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

1,577 篇 会议
1 篇 期刊文献

馆藏范围

1,578 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

900 篇 工学
- 775 篇 计算机科学与技术...
- 533 篇 软件工程
- 254 篇 信息与通信工程
- 195 篇 控制科学与工程
- 131 篇 电气工程
- 85 篇 网络空间安全
- 78 篇 生物医学工程（可授...
- 75 篇 安全科学与工程
- 64 篇 交通运输工程
- 62 篇 生物工程
- 55 篇 电子科学与技术（可...
- 46 篇 动力工程及工程热...
- 44 篇 仪器科学与技术
- 43 篇 石油与天然气工程
- 42 篇 光学工程
307 篇 理学
- 164 篇 数学
- 83 篇 生物学
- 73 篇 统计学（可授理学、...
- 62 篇 物理学
- 37 篇 系统科学
- 34 篇 化学
267 篇 管理学
- 203 篇 管理科学与工程(可...
- 106 篇 图书情报与档案管...
- 84 篇 工商管理
- 34 篇 公共管理
114 篇 医学
- 80 篇 临床医学
- 77 篇 公共卫生与预防医...
- 64 篇 基础医学(可授医学...
51 篇 法学
- 48 篇 社会学
39 篇 经济学
- 39 篇 应用经济学
18 篇 文学
15 篇 教育学
11 篇 农学
2 篇 军事学
1 篇 艺术学

主题

193 篇 data models
157 篇 accuracy
154 篇 computational mo...
92 篇 training
86 篇 feature extracti...
86 篇 machine learning
78 篇 predictive model...
69 篇 data mining
68 篇 deep learning
63 篇 real-time system...
57 篇 big data
49 篇 adaptation model...
45 篇 security
44 篇 robustness
41 篇 decision making
40 篇 convolutional ne...
39 篇 cloud computing
36 篇 analytical model...
35 篇 neural networks
34 篇 optimization

机构

14 篇 lovely professio...
7 篇 shandong provinc...
7 篇 key laboratory o...
6 篇 college of mecha...
6 篇 institute of aer...
5 篇 lloyd institute ...
5 篇 college of intel...
5 篇 university of ch...
5 篇 national univers...
5 篇 department of co...
5 篇 shandong fundame...
5 篇 national univers...
5 篇 institute of aer...
4 篇 karnataka bangal...
4 篇 shanghai jiao to...
4 篇 school of comput...
4 篇 chitkara univers...
4 篇 new horizon coll...
4 篇 srm institute of...
4 篇 school of softwa...

作者

7 篇 nijhawan ginni
5 篇 jeanette miriam ...
5 篇 lu xiaobo
5 篇 singh navdeep
4 篇 nicola franco
4 篇 choudhry mani de...
4 篇 akhil karnam
4 篇 zhang rui
4 篇 yu zhang
4 篇 akshya j.
4 篇 lihui wang
4 篇 qu yuben
4 篇 wang lihui
4 篇 li lei
4 篇 dong chao
4 篇 dutt varun
4 篇 puri chetan
3 篇 yuangan wang
3 篇 junqi luo
3 篇 zhang lei

语言

1,274 篇 英文
303 篇 其他
4 篇 中文

检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1578 条记录，以下是1411-1420 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：

FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework

FruitNeRF: A Unified Neural Radiance Field based Fruit Count...

引用

ieee/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Lukas Meyer Andreas Gilson Ute Schmid Marc Stamminger Visual Computing Erlangen (VCE) Friedrich-Alexander-Universität Erlangen-Nürnberg-Fürth Germany EZRT Fraunhofer Institute for Integrated Circuits (IIS) Fürth Germany Cognitive Systems University of Bamberg Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count. The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit. We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango. Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.

关键词： Point cloud compression Image segmentation Three-dimensional displays Foundation models Semantics Neural radiance field Object tracking Optical flow Intelligent robots Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Real-Time data Analytics and Decision-Making in Modern Sensor Networks

Real-Time Data Analytics and Decision-Making in Modern Senso...

