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

73 篇 会议
26 篇 期刊文献

馆藏范围

99 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

54 篇 工学
- 45 篇 计算机科学与技术...
- 37 篇 软件工程
- 20 篇 电气工程
- 18 篇 信息与通信工程
- 9 篇 控制科学与工程
- 5 篇 网络空间安全
- 4 篇 生物工程
- 3 篇 光学工程
- 3 篇 交通运输工程
- 3 篇 生物医学工程（可授...
- 2 篇 机械工程
- 2 篇 电子科学与技术（可...
- 1 篇 动力工程及工程热...
15 篇 理学
- 7 篇 数学
- 4 篇 生物学
- 2 篇 物理学
- 2 篇 统计学（可授理学、...
- 1 篇 化学
- 1 篇 地球物理学
- 1 篇 系统科学
13 篇 管理学
- 9 篇 图书情报与档案管...
- 7 篇 管理科学与工程(可...
- 4 篇 工商管理
4 篇 医学
- 4 篇 基础医学(可授医学...
- 4 篇 临床医学
- 4 篇 公共卫生与预防医...
3 篇 法学
- 3 篇 法学
- 1 篇 社会学
1 篇 文学
- 1 篇 中国语言文学
- 1 篇 外国语言文学

主题

9 篇 federated learni...
9 篇 accuracy
9 篇 data models
8 篇 feature extracti...
6 篇 speech processin...
6 篇 contrastive lear...
6 篇 computational mo...
5 篇 transformers
5 篇 semantics
4 篇 image fusion
4 篇 noise
4 篇 predictive model...
4 篇 training
4 篇 attention mechan...
3 篇 data mining
3 篇 security
3 篇 correlation
3 篇 biological syste...
3 篇 privacy
2 篇 patents

机构

40 篇 shandong provinc...
34 篇 key laboratory o...
21 篇 shandong provinc...
13 篇 shandong fundame...
10 篇 shandong fundame...
7 篇 qilu university ...
6 篇 shandong key lab...
6 篇 qilu university ...
6 篇 qilu university ...
5 篇 australian artif...
4 篇 beihang universi...
4 篇 shandong provinc...
3 篇 shandong enginee...
3 篇 key laboratory o...
3 篇 ministry of educ...
3 篇 key laboratory o...
3 篇 qilu university ...
3 篇 national yunlin ...
3 篇 shandong fundame...
2 篇 china telecom di...

作者

11 篇 gao longxiang
6 篇 lu wenpeng
5 篇 lv guohua
5 篇 wei zhang
5 篇 qu youyang
4 篇 si jiasheng
4 篇 guohua lv
4 篇 gao yongbiao
4 篇 wang changwei
4 篇 wang guijuan
4 篇 xu rongtao
4 篇 na li
4 篇 wenpeng lu
3 篇 zhang weiyu
3 篇 gao qian
3 篇 li xiong
3 篇 longxiang gao
3 篇 guixin zhao
3 篇 weiyu zhang
3 篇 peng xueping

语言

86 篇 英文
13 篇 其他
2 篇 中文

检索条件"机构=Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing"

共 99 条记录，以下是61-70 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：

A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

引用

2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Cao, Xiaoya Chen, Zhenya Liu, Weisheng Liu, Yuanlong Wang, Wenting Wang, Xin Yang, Ming Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (纸本)9798350312270

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy. © 2023 IEEE.

关键词： System stability

来源：评论

学校读者我要写书评

暂无评论

Trusted Auditing of Data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

引用

21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive L...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huiting Li Weiyu Zhang Yong Shang Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target binding affinity prediction is critical to drug design. Some existing computational methods rely on single-scale data and cannot fully integrate the rich information of multi-scale data, such as molecular structures and network information. In this paper, we propose the MBC-DTA model, which effectively integrates multi-scale data, and models the complex interactions between atoms and amino acids. We introduce GCN and GAT, which effectively extract the feature representation of molecular structure scale and network scale. Afterwards, the features obtained from the molecular structure scale are fed into a bilinear attention network, which captures the intricate interaction information between drug-target pairs. Additionally, a cross-scale graph contrastive learning strategy is applied, optimizing the feature representations across different scales. Extensive experimental results on two benchmark datasets demonstrate that the MBC-DTA model outperforms existing state-of-the-art methods.

