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

579 篇 会议
184 篇 期刊文献

馆藏范围

763 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

357 篇 工学
- 281 篇 计算机科学与技术...
- 237 篇 软件工程
- 89 篇 信息与通信工程
- 77 篇 电气工程
- 48 篇 控制科学与工程
- 33 篇 电子科学与技术（可...
- 28 篇 生物工程
- 28 篇 网络空间安全
- 23 篇 机械工程
- 22 篇 光学工程
- 20 篇 交通运输工程
- 19 篇 安全科学与工程
- 16 篇 生物医学工程（可授...
- 12 篇 化学工程与技术
- 11 篇 仪器科学与技术
- 9 篇 土木工程
- 8 篇 建筑学
159 篇 理学
- 99 篇 数学
- 37 篇 物理学
- 27 篇 生物学
- 26 篇 统计学（可授理学、...
- 10 篇 化学
- 10 篇 系统科学
83 篇 管理学
- 50 篇 管理科学与工程(可...
- 49 篇 图书情报与档案管...
- 12 篇 工商管理
27 篇 法学
- 16 篇 社会学
- 13 篇 法学
14 篇 医学
- 13 篇 临床医学
- 9 篇 基础医学(可授医学...
4 篇 教育学
3 篇 经济学
3 篇 军事学
2 篇 农学
1 篇 文学

主题

87 篇 feature extracti...
70 篇 accuracy
54 篇 semantics
48 篇 computational mo...
45 篇 data models
39 篇 federated learni...
39 篇 training
31 篇 neural networks
28 篇 deep learning
26 篇 transformers
22 篇 predictive model...
22 篇 adaptation model...
20 篇 data mining
19 篇 benchmark testin...
19 篇 privacy
18 篇 noise
18 篇 visualization
18 篇 codes
18 篇 attention mechan...
17 篇 object detection

机构

230 篇 key laboratory o...
199 篇 shandong provinc...
45 篇 shandong enginee...
43 篇 shaanxi key labo...
38 篇 school of comput...
38 篇 shandong fundame...
38 篇 school of comput...
33 篇 shaanxi key labo...
29 篇 shandong provinc...
28 篇 qilu university ...
27 篇 shandong provinc...
16 篇 qilu university ...
15 篇 key laboratory o...
15 篇 quan cheng labor...
14 篇 key laboratory o...
14 篇 tianjin key labo...
14 篇 key laboratory o...
13 篇 key laboratory o...
11 篇 xi'an university...
11 篇 faculty of data ...

作者

29 篇 yichuan wang
24 篇 xinhong hei
23 篇 wang yichuan
20 篇 xiaoming wu
18 篇 hei xinhong
15 篇 xiangzhi liu
15 篇 haiyan jin
14 篇 li wei
13 篇 lu wenpeng
13 篇 guohua lv
13 篇 mingle zhou
13 篇 jin haiyan
13 篇 aimin li
12 篇 xiaohui han
12 篇 delong han
12 篇 lei zhu
12 篇 wenpeng lu
11 篇 jing zhao
11 篇 zhu lei
11 篇 fu chong

语言

692 篇 英文
70 篇 其他
4 篇 中文

检索条件"机构=Shanxi Key Laboratory for Network Computing and Security Technology"

共 763 条记录，以下是361-370 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：

Efficient CNN-Transformer Aggregation network for Remote Sensing Image Change Detection

Efficient CNN-Transformer Aggregation Network for Remote Sen...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Bingjie Kong Qinglong Meng Fengqi Hao Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Change detection is a crucial task in the field of remote sensing, focusing on identifying areas where significant changes occur between two remote sensing images captured at different time points. However, existing change detection methods based on CNNs or Transformers face two main challenges. Firstly, CNNs often lack the ability to effectively model long-range dependencies, resulting in diminished recognition performance for targets sharing the same semantics but having different features. Secondly, Transformers leverage self-attention mechanisms to capture long-range dependencies adeptly but struggle to capture local details and multi-scale features effectively. In response to these challenges, we propose a CNN and Transformer Aggregation network (CTA-Net) for change detection in remote sensing images. Specifically, we devise two encoders based on Transformers and CNNs, respectively, facilitating the generation of complementary global and local features during the encoding phase. In the decoding phase, we design a pyramid-structured complementary decoder to aggregate multi-level complementary features from the CNN and Transformer branches. Furthermore, we propose a novel skip connection strategy to exploit the relationship between decoded features and multi-level complementary features, thereby enhancing the model's multi-scale invariance. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of our proposed CTA-Net model.

关键词： Target recognition Face recognition Semantics Focusing Transformers Feature extraction Encoding Decoding Remote sensing Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Channel Drop Out: A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI 7th

Channel Drop Out: A Simple Way to Prevent CNN from Overfitti...

引用

7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Luo, Jing Wang, Yaojie Xu, Rong Liu, Guangming Wang, Xiaofan Gong, Yijing Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design Xi’an China

ISBN: (纸本)9789811659393

With the development of deep learning, many motor imagery brain-computer interfaces based on convolutional neural networks (CNNs) show outstanding performances. However, the trial number of EEG in the training set is usually limited, and redundancy extensively exists in multiple channel EEG. Thus, overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of model. In this paper, channel drop out is proposed to address this problem by data augmentation and ensemble learning. Specifically, one of all EEG channels will be dropped and replaced by the mean signal of all EEG channels. In this way, the trial number in the training set was enlarged by channel drop out. And at the testing stage, all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the prediction. The experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2a to verify the performances of the proposed channel drop out approach. The results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83% and 2.65% compared with the original CNN model. So the channel drop out approach significantly improves the performances of motor imagery based BCI. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

Adaptive Compression-Encryption Scheme for Medical Image Based on Improved Compressive Sensing and Deoxyribonucleic Acid Coding-Compression

Adaptive Compression-Encryption Scheme for Medical Image Bas...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xianglian Xue Haiyan Jin Changjun Zhou School of Computer Science and Engineering Xi’an University of Technology Xi’an China Section of Computer Teaching and Research Shaanxi University of Chinese Medicine Xianyang China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm’s security.

