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

3,534 篇 会议
2,549 篇 期刊文献
1 册 图书

馆藏范围

6,084 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,100 篇 工学
- 2,830 篇 计算机科学与技术...
- 2,301 篇 软件工程
- 1,050 篇 信息与通信工程
- 515 篇 电气工程
- 472 篇 生物工程
- 468 篇 控制科学与工程
- 458 篇 电子科学与技术（可...
- 383 篇 光学工程
- 298 篇 生物医学工程（可授...
- 284 篇 机械工程
- 256 篇 化学工程与技术
- 202 篇 仪器科学与技术
- 137 篇 动力工程及工程热...
- 137 篇 网络空间安全
- 134 篇 材料科学与工程（可...
- 110 篇 建筑学
- 106 篇 交通运输工程
2,210 篇 理学
- 1,192 篇 数学
- 661 篇 物理学
- 525 篇 生物学
- 363 篇 统计学（可授理学、...
- 270 篇 化学
- 166 篇 系统科学
1,065 篇 管理学
- 567 篇 图书情报与档案管...
- 538 篇 管理科学与工程(可...
- 191 篇 工商管理
249 篇 医学
- 211 篇 临床医学
- 186 篇 基础医学(可授医学...
- 117 篇 药学(可授医学、理...
148 篇 法学
- 117 篇 社会学
81 篇 教育学
76 篇 农学
51 篇 经济学
21 篇 军事学
18 篇 文学
11 篇 艺术学
5 篇 哲学

主题

262 篇 feature extracti...
234 篇 semantics
164 篇 training
131 篇 accuracy
121 篇 computational mo...
111 篇 deep learning
105 篇 convolution
87 篇 data mining
84 篇 image segmentati...
78 篇 object detection
78 篇 data models
76 篇 visualization
70 篇 contrastive lear...
70 篇 robustness
70 篇 convolutional ne...
66 篇 optimization
64 篇 predictive model...
63 篇 neural networks
61 篇 federated learni...
59 篇 face recognition

机构

210 篇 shandong provinc...
192 篇 key laboratory o...
187 篇 hubei province k...
143 篇 key laboratory o...
141 篇 school of comput...
141 篇 school of inform...
116 篇 college of compu...
104 篇 provincial key l...
99 篇 fujian provincia...
85 篇 shandong provinc...
76 篇 national key lab...
74 篇 university of ch...
73 篇 peng cheng labor...
71 篇 shandong provinc...
63 篇 school of comput...
62 篇 hubei province k...
62 篇 shanghai key lab...
60 篇 shanghai key lab...
52 篇 fujian provincia...
52 篇 school of comput...

作者

64 篇 li zuoyong
56 篇 huang qingming
52 篇 zhaohui li
51 篇 li zhaohui
46 篇 li chenglong
44 篇 zhang fuquan
34 篇 tang jin
33 篇 li wei
32 篇 fan li
32 篇 shen linlin
31 篇 guo di
31 篇 wang wei
30 篇 zuoyong li
30 篇 boxin shi
29 篇 feng rui
28 篇 yang zhiyong
27 篇 wang xiao
25 篇 liu yang
25 篇 gao guanglai
25 篇 li fan

语言

5,516 篇 英文
432 篇 其他
145 篇 中文
1 篇 德文
1 篇 法文

检索条件"机构=Provincial Key Laboratory of Computer Information Processing Technology"

共 6084 条记录，以下是801-810 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：

Optical scanning approach to image processing and holography

Optical scanning approach to image processing and holography

引用

Digital Optical Technologies 2023

作者： Poon, Ting-Chung Zhang, Yaping Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Yunnan Kunming650500 China Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

Optical scanning approach to image processing has advantages over frequency-plane architecture in terms of accuracy and flexibility. We will review an optical scanning approach to image processing that includes the ca... 详细信息

ISBN: (数字)9781510664586

ISBN: (纸本)9781510664579

关键词： Holography

来源：评论

学校读者我要写书评

暂无评论

Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding 9

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yu, Yonghao Guo, Yongde Zhao, Dawei Fu, Kexue Tang, Yongwei Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong University School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Jinan China

ISBN: (纸本)9798350391367

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning. © 2024 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Dark-pulse Microcombs in Integrated Chalcogenide Microresonators

Dark-pulse Microcombs in Integrated Chalcogenide Microresona...