引用

Innovations in High Speed Communication and Signal Processing (IHCSP), international conference on

作者： D. Anandhasilambarasan Rahul S.Devi Shivam Khurana Vaibhav Goutham Suresh M.P. Karnik Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This study introduces a novel technique to analyze data and make real-time judgments in sensor networks. This system handles several sensor data issues using innovative machine learning, secure computing, flexible filtering, anomaly detection, and blockchain technology. We evaluated the framework's efficacy by comparing it comprehensively with existing methodologies and finding that it enhanced performance across several performance indicators. The approach is accurate, quick, resource-efficient, error-tolerant, and data-safe. complex machine learning algorithms improve data analytics accuracy and prediction. This enables rapid, precise judgments in shifting scenarios. For data-sensitive applications that must fulfill tight regulatory criteria, the technique uses safe multi-party computer technology to safeguard and secure data. Due to noise reduction and signal clarity, adaptive filtering improves data quality. However, anomaly detection systems find and resolve unusual data patterns, making them more dependable. Blockchain-based data security ensures unambiguous, unchangeable records. This prevents unauthorized data access and changes. For critical applications to perform properly, sensor data must be accurate and trustworthy. An ablation study details how each item fits within the framework and how vital it is to improve the total system. The recommended technique, with its holistic approach, represents a significant technological breakthrough in sensor networks, enabling healthcare monitoring, environmental sensing, and manufacturing automation.

关键词： Accuracy data analysis Machine learning algorithms data security Decision making Machine learning Medical services Real-time systems Blockchains Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Stock Market Predictions with Granger and Reinforcement Learning: An Adaptive Approach

Enhancing Stock Market Predictions with Granger and Reinforc...

引用

Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Chiayu Hsu Lin-Sheng Lee Jenhui Chen Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Traditional graph neural networks (GNNs) construct static graph structures, which are unable to dynamically adapt to market changes. To address this challenge, this paper proposes an integrated prediction model combining GNNs and reinforcement learning (RL), enabling dynamic adjustment of graph structures to enhance the model's adaptability and predictive ability in volatile markets. Firstly, we use GNNs to extract complex inter-stock relationships from historical stock data, constructing a dynamic market structure. Secondly, through RL strategies within the RL model, we continuously adjust trading decisions to maximize long-term returns and adapt to market changes. This combined model not only captures market trading information but also responds promptly to market dynamics, achieving more accurate predictions. Experimental results on multiple datasets of Taiwan 50 constituent stocks demonstrate that our proposed GNN-RL model outperforms traditional methods in terms of accuracy and other metrics, proving its potential and superiority in practical applications.

关键词： Measurement Adaptation models Accuracy Computational modeling Decision making Reinforcement learning Predictive models data models Stock markets Forecasting

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Enhanced Indoor Positioning with RIS-aided Channel Configuration and Analysis

Machine Learning Enhanced Indoor Positioning with RIS-aided ...

引用

ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yanhong Xu Zhao Li Ziru Zhao Blaise Herroine Aguenoukoun Jia Liu Zhixian Chang Yicheng Liu School of Cyber Engineering Xidian University Xi’an China School of Computer Science Xidian University Xi’an China School of Commun. and Inf. Engineering Xi’an University of Posts and Telecommun. Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the rapid development of wireless technologies, future communication networks should not only enhance data transmission but also provide accurate and reliable location services. In complex indoor environment, traditional positioning methods encounter challenges related to accuracy and cost due to unpredictable attenuation, multipath interference, and other factors. This paper designs a Machine Learning Enhanced Indoor Positioning method that utilizes RIS-aided Channel Configuration and Analysis, called RCCA-MLEIP, for large-scale warehouse applications. This method consists of two stages: the RIS-aided Channel Configuration and Analysis (RCCA) stage, and the Machine Learning Enhanced Indoor Positioning (MLEIP) stage. In the RCCA stage, multiple RISs are deployed to create reflection paths associated with product tags. The phase coefficients of the RISs are adjusted multiple times, and the mixed signals observed by the reader after each adjustment are recorded. Based on these mixed signals, a system of equations is established to resolve both the phase fading and amplitude fading of each reflection path. In the MLEIP stage, a feature dataset is created using simulation methods, consisting of the phase fading and amplitude fading of multiple reference tags analyzed in the RCCA stage. We use the coordinates of the reference tags as the label dataset to train a Back Propagation (BP) neural network. The trained model can then output the coordinates of a target product tag based on the channel fading features associated with that tag, which are solved/obtained in the RCCA stage. The proposed RCCA-MLEIP utilizes RIS to create a multipath environment for positioning, effectively reducing the hardware costs of the positioning system. Moreover, employing machine learning techniques to estimate the target position can enhance both accuracy and response speed.

