检索结果-内蒙古大学图书馆

A batch-wise LSTM-encoder decoder network for batch process monitoring

CHEMICAL ENGINEERING RESEARCH & DESIGN 2020年 164卷 102-112页

作者： Ren, Jiayang Ni, Dong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

Process monitoring is essential to keep quality consistency and operation safety in the batch process. However, the existence of multiphase, nonlinearity and dynamic features in the batch process makes the batch process monitoring a complicated task. In this work, a multi-layer recurrent neural network in the encoder-decoder structure called batch-wise LSTM-encoder decoder network is proposed to solve the difficulties mentioned above in batch process monitoring. The LSTM-encoder extracts the nonlinear dynamic features in both between and within batch direction, then projects the high dimensional input space to a low dimensional hidden state space. The decoder part regenerates the samples from hidden states. Control statistics H2 and SPE are designed for process monitoring, and the corresponding control limits are estimated by kernel density estimation. A case study on an extensive reference penicillin fermentation dataset suggests that the proposed method can detect the fault samples more effectively than previous methods while keeping the same robustness in normal conditions. (c) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Nonlinear batch processes Process monitoring Multi-layer LSTM encoder-decoder structure Kernel density estimation

来源：评论

学校读者我要写书评

暂无评论

Image Caption Method From Coarse to Fine Based On Dual encoder-decoder Framework

Image Caption Method From Coarse to Fine Based On Dual Encod...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Li, Zefeng Liu, Yuehu Song, Yonghong Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350359329;9798350359312

encoders are widely used in the field of image caption, but the statements generated by the current image caption method may miss the target and the generated description statements are not appropriate enough for the image content. In order to solve the above problems, we propose a coarse-fine image caption method based on dual encoder-decoder framework, which provides a mechanism for discovering and correcting omissions and enables the model to generate a complete image description. Firstly, an image feature extractor based on global and local information is designed, which can extract global information and local information of image and obtain more abundant image representation. Secondly, a dual encoder-decoder framework is designed, which consists of a coarse-grained encoder-decoder and a fine-grained encoder-decoder. Coarse-grained encoder-decoder requires only the original image features as input, which is processed by transformer to produce a coarse text description. In addition, an image feature auto-enhancement module is proposed to detect missing objects in coarse text and enhance their feature expression. Finally, the fine-grained encoder-decoder uses both the image feature and the coarse text caption as input, and generates the final fine-grained caption after multi-modal information fusion. Experimental results on MSCOCO datasets show that our proposed method outperforms previous image caption methods and achieves a performance of 39.7 BLEU-4 score and 121.6 CIDEr-A score.

关键词： Image caption encoder-decoder structure Feature auto-enhancement

来源：评论

学校读者我要写书评

暂无评论

TCNet: tensor and covariance attention network for semantic segmentation

引用

SOFT COMPUTING 2024年第11-12期28卷 7575页

作者： Xu, Haixia Liu, Yanbang Wang, Wei Zhou, Wei Ding, Fanxun Han, Feng Peng, Wei Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Peoples R China Xiangtan Univ Sch Automat & Elect Informat Xiangtan Peoples R China

Non-local network provides a pioneering approach for capturing long-range dependency by aggregating query-specific global context into each query location;however, non-local network applies the identical weight to each channel of feature maps and ignores the differences from the different channels of features. We design a novel tensor attention module (TAM), which integrates the context information along spatial dimension and channel dimension by introducing a bias learnable parameters tensor, so that the feature at each location of each channel can aggregate the features from all other locations. Motivated by SE-Net, we propose a novel second-order covariance attention module (SCAM) to enhance the feature correlation between different channel maps through the second-order statistics and the local cross-channel interaction strategy. We take the encoder-decoder segmentation network DeepLabv3+ as baseline, and in the encoder develop the attention modules TAM and SCAM for semantic segmentation (TCNet). Experimental results on PASCAL VOC 2012 and Cityscapes datasets show that our proposed network has better performance than the other state-of-the-art segmentation networks.