引用

2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Xia, Di Wu, Jiayue Wang, Zifu Li, Yufei Zhao, Jiaxin Luo, Liyang Liu, Dong Yang, Shuixian Zhang, Bin Li, Zhaohui Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information Technology Sun Yat-sen University Guangzhou510275 China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou510275 China

We demonstrate dark-pulse microcombs generated in an integrated GeSbS microresonator with low pumping power of the ten-milliwatt level. Benefiting from both the high Q-factor and nonlinearity of the microresonator, hi... 详细信息

关键词： Microresonators

来源：评论

学校读者我要写书评

暂无评论

Robust Remote Sensing Scene Classification with Multi-view Voting and Entropy Ranking 1

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Resonant Mode Recognition with Convolutional Neural Network 15

Resonant Mode Recognition with Convolutional Neural Network

引用

15th International Conference on Microwave and Millimeter Wave technology, ICMMT 2023

作者： Xiao, Yuqi Lu, Kai Leung, Kwok Wa Leung, Chi-Sing City University of Hong Kong Department of Electrical Engineering Hong Kong Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350338874

This research proposes a simple approach to recognize resonate modes in rectangular dielectric resonator antennas (DRAs) with machine learning (ML) techniques. The proposed method utilizes a convolutional neural network (CNN) with a focal loss function to classify various modes present in the antenna. DRAs with different dimensions are simulated with ANSYS HFSS to obtain their electric fields at various resonant frequencies. The resulting dataset comprises 700 fields, manually labeled according to their corresponding resonant modes. After training the CNN model using this dataset, it is capable of accurately classifying 12 low-order resonant modes in rectangular DRAs. The performance of the model is evaluated using a test set, where it achieves a recognition accuracy of 97.8%. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning 39

GNN-Transformer Cooperative Architecture for Trustworthy Gra...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Jianqing Wei, Xinkai Chen, Min Wang, Zhiqiang Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan 030006 China

ISBN: (纸本)157735897X

Graph contrastive learning (GCL) has become a hot topic in the field of graph representation learning. In contrast to traditional supervised learning relying on a large number of labels, GCL exploits augmentation strategies to generate multiple views and positive/negative pairs, both of which greatly influence the performance. Unfortunately, commonly used random augmentations may disturb the underlying semantics of graphs. Moreover, traditional GNNs, a type of widely employed encoders in GCL, are inevitably confronted with over-smoothing and over-squashing problems. To address these issues, we propose GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning (GTCA), which inherits the advantages of both GNN and Transformer, incorporating graph topology to obtain comprehensive graph representations. Theoretical analysis verifies the trustworthiness of the proposed method. Extensive experiments on benchmark datasets demonstrate state-of-the-art empirical performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Influence Maximization With Graph Neural Network in Multi-Feature Social Network 24

Influence Maximization With Graph Neural Network in Multi-Fe...

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： Wang, Yuhao Li, Peng Huang, Weiyi School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

The classical influence maximization (IM) prob-lem aims to select a set of nodes as seed nodes for information propagation. Most related studies focus on one-dimensional information dissemination. However, the diffusion of information should be a multi-dimensional (or multi-feature) diffusion process. The user's judgment of information often depends on the combined judgment of multiple-dimensional information. Based on this, we consider a multi-feature diffusion model. In-formation will be decomposed into multi-dimensional features for diffusion. The user will receive these features and integrate them into a complete message. In addition, we consider seed node selection as a network node classification task. Consid-ering that social networks satisfy a graph structure, we use the node classification technique of graph neural networks. This technique classifies the nodes by the network topology to find the set of influential nodes. We propose a multi-feature influence (MFI) algorithm to improve the performance of the prediction. The set of candidate nodes obtained by classification is used as a benchmark to find the set of nodes with maximum influence. Our experiment results on real datasets show that the proposed algorithm has better algorithmic efficiency than the traditional greedy algorithm and better effectiveness than similar heuristic algorithms. © 2022 IEEE.