关键词： Fading channels Wireless communication Accuracy Costs Neural networks Machine learning Reflection Hardware data models Signal resolution

来源：评论

学校读者我要写书评

暂无评论

BClean: A Bayesian data Cleaning System

BClean: A Bayesian Data Cleaning System

引用

international conference on data Engineering

作者： Jianbin Qin Sifan Huang Yaoshu Wang Jing Zhu Yifan Zhang Yukai Miao Rui Mao Makoto Onizuka Chuan Xiao Shenzhen University Shenzhen Institute of Computing Science Zhongguancun Laboratory Osaka Univeristy Nagoya University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

There is a considerable body of work on data cleaning which employs various principles to rectify erroneous data and transform a dirty dataset into a cleaner one. One of prevalent approaches is probabilistic methods, including Bayesian methods. However, existing probabilistic methods often assume a simplistic distribution (e.g., Gaussian distribution), which is frequently under-fitted in practice, or they necessitate experts to provide a complex prior distribution (e.g., via a programming language). This requirement is both labor-intensive and costly, rendering these methods less suitable for real-world applications. In this paper, we propose BClean, a Bayesian Cleaning system that features automatic Bayesian network construction and user interaction. We recast the data cleaning problem as a Bayesian inference that fully exploits the relationships between attributes in the observed dataset and any prior information provided by users. To this end, we present an automatic Bayesian network construction method that extends a structure learning-based functional dependency discovery method with similarity functions to capture the relationships between attributes. Furthermore, our system allows users to modify the generated Bayesian network in order to specify prior information or correct inaccuracies identified by the automatic generation process. We also design an effective scoring model (called the compensative scoring model) necessary for the Bayesian inference. To enhance the efficiency of data cleaning, we propose several approximation strategies for the Bayesian inference, including graph partitioning, domain pruning, and pre-detection. By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0.9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.

关键词： Transforms Gaussian distribution Probabilistic logic Rendering (computer graphics) data engineering Cleaning data models

来源：评论

学校读者我要写书评

暂无评论

A Combinatorial Approach to Hyperparameter Optimization

A Combinatorial Approach to Hyperparameter Optimization

引用

AI Engineering - Software Engineering for AI (WAIN), ieee/ACM Workshop on

作者： Krishna Khadka Jaganmohan Chandrasekaran Yu Lei Raghu N. Kacker D. Richard Kuhn University of Texas at Arlington Arlington TX USA National Security Institute Virginia Tech Arlington VA USA Information Technology Laboratory National Institute of Standards and Technology Gaithersburg MD USA

ISBN: (数字)9798400705915

ISBN: (纸本)9798350352191

In machine learning, hyperparameter optimization (HPO) is essential for effective model training and significantly impacts model performance. Hyperparameters are predefined model settings which fine-tune the model’s behavior and are critical to modeling complex data patterns. Traditional HPO approaches such as Grid Search, Random Search, and Bayesian Optimization have been widely used in this field. However, as datasets grow and models increase in complexity, these approaches often require a significant amount of time and resources for HPO. This research introduces a novel approach using t-way testing—a combinatorial approach to software testing used for identifying faults with a test set that covers all t-way interactions—for HPO. -way testing substantially narrows the search space and effectively covers parameter interactions. Our experimental results show that our approach reduces the number of necessary model evaluations and significantly cuts computational expenses while still outperforming traditional HPO approaches for the models studied in our *** CONCEPTS•computing methodologies → Machine learning;•Software and its engineering → Software maintenance tools.

关键词： Training Software testing Fault diagnosis Software maintenance Computational modeling Hyperparameter optimization data models

来源：评论

学校读者我要写书评

暂无评论

Enabling Privacy-Preserving Edge AI: Federated Learning Enhanced with Forward-Forward Algorithm

Enabling Privacy-Preserving Edge AI: Federated Learning Enha...

引用

Omni-layer Intelligent Systems (COINS), international conference on

作者： Mohammadnavid Ghader Saeed Reza Kheradpisheh Bahar Farahani Mahmood Fazlali Department of Computer Science Shahid Beheshti University Tehran Iran Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Artificial Intelligence (AI) has emerged as a pivotal technology across various sectors, including healthcare, trans-portation, and the development of smart cities, revolutionizing service delivery and operational efficiency. However, the adoption and introduction of new data-driven services leveraging central-ized training models have been hindered by significant concerns over privacy and data security, as these traditional techniques potentially expose sensitive information to breaches. Federated Learning (FL) presents a compelling solution to this dilemma, enabling decentralized data processing without compromising privacy. Integrating Edge AI into this framework, FL enables the collaborative training of models based on data distributed across different clients. Nevertheless, implementing FL on edge devices introduces a set of challenges due to the limited computational and memory resources available on such tiny devices. Specifically, the backpropagation (BP) phase of training models is notably resource-intensive, posing a barrier to efficient deployment. To address this, we replaced the backpropagation phase with a Forward-Forward (FF) algorithm. Moreover, we integrated and compared several loss functions, namely Hinton, Symba, and Swish, to assess their compatibility and efficiency in the context of forward-forward training within the federated learning framework. The study indicates that our novel method leads to a slight decrease in accuracy for large and complex datasets compared to the traditional BP technique. However, it has the potential to enhance runtime and reduce memory overhead. The proposed technique represents a promising path toward the broader adoption of Edge AI by effectively addressing critical technical challenges, namely privacy concerns and on-chip model training.

