检索结果-南通市图书馆

attention-based efficient robot grasp detection network

Frontiers of Information Technology & Electronic Engineering 2023年第10期24卷 1430-1444页

作者： Xiaofei QIN Wenkai HU Chen XIAO Changxiang HE Songwen PEI Xuedian ZHANG School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China College of Science University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System Shanghai 200093China Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education Shanghai 200093China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201210China

To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic information.Several lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a collision.This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers.We design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp configurations.AEGDN achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,respectively.The inference speed reaches 43.5 frames per second with only about 1.2×10^(6)parameters.The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well.Codes are available at https://***/robvincen/robot_gradet.

关键词： Robot grasp detection attention mechanism Encoder-decoder Neural network

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Leveraging Structured Information from a Passage to Generate Questions

引用

Tsinghua Science and Technology 2023年第3期28卷 464-474页

作者： Jian Xu Yu Sun Jianhou Gan Mingtao Zhou Di Wu Key Laboratory of Educational Informatization for Nationalities Yunnan Normal UniversityKunming 650500China School of Information Science and Technology Yunnan Normal UniversityKunming 650500China Yunnan Key Laboratory of Smart Education Yunnan Normal UniversityKunming 650500China School of Information Engineering Qujing Normal UniversityQujing 655011China

Question Generation(QG)is the task of utilizing Artificial Intelligence(AI)technology to generate questions that can be answered by a span of text within a given passage.Existing research on QG in the educational field struggles with two challenges:the mainstream QG models based on seq-to-seq fail to utilize the structured information from the passage;the other is the lack of specialized educational QG datasets.To address the challenges,a specialized QG dataset,reading comprehension dataset from examinations for QG(named RACE4QG),is reconstructed by applying a new answer tagging approach and a data-filtering strategy to the RACE dataset.Further,an end-to-end QG model,which can exploit the intra-and inter-sentence information to generate better questions,is proposed.In our model,the encoder utilizes a Gated Recurrent Units(GRU)network,which takes the concatenation of word embedding,answer tagging,and Graph attention neTworks(GAT)embedding as input.The hidden states of the GRU are operated with a gated self-attention to obtain the final passage-answer representation,which will be fed to the decoder.Results show that our model outperforms baselines on automatic metrics and human evaluation.Consequently,the model improves the baseline by 0.44,1.32,and 1.34 on BLEU-4,ROUGE-L,and METEOR metrics,respectively,indicating the effectivity and reliability of our model.Its gap with human expectations also reflects the research potential.

关键词： automatic Question Generation(QG) RACE4QG dataset Answer-Oriented GAT(AO-GAT) attention mechanism structured information

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Effective ensemble learning approach for SST field prediction using attention-based PredRNN

引用

Frontiers of Computer Science 2023年第1期17卷 143-155页

作者： Baiyou QIAO Zhongqiang WU Ling MA Yicheng Zhou Yunjiao SUN School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang 110169China

Accurate prediction of sea surface temperature (SST) is extremely important for forecasting oceanic environmental events and for ocean studies. However, the existing SST prediction methods do not consider the seasonal periodicity and abnormal fluctuation characteristics of SST or the importance of historical SST data from different times;thus, these methods suffer from low prediction accuracy. To solve this problem, we comprehensively consider the effects of seasonal periodicity and abnormal fluctuation characteristics of SST data, as well as the influence of historical data in different periods, on prediction accuracy. We propose a novel ensemble learning approach that combines the Predictive Recurrent Neural Network(PredRNN) network and an attention mechanism for effective SST field prediction. In this approach, the XGBoost model is used to learn the long-period fluctuation law of SST and to extract seasonal periodic features from SST data. The exponential smoothing method is used to mitigate the impact of severely abnormal SST fluctuations and extract the a priori features of SST data. The outputs of the two aforementioned models and the original SST data are stacked and used as inputs for the next model, the PredRNN network. PredRNN is the most recently developed spatiotemporal deep learning network, which simulates both spatial and temporal representations and is capable of transferring memory across layers and time steps. Therefore, we used it to extract the spatiotemporal correlations of SST data and predict future SSTs. Finally, an attention mechanism is added to capture the importance of different historical SST data, weigh the output of each step of the PredRNN network, and improve the prediction accuracy. The experimental results on two ocean datasets confirm that the proposed approach achieves higher training efficiency and prediction accuracy than the existing SST field prediction approaches do.

