检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

3,059 篇 期刊文献
2,674 篇 会议
4 册 图书

馆藏范围

5,737 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,728 篇 工学
- 2,391 篇 计算机科学与技术...
- 1,969 篇 软件工程
- 860 篇 信息与通信工程
- 454 篇 控制科学与工程
- 453 篇 电气工程
- 440 篇 生物工程
- 322 篇 电子科学与技术（可...
- 296 篇 化学工程与技术
- 256 篇 生物医学工程（可授...
- 253 篇 机械工程
- 233 篇 光学工程
- 184 篇 材料科学与工程（可...
- 169 篇 动力工程及工程热...
- 167 篇 交通运输工程
- 149 篇 仪器科学与技术
- 145 篇 建筑学
- 139 篇 土木工程
2,080 篇 理学
- 1,022 篇 数学
- 537 篇 物理学
- 525 篇 生物学
- 332 篇 统计学（可授理学、...
- 312 篇 化学
- 148 篇 系统科学
1,069 篇 管理学
- 632 篇 管理科学与工程(可...
- 489 篇 图书情报与档案管...
- 228 篇 工商管理
301 篇 医学
- 245 篇 临床医学
- 159 篇 基础医学(可授医学...
177 篇 法学
- 144 篇 社会学
150 篇 农学
116 篇 经济学
- 116 篇 应用经济学
48 篇 教育学
16 篇 文学
12 篇 军事学
6 篇 艺术学
1 篇 历史学

主题

181 篇 semantics
156 篇 feature extracti...
139 篇 deep learning
135 篇 training
97 篇 computational mo...
85 篇 data models
76 篇 convolution
76 篇 machine learning
75 篇 data mining
70 篇 predictive model...
69 篇 accuracy
61 篇 neural networks
58 篇 object detection
56 篇 visualization
54 篇 federated learni...
53 篇 big data
53 篇 optimization
51 篇 contrastive lear...
50 篇 graph neural net...
50 篇 adaptation model...

机构

166 篇 school of inform...
147 篇 key laboratory o...
132 篇 school of econom...
96 篇 state key labora...
94 篇 school of comput...
82 篇 fujian provincia...
78 篇 henan key labora...
73 篇 college of big d...
73 篇 school of comput...
73 篇 research center ...
73 篇 school of comput...
71 篇 university of ch...
68 篇 guangdong key la...
60 篇 hubei key labora...
53 篇 shandong provinc...
53 篇 key laboratory o...
53 篇 shenzhen researc...
52 篇 shandong enginee...
51 篇 school of softwa...
49 篇 school of inform...

作者

47 篇 shi yong
41 篇 zhang rui
41 篇 yong shi
39 篇 pan jeng-shyang
37 篇 chang kuo-chi
35 篇 wu xindong
35 篇 chen hong
34 篇 wang meng
33 篇 liu yang
33 篇 xue xingsi
33 篇 du xiaoyong
32 篇 liu jun
31 篇 li cuiping
31 篇 zhang jing
30 篇 xindong wu
29 篇 wen ji-rong
29 篇 huang qingming
28 篇 chu kai-chun
25 篇 niyato dusit
25 篇 hong chen

语言

4,718 篇 英文
913 篇 其他
128 篇 中文
4 篇 德文
4 篇 法文

检索条件"机构=The Key Laboratory of Knowledge Engineering with Big Data"

共 5737 条记录，以下是811-820 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment Analysis 24

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

引用

24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Dang, Lanxue Wang, Chunyu Han, Hongyu Hou, Yan-E Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9798350319934

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis

An Interactive Attention Mechanism Fusion Network for Aspect...