关键词： Training Backpropagation data privacy Accuracy Federated learning Smart cities Computational modeling

来源：评论

学校读者我要写书评

暂无评论

SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

引用

Proceedings of the 30th international conference on Parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

来源：评论

学校读者我要写书评

暂无评论

Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

Improving VTE Identification through Language Models from Ra...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Jamie Deng Yusen Wu Yelena Yesha Phuong Nguyen Department of Computer Science University of Miami Miami FL USA Frost Institute for Data Science & Computing University of Miami Miami FL USA Department of Radiology University of Miami Miami FL USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Venous thromboembolism (VTE) is a critical cardio-vascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our previous work, which addressed VTE detection using deep learning methods for DVT and a hybrid approach combining deep learning and rule-based classification for PE. Our earlier approaches, while effective, had two major limitations: they were complex and required expert involvement for feature engineering of the rule set. To overcome these challenges, we utilize the Mamba architecture-based classifier. This model achieves remarkable results, with a 97% accuracy and F1 score on the DVT dataset and a 98% accuracy and F1 score on the PE dataset. In contrast to the previous hybrid method on PE identification, the Mamba classifier eliminates the need for hand-engineered rules, significantly reducing model complexity while maintaining comparable performance. Additionally, we evaluated a lightweight Large Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model delivers competitive results, outperforming the baseline BERT models, it proves to be computationally intensive due to its larger parameter set. Our evaluation shows that the Mamba-based model demonstrates superior performance and efficiency in VTE identification, offering an effective solution to the limitations of previous approaches.

关键词： Deep learning Training Accuracy Computational modeling Biological system modeling Computer architecture Feature extraction complexity theory Thrombosis Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Multimodal Knowledge Graph Vision-Language Models for Precision Retrieval and Inference in Embedded Systems

Multimodal Knowledge Graph Vision-Language Models for Precis...

引用

ieee international conference on Robotics and Biomimetics

作者： Mingyi Li Jiayin Li Hui Liu Lijin Han Dengting Liao Zekun Zhang Zong Gao Baoshuai Liu Zhongfeng Jin School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Shanghai Zhangjiang Institute of Mathematicas Zhangjiang Institute of Shanghai Shanghai China General Technology Department China North Vehicle Research Institute China GNational Computer Network Emergency Response Technical Team Coordination Center of China China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In light of the challenges associated with enhancing multimodal knowledge graph retrieval and improving embodied intelligence in dynamic environments, we propose the Multimodal Knowledge Graph Vision-Language System (MKGVL). This system is designed to enhance the reasoning and feedback capabilities of embodied intelligent systems by continuously updating the knowledge graph through real-time feedback from the Vision-Language Model (VLM). By integrating visual encoders, language models, and knowledge graph networks, MKGVL constructs a unified multimodal representation that enables adaptive decision-making and responsiveness to environmental changes. Experimental results show that MKGVL outperforms existing models in fine-grained retrieval tasks, achieving an 11.5% improvement in Rank1 accuracy and a mean Average Precision (mAP) of 97.49%. Further evaluations conducted on datasets such as ARKitScenes, MultiScan, and 3RScan highlight the model's robustness and adaptability. Additionally, MKGVL's deployment on embedded platforms like Jetson Orin demonstrates its efficiency in real-time multimodal tasks, particularly in resource-constrained environments. These findings underscore MKGVL's ability to deliver accurate and efficient multimodal processing, making it a strong solution for adaptive knowledge-based systems in complex settings.

关键词： Adaptation models Solid modeling Visualization Accuracy Three-dimensional displays Adaptive systems Knowledge graphs Real-time systems Cognition Intelligent systems

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158页 << < 138 139 140 141 142 143 144 145 146 147 > >>

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：