关键词： Image coding Heuristic algorithms DNA Transforms Benchmark testing Encoding Sensors Compressed sensing Secure storage Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Feature Aggregation network for Memory-Based Industrial Anomaly Detection

Feature Aggregation Network for Memory-Based Industrial Anom...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Min Li Delong Han Rui Shao Jinghui He Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet.

关键词： Training Runtime Shape Memory management Training data Production Feature extraction Robustness Anomaly detection Testing

来源：评论

学校读者我要写书评

暂无评论

FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

Map2Schedule: An End-to-End Link Scheduling Method for Urban...

引用

IEEE International Conference on Communications (ICC)

作者： Lihao Zhang Haijian Sun Jin Sun Ramviyas Parasuraman Yinghui Ye Rose Qingyang Hu School of Electrical and Computer Engineering University of Georgia Athens GA USA School of Computing University of Georgia Athens GA USA Shanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China Electrical and Computer Engineering Department at Utah State University Logan UT USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

关键词： Wireless communication Accuracy Processor scheduling Computational modeling System performance Urban areas Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Routing Mutation Mechanism based on Mutation Cost and Resource Trustworthiness in network Moving Target Defense 7

A Hybrid Routing Mutation Mechanism based on Mutation Cost a...

引用

7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Guo, Kun Gao, Yibo Wang, Dongbin Zhi, Hui Zhang, Tao Lu, Yueming Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Cyberspace Security Research Center Peng Cheng Laboratory Beijing China Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

network moving target defense technology can effectively defend against attacker monitoring of the service. The technology makes it more difficult for attackers to attack and can ensure secure communication for services. However, there may be multiple network attacks in the network. As a result, there may be security problems with the trustworthiness of the network resource mutation space required for network moving target defense technology. The focus of current researches is mostly on the mutation timing and cost optimization of the resource mutation space. It may lead to the possibility that the resources in the resource mutation space have been already under network attacks. In this paper, we propose a hybrid routing mutation mechanism based on mutation cost and resource trustworthiness in SDN networks. The mechanism is able to evaluate the routing mutation space selected by the routing-based network moving target defense technology. In terms of both mutation cost and resource trustworthiness, the optimal weighting ratio of the two factors will be to discovered. Simulation results show that the proposed mechanism can effectively enhance the ability of the routing mutation space to avoid attacked nodes and links and improve the transmission success rate of services. © 2022 IEEE.

关键词： network routing

来源：评论

学校读者我要写书评

暂无评论

Improved YOLOv5 Object Detection Method for Cervical Fluid Cell Pathological Image Detection

Improved YOLOv5 Object Detection Method for Cervical Fluid C...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Qingyan Ding Xuecheng Dong Na Li Yu Pan Wan Zheng Dept. Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cervical liquid-based cell pathology image detection and recognition is a key task in gynecological pathology aimed at early detection of cervical precancerous lesions. In this study, a deep learning based method is proposed for the detection and recognition of cervical liquid-based cell images. This paper is based on the YOLOv5 network model for improvement. Firstly, in the data preprocessing stage: for the characteristics of the dataset, this paper proposes two data enhancement methods to augment the training dataset; secondly, the Transformer module for local information enhancement is improved by introducing a local attention mechanism, which restricts each position to only pay attention to positions within a certain range around it; then, the spatial pyramid pooling module (DSPPCSPC) is improved, and the spatial Pyramid Pooling Module (DSPPC-SPC), introducing deformable mask of deformable convolution (ODConv); then, this paper proposes a new Attention Module (XCAttention), improving S2Attention to focus on the local information of the image, and introducing its Channel Attention Module into the third branch, which performs the Dot with the first and the second branches that have gone through Spatial-shift to obtain the final output. Product operation to get the final output. Through experiments, this paper’s algorithm improves in Precision, Recall, mAP50, and mAP50-95 metrics by 1.9%, 1.9%, 2%, and 1.2%, respectively. The experimental results show that this paper’s algorithm can effectively improve the detection performance of the model.

关键词： YOLO Training Deep learning Pathology Technological innovation Image recognition Convolution

来源：评论

学校读者我要写书评

暂无评论

Trust Evaluation Model of Social Internet of Things Based on Multi-relationships

Trust Evaluation Model of Social Internet of Things Based on...

引用

2022 International Conference on networking and network Applications, NaNA 2022

作者： Fan, Fan Zhang, Hongbin Zhao, Dongmei Wang, Yanxia Liu, Bin Liu, Jian Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei Geological Workers' University Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Trust plays a vital role in ensuring the security of the Social Internet of Things. Due to the increased mobility of intelligent devices, there are frequent interactions between machines and changes in social relationships. It leads to a possible data sparsity problem in the Social Internet of Things. Therefore, it is presented that the trust evaluation model is based on various social relationships. The model considers the impact of multiple dynamically changing social relationships between nodes on trust. The implicit social relationship is mined based on the stated social relationship and potential attributes connecting nodes. It addresses the scarcity issue and the slow start of the Social Internet of Things. Simulations on the Santander Smart Cities dataset demonstrate that the approach improves the accuracy and convergence of trust evaluation in both sparse and regular networks. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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77页 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：