关键词： Attention module TAM SCAM encoder-decoder structure

来源：评论

学校读者我要写书评

暂无评论

Predicting water quality in municipal water management systems using a hybrid deep learning model

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Luo, Wenxian Huang, Leijun Shu, Jiabin Feng, Hailin Guo, Wenjie Xia, Kai Fang, Kai Wang, Wei Zhejiang A&F Univ 666 Wusu St Hangzhou 311300 Zhejiang Peoples R China Quzhou Digital Rural Construct Ctr 139 Fushi Rd Quzhou 324000 Zhejiang Peoples R China Shenzhen MSU BIT Univ Artificial Intelligence Res Inst Guangdong Hong Kong Macao Joint Lab Emot Intellige Shenzhen 518172 Guangdong Peoples R China

Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non -stationary characteristics of water quality data of the municipal water resources, it is challenging to achieve high prediction accuracy, especially for medium -term and long-term predictions. To address this issue, we propose a novel hybrid deep learning model to predict water quality multiple steps ahead. The proposed model adopts the encoder-decoder structure in the form of two long short-term memory (LSTM) networks, integrated with the attention mechanism and a convolutional neural network (CNN). The model extracts the complex correlation between multiple water quality features through the CNN, and uses the two LSTM networks to transfer historical information to predictions, with an attention layer assigning different weights to the different parts of the historical information. Using three years of water quality data collected from an urban river, we experimentally show that the proposed model outperforms the baseline models by 11%-34% in root mean squared error (RMSE) when predicting dissolved oxygen multiple steps ahead, and by 1%-7% when predicting total phosphorus. Similar improvement has also been found in Nash-Sutcliffeefficiency (NSE) and mean absolute error (MAE). The proposed model is a feasible solution for multi -step medium -term water quality prediction.

关键词： Water quality Multi-step prediction encoder-decoder structure Long short-term memory Convolutional neural network Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

CAE-GAN: A hybrid model for vehicle trajectory prediction

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2022年第12期16卷 1682-1696页

作者： Chen, Long Zhou, Qiyang Cai, Yingfeng Wang, Hai Li, Yicheng Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang Jiangsu Peoples R China

Trajectory prediction of surrounding vehicles is a crucial capability of intelligent driving vehicles. In a scene, a vehicle and its surrounding vehicles constitute an integral system, and the vehicle's future motion trajectory is affected by the actions of surrounding vehicles. The influencing mode and degree are hidden in the relevant historical information of the vehicle and its neighbour vehicle. The existing trajectory prediction methods either do not consider the confidence of the predicted trajectory, or the accuracy requirement is ignored when considering the confidence of the predicted trajectory. In order to address this problem, a mixed Conditional Autoencoder Generative Adversarial Network (CAE-GAN) model based on the multi-loss function is proposed. The proposed model uses the encoder-decoder structure with a convolutional social pool to extract general features. The generative adversarial networks (GANs) are used to extract the confidence features of the generated trajectories, which enables the proposed model to generate trajectories that are close to the real trajectories. In addition, a classifier structure based on an LSTM network is added to output the probability that the predicted trajectory belongs to a particular lateral maneuver so that the generated trajectory lateral maneuver of the model is consistent with the real trajectory lateral maneuver. The proposed model is evaluated using the publicly available NGSIM US-101 and I-80 datasets, and results show that the accuracy of the proposed model is higher than that of the existing methods. The proposed model achieves an average accuracy improvement of 16.34% in comparison to the most advanced existing models.

关键词： convolutional social pool generative adversarial networks deep learning (artificial intelligence) trajectory prediction methods future motion trajectory I-80 dataset LSTM network road traffic intelligent driving vehicles NGSIM US-101 dataset multiloss function encoder-decoder structure recurrent neural nets neighbour vehicle generated trajectory lateral maneuver feature extraction vehicle trajectory prediction mixed conditional autoencoder generative adversarial network model CAE-GAN traffic engineering computing

来源：评论

学校读者我要写书评

暂无评论

Traffic Scene Captioning with Multi-Stage Feature Enhancement

引用

Computers, Materials & Continua 2023年第9期76卷 2901-2920页

作者： Dehai Zhang Yu Ma Qing Liu Haoxing Wang Anquan Ren Jiashu Liang School of Software Yunnan UniversityKunming650091China

Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images,ensuring road safety while providing an important decision-making function for sustainable *** order to provide a comprehensive and reasonable description of complex traffic scenes,a traffic scene semantic captioningmodel withmulti-stage feature enhancement is proposed in this *** general,the model follows an encoder-decoder ***,multilevel granularity visual features are used for feature enhancement during the encoding process,which enables the model to learn more detailed content in the traffic scene ***,the scene knowledge graph is applied to the decoding process,and the semantic features provided by the scene knowledge graph are used to enhance the features learned by the decoder again,so that themodel can learn the attributes of objects in the traffic scene and the relationships between objects to generate more reasonable *** paper reports extensive experiments on the challenging MS-COCO dataset,evaluated by five standard automatic evaluation metrics,and the results show that the proposed model has improved significantly in all metrics compared with the state-of-the-art methods,especially achieving a score of 129.0 on the CIDEr-D evaluation metric,which also indicates that the proposed model can effectively provide a more reasonable and comprehensive description of the traffic scene.

关键词： Traffic scene captioning sustainable transportation feature enhancement encoder-decoder structure multi-level granularity scene knowledge graph

来源：评论

学校读者我要写书评

暂无评论

A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling

引用

IEEE ACCESS 2024年 12卷 12919-12939页

作者： Wang, Min Huang, Li Yan, Jingke Huang, Jin Yang, Tao Sichuan Technol & Business Univ Coll Elect & Informat Engn Chengdu 611745 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

Crowd counting is an important research topic in the fields of computer vision and image processing, with monitoring and management of crowded scenes becoming an increasingly prominent issue. Existing methods still suffer from the problem of severe overlap in density maps within dense areas, leading to inadequate counting and localization accuracy. This paper presents innovative research on crowd counting and localization. Firstly, addressing the limitations of density maps in localization performance in existing algorithms, we optimize the generation method of FIDT maps, decoupling the counting and localization tasks. By avoiding the problem of overlap in dense areas, the optimized label maps achieve a good balance between counting accuracy and localization, with MAE and MSE reaching 64.1 and 103.9 in SHHA, and 10.9 and 17.4 in SHHB, ***, to address the scale insensitivity of the encoder and the potential loss of critical features during the encoding process, we propose the Adaptive Feature Fusion Module and the Multi-Scale Global Attention Upsampling Module, constructing the CALNET network. By reducing redundant features inside and outside the separable branch, the model achieves global fusion of shallow features during the decoding process. The F1-m scores obtained on the SHHA and SHHB datasets reach 72.9% and 79.4% respectively, significantly improving the model's ***, this paper extends the application of crowd counting and localization algorithms to different domains such as citrus orchards, vehicles, and campus crowds. Through experiments, the robustness and transferability of the network are validated, expanding the application areas of crowd counting and localization algorithms and providing a broader space for future research.

关键词： Label map encoder-decoder structure adaptive feature fusion multi-scale global attention upsampling crowd counting and localization

来源：评论

学校读者我要写书评

暂无评论

SMART: Supervised multi-class image retargeting generative model based on a long-range sampling strategy

引用

DIGITAL SIGNAL PROCESSING 2024年 154卷

作者： Cui, Jia Jiang, Hao Qi, Meng Gu, Zhenyu Lu, Hongju State Key Lab Subtrop Bldg Sci Guangzhou 510640 Peoples R China South China Univ Technol Sch Design Guangzhou 510006 Peoples R China HCruiser Informat sgesellschaft mbH D-80807 Munich Germany Shandong Normal Univ Sch Informat Sci & Engn Jinan 250300 Peoples R China Shanghai Jiao Tong Univ Sch Design Shanghai 205530 Peoples R China Guangzhou City Univ Technol Sch Management Guangzhou 510800 Peoples R China

Content-aware image retargeting (CAIR) techniques are crucial in multimedia processing for displaying images on various devices while preserving visually salient contents with desirable visual effects. There are discrete and continuous algorithms. For the former, the artefacts happen when the foreground proportion is larger than the retargeting ratio;for the latter, the salient regions are prone to be squeezed. In this paper, we reformulate the retargeting process into sampling the salient signal and reconstruction under aesthetic supervision, the supervised multi-class image retargeting reconstruction (SMART) framework. The target images can be represented into complementary parts, the masked and unmasked ones, according to the saliency influences in the encoder phrase. The long-range sampling algorithm is proposed to calculate similarities through an 8-connected planar path while considering spatial distance and feature correlation. The sampled embeddings in latent space reconstruct the retargeted images under supervised signals for aesthetic quality. The semantic loss Lsem from the pretrained CLIP model can maintain consistency for both content and semantics. The supervised loss, Lir, is introduced to ensure the retargeted qualities are close to the preferred labels. Then, we release a new retargeting dataset comprising seven image classes (animal, building, car, flower, indoor, landscape and people) with supervised labels collected from designers for further aesthetic retargeting study. The ablation studies are conducted to confirm the effectiveness of the new dataset, and comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