关键词： Drugs Computational modeling Biological system modeling Contrastive learning Benchmark testing Feature extraction Data models Bipartite graph Bioinformatics Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

引用

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

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Prediction of MPI Communication Time in High-Performance computing Applications

Machine Learning-Based Prediction of MPI Communication Time ...

引用

Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Zhuang Wang Yuan Zhuang Yunhui Zeng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC systems and directly affects the performance and efficiency of these applications. Therefore, modeling and predicting the performance of MPI communication is critically important. This paper utilizes a machine learning-based approach to predict MPI communication time. It uses performance analysis tools to collect real-time communication data and select critical features related to execution time. The data is categorized, then, for each type of MPI communication, a dedicated regression model is trained. Firstly, XGBoost and Artificial Neural Networks (ANN) are employed for function level modeling. Then, fusion techniques are used to combine XGBoost and ANN models to improve prediction accuracy. Finally, these function level models are integrated into an application level model to predict the overall communication time. This model is evaluated using CG, FT, and CPMD. Experimental results show that the average prediction errors of the model are 12.6%, 13.2%, and 13.5%, with a minimum error of 7%.

关键词： Accuracy Computational modeling High performance computing Message passing Artificial neural networks Machine learning Predictive models Data models Supercomputers Tuning

来源：评论

学校读者我要写书评

暂无评论

DAC-UNet: Dual Attention CNN-Enhanced CswinUnet for Gastric Cancer Pathological Image Segmentation

DAC-UNet: Dual Attention CNN-Enhanced CswinUnet for Gastric ...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Tao Hu Jingshan Pan Na Li Tangbin Tian Shengyu Liu Lu Zhang Yujiao Han Jiwei Xu 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 Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Gastric cancer is one of the most lethal cancers. Currently, clinical diagnosis primarily relies on gastroscopic biopsies and the manual experience of doctors to assess the development of lesions in tissue sections. However, pathological screening requires a significant amount of time from specialized pathologists. Due to variations in clinical experience and limitations in medical resources, the detection of gastric cancer still faces challenges. Traditional methods for segmenting gastric cancer images often overlook the hierarchical structure of features and the spatial details, both local and global, in the lesion images. To address the above issues, this paper proposes a hybrid and efficient pathological image segmentation network named DAC-UNet, designed for precise pathological image segmentation tasks. DAC-UNet uses Cswin Transformer blocks to build a hierarchical structure, allowing it to capture global information with less computational complexity. The network also includes multi-scale dual attention skip connections between the encoder and decoder to preserve local spatial details and improve global interactions across different scales. We conducted a thorough theoretical analysis of this method and validated it on a gastric slice dataset. The experimental results demonstrate that our proposed method achieves outstanding performance on both our gastric slice dataset and the MoNuSeg dataset. Specifically, on our gastric slice dataset, we obtained a Dice similarity coefficient (DSC) of 89.42% and an HD95 of 47.24, while on the MoNuSeg dataset, we achieved a DSC of 79.28% and a 95th percentile Hausdorff distance (HD95) of 19.38.

关键词： Image segmentation Pathology Semantics Manuals Transformers Decoding Lesions Medical diagnostic imaging Faces Cancer

来源：评论

学校读者我要写书评

暂无评论

Workflow Scheduling Method in Multi-Cluster Environments Using Tasks' Spatiotemporal Attributes

Workflow Scheduling Method in Multi-Cluster Environments Usi...

