep network to robustly regress and predict complex spatiotemporal variables. We embedded full shortcuts from each encoding layer to its corresponding decoding layer in a systematic encoder-decoder architecture for efficient residual mapping and error signal propagation. We demonstrated, theoretically and experimentally, that the proposed network structure with full residual connections can successfully boost the backpropagation of signals and improve learning outcomes. This novel method has been extensively evaluated and compared with four commonly used methods (i.e., plain neural network, cascaded residual autoencoder, generalized additive model, and XGBoost) across different testing cases for continuous variable predictions. For model evaluation, we focused on spatiotemporal imputation of satellite aerosol optical depth with massive nonrandomness missingness and spatiotemporal estimation of atmospheric fine particulate matter <= 2.5 mu m (PM2.5). Compared with the other approaches, our method achieved the state-of-the-art accuracy, had less bias in predicting extreme values, and generated more realistic spatial surfaces. This encoder-decoder full residual deep network can be an efficient and powerful tool in a variety of applications that involve complex nonlinear relationships of continuous variables, varying sample sizes, and spatiotemporal data with weak or little invariance to translation.

关键词： Bias deep learning encoder-decoder full residual deep network non-linear regression prediction of satellite aerosol optical depth (AOD) and PM2.5 spatiotemporal modeling

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：