关键词： Image retargeting Long-range sampling Multi-class image reconstruction Supervised signals encoder-decoder structure

来源：评论

学校读者我要写书评

暂无评论

Learning Feature Embedding Refiner for Solving Vehicle Routing Problems

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15279-15291页

作者： Li, Jingwen Ma, Yining Cao, Zhiguang Wu, Yaoxin Song, Wen Zhang, Jie Chee, Yeow Meng Sichuan Normal Univ Dept Comp Sci Chengdu 610101 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Coll Design & Engn Singapore 119077 Singapore Singapore Management Univ Sch Comp & Informat Syst Singapore 188065 Singapore Eindhoven Univ Technol Fac Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

While the encoder-decoder structure is widely used in the recent neural construction methods for learning to solve vehicle routing problems (VRPs), they are less effective in searching solutions due to deterministic feature embeddings and deterministic probability distributions. In this article, we propose the feature embedding refiner (FER) with a novel and generic encoder-refiner-decoder structure to boost the existing encoder-decoder structured deep models. It is model-agnostic that the encoder and the decoder can be from any pretrained neural construction method. Regarding the introduced refiner network, we design its architecture by combining the standard gated recurrent units (GRU) cell with two new layers, i.e., an accumulated graph attention (AGA) layer and a gated nonlinear (GNL) layer. The former extracts dynamic graph topological information of historical solutions stored in a diversified solution pool to generate aggregated pool embeddings that are further improved by the GRU, and the latter adaptively refines the feature embeddings from the encoder with the guidance of the improved pool embeddings. To this end, our FER allows current neural construction methods to not only iteratively refine the feature embeddings for boarder search range but also dynamically update the probability distributions for more diverse search. We apply FER to two prevailing neural construction methods including attention model (AM) and policy optimization with multiple optima (POMO) to solve the traveling salesman problem (TSP) and the capacitated VRP (CVRP). Experimental results show that our method achieves lower gaps and better generalization than the original ones and also exhibits competitive performance to the state-of-the-art neural improvement methods.

关键词： Probability distribution Decoding Search problems Vehicle routing Routing Logic gates Costs encoder-decoder structure neural combinatorial optimization reinforcement learning vehicle routing problems (VRPs)

来源：评论

学校读者我要写书评

暂无评论

Efficient real-time semantic segmentation: accelerating accuracy with fast non-local attention

引用

VISUAL COMPUTER 2024年第8期40卷 5783-5796页

作者： Lan, Tianye Dou, Furong Feng, Ziliang Zhang, Chengfang Sichuan Univ Coll Comp Sci Chengdu Peoples R China Sichuan Police Coll Intelligent Policing Key Lab Sichuan Prov Luzhou Peoples R China

As an essential aspect of semantic segmentation, real-time semantic segmentation poses significant challenge in achieving trade-off between segmentation accuracy and inference speed. Standard non-local block can effectively capture the long-range dependencies that are critical to semantic segmentation, while its huge computational cost is unacceptable for real-time semantic segmentation. To confront this issue, we propose fast non-local attention network (FNANet) with encoder-decoder structure for real-time semantic segmentation. FNANet relies on the utilization of fast non-local attention module and fast non-local attention fusion module. These modules serve the dual purpose of reducing computational demands and capturing essential contextual information, thereby achieving an equilibrium between enhanced segmentation accuracy and minimized computational overhead. Furthermore, improved non-local attention is incorporated to augment feature representation, consequently facilitating precise class label prediction. Experimental results demonstrate that FNANet outperforms state-of-the-art methods in terms of segmentation accuracy and speed on Cityscapes and CamVid.

关键词： Semantic segmentation Fast non-local attention Attentional feature fusion Real-time speed encoder-decoder structure

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：