关键词： SST prediction ensemble learning XGBoost PredRNN attention mechanism

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Dynamic interwell connectivity analysis of multi-layer water fl ooding reservoirs based on an improved graph neural network

引用

PETROLEUM SCIENCE 2024年第2期21卷 1062-1080页

作者： Huang, Zhao-Qin Wang, Zhao-Xu Hu, Hui-Fang Zhang, Shi-Ming Liang, Yong -Xing Guo, Qi Yao, Jun China Univ Petr East China Sch Petr Engn Qingdao 266580 Shandong Peoples R China SINOPEC Explorat & Dev Res Inst Shengli Oil Field Dongying 257015 Shandong Peoples R China

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi -layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection -production wells with limited field data. The three-dimensional well pattern of multi -layer reservoir and the relationship between injection -production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection -production wells by attention mechanism. Based on the material balance and physical information, the overall connectivity from the injection wells, through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection -production relationship of the reservoir and the development of the remaining oil. (c) 2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-

关键词： Graph neural network Dynamic interwell connectivity Production -injection splitting attention mechanism Multi -layer reservoir NUMERICAL-SIMULATION MODEL FIELD

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Crop Disease Recognition Based on Improved Model-Agnostic Meta-Learning

引用

Computers, Materials & Continua 2023年第6期75卷 6101-6118页

作者： Xiuli Si Biao Hong Yuanhui Hu Lidong Chu College of Information Technology Jilin Agricultural UniversityChangchun130118China College of Engineering and Technology Jilin Agricultural UniversityChangchun130118China

Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-attention model.Compared to the other few-shot methods,the proposed MAML-attention performs better.

关键词： Meta-learning disease image recognition deep learning attention mechanism

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A new target tracking filter based on deep learning

引用

Chinese Journal of Aeronautics 2022年第5期35卷 11-24页

作者： Yaqi CUI You HE Tiantian TANG Yu LIU Naval Aviation University Yantai 264001China

At present,current filters can basically solve the filtering problem in target tracking,but there are still many problems such as too many filtering variants,too many filtering forms,loosely coupled with the target motion model,and so on.To solve the above problems,we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters.We firstly analyze the computation graphs of typical a-βand Kalman.Through analysis,it is concluded that a-βand Kalman have the same computation structures analogous to a typical recurrent neural network and can be considered as a kind of recurrent neural network with constrained weights.Then,given this and considering that a recurrent neural network has the recognition capability for target motion patterns,a new filter is developed in a unified neural network architecture and specifically constructed using feedforward neural network,recurrent neural network,and attention mechanism.And the unified tracking filter proposed in this paper can generate three aspects of unity:a unified target motion model,an adaptive filter method,and an overall track filtering framework.Finally,Simulation results show that the proposed filter is effective and useful,of which the overall performance is superior to those of compared filters.

关键词： attention mechanism Feedforward neural network Interactive multiple model Recurrent neural network Tracking filter

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Combining Residual attention mechanisms and Generative Adversarial Networks for Hippocampus Segmentation

引用

Tsinghua Science and Technology 2022年第1期27卷 68-78页

作者： Hongxia Deng Yuefang Zhang Ran Li Chunxiang Hu Zijian Feng Haifang Li the School of Information and Computer Taiyuan University of TechnologyTaiyuan 030600China

This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus.Different convolutional configurations were proposed to capture information obtained by a segmentation network.In addition,a generative adversarial network based on Pixel2Pixel was proposed.The generator was a codec structure combining a residual network and an attention mechanism to capture detailed information.The discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and that of the expert.Through the continuously transmitted losses of the generator and discriminator,the generator reached the optimal state of hippocampus segmentation.T1-weighted magnetic resonance imaging scans and related hippocampus labels of 130 healthy subjects from the Alzheimer’s disease Neuroimaging Initiative dataset were used as training and test data;similarity coefficient,sensitivity,and positive predictive value were used as evaluation indicators.Results showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases,such as Alzheimer’s disease.