关键词： information dissemination

来源：评论

学校读者我要写书评

暂无评论

Class Semantic Attribute Perception Guided Zero-Shot Learning 39

Class Semantic Attribute Perception Guided Zero-Shot Learnin...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yue, Qin Cui, Junbiao Liang, Jianqing Bai, Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan 030006 China

ISBN: (纸本)157735897X

Deep learning has achieved remarkable success in supervised image classification tasks, which relies on a large number of labeled samples for each class. Recently, zero-shot learning has garnered significant attention, which aims to recognize unseen classes using only training samples from seen classes. To bridge the gap between images and classes, class semantic attributes are introduced, making the alignment between image and class semantic attributes critical to zero-shot learning. However, existing methods often struggle to accurately focus on the image regions corresponding to individual class semantic attributes and tend to overlook the relations between different regions of an image, leading to poor alignment. To address these challenges, we propose a class semantic attribute perception guided zero-shot learning method. Specifically, we achieve coarse-grained perception of class semantic attributes across the entire image through contrastive semantic learning. Additionally, we attain fine-grained perception of individual class semantic attributes within image regions via region partitioning-based attribute alignment, which fully considers the relations between different regions of an image. By integrating these two processes into a unified network, we achieve multi-grained class semantic attribute perception, thereby enhancing the alignment between images and class semantic attributes. We validate the effectiveness of the proposed method on zero-shot learning benchmark data sets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

GrayINT – Detection and Localization of Gray Failures via Hybrid In-band Network Telemetry 24

GrayINT – Detection and Localization of Gray Failures via H...

引用

24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Zhang, Kuichao Su, Wei Shi, Huiling Zhang, Kai Zhang, Wei Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Electronics and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9788995004395

As the underlying infrastructure, data center networks (DCNs) provide an important platform for cloud computing and next-generation networks. However, due to the influence of many factors, such as network, hardware, and software, various failures may occur during the operation of the DCN, which brings great challenges to its reliability. Particularly, a more insidious type of network failure, known as gray failure, presents a significant threat to system stability. In response to this challenge, in this work, we propose GrayINT, a gray failure detection and localization system that leverages In-band Network Telemetry (INT) technology to monitor all paths within the data center network in real-time. To verify the feasibility of our design, we build a fat-tree data center network on a virtual network testbed. A large number of experimental results demonstrate that our system can monitor device and link status in real-time, achieving gray failure detection and localization. Copyright 2023 KICS.

关键词： Next generation networks

来源：评论

学校读者我要写书评

暂无评论

Wideband Planar Glass Antenna with Stable Gain

Wideband Planar Glass Antenna with Stable Gain

引用

International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jiaxing Zhang Peng Fei Hu Kwok Wa Leung Kai Lu Nan Yang Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology (School of Microelectronics) Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

A wideband planar glass antenna with a stable antenna gain is presented in this paper. It consists of a vertical planar glass and a differential feeding structure. The planar glass is used as a radiator, while the differential feeding structure leads to symmetric radiation patterns. The glass antenna operates in a traveling-wave mode. With a small size of 0.50 × 0.06 × 0.76 λ03, it achieves a wide 10-dB impedance bandwidth of 58.15% (1.61–2.93 GHz), alongside a stable antenna gain of 5.5 dBi (where λ0 represents the vacuum wavelength at the central operating frequency). To validate the design, a prototype operating for Long-Term Evolution (LTE) bands was fabricated and measured. The measured results revealed a 10-dB impedance bandwidth of 60.79% (1.50–2.81 GHz). This wideband glass antenna can be used for vehicle communication systems.

关键词： Microwave antennas Antenna measurements Wavelength measurement Prototypes Glass Antenna feeds Broadband antennas

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 << < 77 78 79 80 81 82 83 84 85 86 > >>

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：