引用

2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Jinghong Gao, Yuan Li, Haokang College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang050000 China

ISBN: (纸本)9798350303780

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-view Vision Transformer for Driver Action Recognition 6th

Multi-view Vision Transformer for Driver Action Recognition

引用

6th International Conference on Intelligent Transportation engineering, ICITE 2021

作者： Shan, Guangwei Ji, Qingge Xie, Yuguang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9789811922589

Distracted driving causes many accidents every year, most of which can be avoided with automatic recognition. As a result, vision-based driver action recognition is receiving increasing research attention. In a limited in-vehicle space, actions can be very ambiguous from an individual view. Therefore exploring efficient multi-view action recognition architecture is meaningful. This study aims to detect the distraction of drivers while identifying the cause. A novel driver action recognition architecture named multi-view vision transformer (MVVT) is proposed, which combines classical convolutional neural networks (CNNs) with vision transformer. Self-attention mechanism is utilized to dynamically aggregate temporal information and fuse features of different views jointly. Experiments demonstrate that MVVT can effectively recognize drivers’ behaviors with multi-view input. A promising result of 84.9% accuracy is achieved on a large public driver action dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

In-Line Wideband Waveguide Bandpass Filters Using Hybrid Irises and Non-Uniform Resonators with Over-Octave Spurious Suppression

In-Line Wideband Waveguide Bandpass Filters Using Hybrid Iri...

引用

2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Xu, Zhihong Li, Jin Chen, Sicheng Yuan, Tao Guangdong Prov. Mobile Terminal Microwave and Millimeter-Wave Antenna Engineering Research Center College of Electronics and Information Engineering Shenzhen University China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China State Key Laboratory of Millimeter Waves China Shenzhen University ATR National Key Laboratory of Defense Technology College of Electronics and Information Engineering China

ISBN: (纸本)9798350347647

This paper reports on a geometrical-shaping-based resonator design approach for in-line wideband rectangular waveguide bandpass filters (BPFs) aiming to improve the spurious suppression. The rectangular resonator is shaped with capacitive and inductive irises and the shaped resonator features irregular boundaries with the first spurious mode spectrally over an octave away from the fundamental mode. X-band 13%-fractional-bandwidth five-pole BPFs composed of uniform capacitive-iris-loaded resonators (filter I) and non-uniform hybrid-iris-loaded resonators (filter II) are developed. The shaping and non-uniform configuration of the resonators contribute to significantly improved stopband performance of the filter. The filter prototypes are manufactured additively and monolithically by incorporating Polyjet 3-D printing and electroless copper plating processes. The 3-D printed filters demonstrate excellent passband performance with the measured return loss and insertion loss of over 20 dB and lower than 0.3 dB, respectively. The first spurious passband appears at over 1.1× and 1.5× octaves away from the fundamental-mode passband for the filter I and filter II, respectively. © 2023 IEEE.

关键词： Bandpass filters

来源：评论

学校读者我要写书评

暂无评论

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusion Networks

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusi...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jin, Yi Wang, Xu Hu, Mengxia Yu, Hui Li, Yidong Wang, Tao Feng, Songhe Lang, Congyan State Key Laboratory of Intelligent Vehicle Safety Technology China School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing China China Automotive Engineering Research Institute Co. Ltd. China School of Psychology & Neuroscience University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781665410205

Task-oriented point cloud sampling methods have attracted considerable attention for their ability to adaptively select important point sets based on downstream tasks, achieving an excellent balance between data simplification and task performance. However, existing task-oriented sampling models, primarily based on single-branch designs, struggle to fully extract features from input point clouds that comprehensively reflect multi-dimensional key information, thus limiting their sampling performance. In this paper, we introduce a dual-branch sampling network, named DBS-NET, which conducts crucial point sampling from both the global and local importance perspectives separately before merging them, thereby preserving multi-dimensional key information of the input data during the sampling process. Qualitative and quantitative experimental results demonstrate the competitive performance of DBS-NET on the classification benchmark task. © 2024 IEEE.

关键词： Importance sampling

来源：评论

学校读者我要写书评

暂无评论

A Fully 3-D-Printing-Compatible E-Plane Elliptical Waveguide Junction for Power Dividing/Combining Applications

A Fully 3-D-Printing-Compatible E-Plane Elliptical Waveguide...