引用

Electronic Information Engineering and computer Communication (EIECC), International Conference on

作者： Yudong Geng Jing Chen Honghua Yan Qingshui Hao Aimin Zhang Tianxiang Zhang Mingchao Ji Tonglin Fu 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Greenthink Electric Power Technology Co. Ltd Jinan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Nowadays, computing resources are widely distributed and diverse, and the demand for efficient execution of various workflow tasks on wide-area resources has been raised. multi-objection scheduling method based on space-time attribute of tasks. A scheduling scheme is generated through the three stages of workflow scheduling queue generation, resource selection combined with task spatiotemporal attributes and task and resource matching, and the selected resources of workflow are run according to this scheduling scheme. This method constructs a scheduling problem model to minimize workflow completion time, cost and resource matching degree, and finds the optimal scheduling scheme to improve task execution efficiency. In the process of the experiment, the workflow is actually run in the multi-cluster environment, and the experiment proves that it has a good effect in reducing the completion time and cost of workflow.

关键词： Sequential analysis Costs Processor scheduling Filtering Optimal scheduling Scheduling Spatiotemporal phenomena

来源：评论

学校读者我要写书评

暂无评论

T-M Net: A Fusion Convolutional Neural Network Method for Lung Aging Assessment

T-M Net: A Fusion Convolutional Neural Network Method for Lu...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Tangbin Tian Jingshan Pan Jing Ge Na Li Dexin Yu Liping Zuo 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Radiology Qilu Hospital of Shandong University Jinan China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

As the population ages, the precise assessment of pulmonary senescence becomes essential for the diagnosis and management of chronic respiratory diseases. However, existing medical imaging analysis techniques encounter significant chal-lenges due to data scarcity, especially in the domain of lung CT imaging. This research proposes an innovative method for assessing lung aging in CT images-T-M Net. This method combines transfer learning and MS-CNN to improve the accuracy and efficiency of lung aging assessment in CT images. We employed a pretrained ResNet18 model, capitalizing on transfer learning to harness the insights from extensive datasets, thereby minimizing the computational demands of model training. Furthermore, a channel attention mechanism is integrated within the transfer learning framework to heighten the model's focus on critical pulmonary regions, thus augmenting its predictive accuracy and interpretability. To accommodate the distinctive characteristics of lung CTs, a MS CNN architecture was developed, capturing multi-dimensional information ranging from microscopic details to macroscopic structures through variously sized convolutional kernels. Testing on a lung CT dataset revealed that our approach surpasses traditional methods in accuracy and generalizability, underscoring its potential utility in the early diagnosis and effective treatment of pulmonary ailments.

关键词： Training Accuracy Attention mechanisms Lungs Computational modeling Computed tomography Transfer learning Aging Convolutional neural networks Load modeling

来源：评论

学校读者我要写书评

暂无评论

Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

引用

25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Generating Personalized Imputations for Patient Health Status Prediction in Electronic Health Records

Generating Personalized Imputations for Patient Health Statu...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Liu Weiyu Zhang Jiasheng Si Zhao Li Xueping Peng Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electronic health records (EHRs) play a crucial role in the development of personalized treatment plans for patients. However, EHRs are often highly incomplete, posing significant challenges for predictive modeling. While existing deep learning models employ various imputation techniques to reconstruct missing values, they often fail to represent missing data in a personalized manner and do not learn from the missingness patterns in EHRs data. This limitation reduces their effectiveness in practical personalized healthcare applications. To address this issue, we propose SPIME, a self-supervised model that generates personalized imputations in EHRs data for patient health status prediction. We introduce a personalized missing mask (PMM) based on the frequency of feature measurements. Additionally, we incorporate a masked imputation task (MIT) loss that minimizes the loss of artificially introduced missing values, thereby enhancing the model’s capability to handle missing data. SPIME adopts self-supervised pretraining to learn representations from personalized missing patterns and reconstructs missing data in the latent space. To further enhance representation learning, two independent attention mechanisms are applied separately across the feature and temporal dimensions. Experimental results on two real-world EHRs datasets show that SPIME outperforms existing state-of-the-art methods in predicting in-hospital mortality and decompensation, demonstrating its effectiveness in reconstructing missing data and predicting patient health status. The code will be published at https://***/cling6666/SPIME.

关键词： Training Representation learning Biological system modeling Mortality Predictive models Biomedical measurement Imputation Data models Frequency measurement Electronic medical records

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10页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：