关键词： magnetic resonance imaging generative adversarial network residual network attention mechanism

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

SUNet++: A Deep Network with Channel attention for Small-Scale Object Segmentation on 3D Medical Images

引用

清华大学学报:自然科学版(英文版) 2023年第4期28卷 628-638页

作者： Lan Zhang Kejia Zhang Haiwei Pan College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China

As a deep learning network with an encoder-decoder architecture,UNet and its series of improved versions have been widely used in medical image segmentation with great applications.However,when used to segment targets in 3D medical images such as magnetic resonance imaging(MRI),computed tomography(CT),these models do not model the relevance of images in vertical space,resulting in poor accurate analysis of consecutive slices of the same patient.On the other hand,the large amount of detail lost during the encoding process makes these models incapable of segmenting small-scale tumor targets.Aiming at the scene of small-scale target segmentation in 3D medical images,a fully new neural network model SUNet++is proposed on the basis of UNet and UNet++.SUNet++improves the existing models mainly in three aspects:1)the modeling strategy of slice superposition is used to thoroughly excavate the three dimensional information of the data;2)by adding an attention mechanism during the decoding process,small scale targets in the picture are retained and amplified;3)in the up-sampling process,the transposed convolution operation is used to further enhance the effect of the model.In order to verify the effect of the model,we collected and produced a dataset of hyperintensity MRI liver-stage images containing over 400 cases of liver nodules.Experimental results on both public and proprietary datasets demonstrate the superiority of SUNet++in small-scale target segmentation of three-dimensional medical images.

关键词： 3D medical images small-scale target segmentation attention mechanism

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model

引用

Journal of Integrative Agriculture 2023年第6期22卷 1671-1683页

作者： SONG Chao-yu ZHANG Fan LI Jian-sheng XIE Jin-yi YANG Chen ZHOU Hang ZHANG Jun-xiong College of Engineering China Agricultural UniversityBeijing 100083P.R.China College of Agronomy and Biotechnology China Agricultural UniversityBeijing 100083P.R.China

Maize tassel detection is essential for future agronomic management in maize planting and breeding,with application in yield estimation,growth monitoring,intelligent picking,and disease detection.However,detecting maize tassels in the field poses prominent challenges as they are often obscured by widespread occlusions and differ in size and morphological color at different growth stages.This study proposes the SEYOLOX-tiny Model that more accurately and robustly detects maize tassels in the field.Firstly,the data acquisition method ensures the balance between the image quality and image acquisition efficiency and obtains maize tassel images from different periods to enrich the dataset by unmanned aerial vehicle(UAV).Moreover,the robust detection network extends YOLOX by embedding an attention mechanism to realize the extraction of critical features and suppressing the noise caused by adverse factors(e.g.,occlusions and overlaps),which could be more suitable and robust for operation in complex natural environments.Experimental results verify the research hypothesis and show a mean average precision(mAP_(@0.5)) of 95.0%.The mAP_(@0.5),mAP_(@0.5-0.95),mAP_(@0.5-0.95(area=small)),and mAP_(@0.5-0.95(area=medium)) average values increased by 1.5,1.8,5.3,and 1.7%,respectively,compared to the original model.The proposed method can effectively meet the precision and robustness requirements of the vision system in maize tassel detection.

关键词： maize tassel detection remote sensing deep learning attention mechanism

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

引用

Chinese Journal of Chemical Engineering 2023年第4期56卷 1-14页

作者： Shanwei Xiong Li Zhou Yiyang Dai Xu Ji School of Chemical Engineering Sichuan UniversityChengdu 610065China

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

关键词： Safety Fault diagnosis Process systems Long short-term memory attention mechanism Neural networks

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：