引用

2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Chen, Sicheng Li, Jin Yuan, Tao Guangdong Prov. Mobile Terminal Microwave and Millimeter-Wave Antenna Engineering Research Center College of Electronics and Information Engineering Shenzhen University China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China State Key Laboratory of Millimeter Waves China College of Electronics and Information Engineering ATR National Key Laboratory of Defense Technology Shenzhen University China

ISBN: (纸本)9798350347647

In this paper, a new class of 3-D-printing-compatible E-plane elliptical waveguide (EWG) junction is proposed for wideband power dividers/combiners. The junction is based on an EWG architecture with tailored power dividing and impedance-matching structures that are configured in a Y-shape profile. The junction is devised according to a specified orientation for 3-D printing and it features smooth and intrinsically self-supportable waveguide sidewalls. Compared to conventional rectangular waveguide junctions, the proposed EWG junction exhibits dramatically enhanced structural compatibility with additive manufacturing technology. A novel four-way EWG-junction-based power divider is demonstrated at Ka band. The power divider is prototyped monolithically by incorporating Polyjet 3-D printing and electroless copper plating processes. RF performance of the power divider shows a return loss of mostly over 20 dB, transmission coefficients of averagely -6.5 dB, and small amplitude and phase imbalances across the entire Ka band, successfully validating the wideband and low-loss characteristics of the proposed EWG junction. © 2023 IEEE.

关键词： Voltage dividers

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Image Semantic Segmentation Based on Fusion of Transformer and Lightweight Deeplabv3+

Remote Sensing Image Semantic Segmentation Based on Fusion o...

引用

International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Cao, Yuanyang Chen, Jian Xue, Zhentao Zhang, Zichao Hu, Gui College of Engineering China Agricultural University Beijing China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNR Shanghai China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijng China

ISBN: (纸本)9789811966125

Aiming at the problems that the classification of remote sensing image datasets is not balanced so that the accuracy of semantic segmentation is not high and the segmentation effect of scarce samples is poor. Transformer encoder with multihead self-attention mechanism is integrated into Deeplabv3+ network, and the attention mechanism is used to enhance the ability to capture global information, so as to improve the precision requirement of scarce categories in remote sensing semantic segmentation task. The algorithm firstly uniformly crops high-resolution remote sensing images into low-resolution image information for batch training and reducing information loss. Secondly, MobileNetV2, a lightweight backbone feature extraction network, is used to replace Xception network of Deeplabv3+. Finally, Transformer encoder based on multihead self-attention mechanism is connected in series with atrous spatial pyramid pooling (ASPP) of Deeplabv3+ encoder region to increase its segmentation precision by enhancing feature learning of scarce category samples in deep feature information. The experimental result shows that the proposed model called TransDeeplabv3+ gets 67.67% mIoU and 81.86% mPA on GID remote sensing dataset. Compared with Deeplabv3+ model, mIoU and mPA are improved by 9.46% and 8.52% respectively. TransDeeplabv3+ can effectively increase the precision because of the ability of increasing attention to the scarce species samples and solve the problem of the decrease of segmentation accuracy due to unbalanced data categories. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

END-TO-END GLOBAL SEGMENTATION OF POLSAR IMAGES WITH data AUGMENTATION

END-TO-END GLOBAL SEGMENTATION OF POLSAR IMAGES WITH DATA AU...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wang, Zehua Wang, Zezhong Qiu, Xiaolan Zhang, Zhe Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou China Suzhou Aerospace Information Research Institute Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Deep neural networks have demonstrated impressive performance in PolSAR image classification. However, the lack of labelled data and the complexity of land boundaries hindering the model's ability to achieve high-quality classification results. In order to address these inherent challenges, this paper introduces an augmentation method designed to maximize the utility of available labelled data. Furthermore, we present an end-to-end PolSAR image classification network, which we refer to as PolSARMixer. Our experimental results indicate that our proposed methodology significantly streamlines the classification process for PolSAR images when compared to traditional methods. Moreover, our proposed network exhibits superior performance in terms of classification accuracy and efficiency. © The Institution of engineering & Technology 2023.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

IKGN: Intention-aware knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

引用

IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 78 79 80 81 82 83 84 85 86 87 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：