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

1,195 篇 会议
37 篇 期刊文献

馆藏范围

1,232 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

642 篇 工学
- 551 篇 计算机科学与技术...
- 455 篇 软件工程
- 225 篇 信息与通信工程
- 125 篇 控制科学与工程
- 96 篇 电气工程
- 94 篇 生物工程
- 91 篇 生物医学工程（可授...
- 72 篇 电子科学与技术（可...
- 69 篇 网络空间安全
- 53 篇 光学工程
- 46 篇 安全科学与工程
- 34 篇 仪器科学与技术
- 32 篇 机械工程
- 30 篇 动力工程及工程热...
- 29 篇 化学工程与技术
- 27 篇 交通运输工程
368 篇 理学
- 226 篇 数学
- 114 篇 统计学（可授理学、...
- 102 篇 生物学
- 54 篇 物理学
- 33 篇 化学
- 29 篇 系统科学
241 篇 管理学
- 166 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 55 篇 工商管理
85 篇 医学
- 80 篇 基础医学(可授医学...
- 79 篇 临床医学
- 43 篇 公共卫生与预防医...
- 43 篇 药学(可授医学、理...
39 篇 法学
- 36 篇 社会学
25 篇 经济学
21 篇 农学
7 篇 教育学
3 篇 军事学

主题

165 篇 feature extracti...
157 篇 accuracy
108 篇 convolutional ne...
80 篇 deep learning
54 篇 optimization
51 篇 support vector m...
50 篇 predictive model...
49 篇 internet of thin...
42 篇 computational mo...
35 篇 training
34 篇 long short term ...
33 篇 real-time system...
33 篇 machine learning
33 篇 data models
31 篇 classification a...
25 篇 security
25 篇 prediction algor...
25 篇 measurement
24 篇 visualization
24 篇 network security

机构

194 篇 department of co...
135 篇 department of el...
117 篇 the islamic univ...
98 篇 nitte meenakshi ...
87 篇 school of comput...
82 篇 the islamic univ...
58 篇 sr university sc...
53 篇 college of techn...
44 篇 school of comput...
37 篇 department of co...
36 篇 department of ec...
33 篇 nitte meenakshi ...
33 篇 nitte meenakshi ...
31 篇 nitte meenakshi ...
28 篇 department of ar...
28 篇 school of comput...
26 篇 east west instit...
26 篇 department of co...
26 篇 sr university sc...
25 篇 department of co...

作者

74 篇 piyush kumar par...
73 篇 pareek piyush ku...
24 篇 ramachandra a.c.
23 篇 muntather almusa...
23 篇 almusawi muntath...
21 篇 ramachandra a c
21 篇 parameshachari b...
20 篇 myasar mundher a...
20 篇 srinivas aluvala
20 篇 aluvala srinivas
19 篇 al-jawahry hassa...
18 篇 alabdeli haider
18 篇 hassan m. al-jaw...
18 篇 adnan myasar mun...
18 篇 haider alabdeli
16 篇 mohammed al-faro...
16 篇 nalini n.
15 篇 sundaram s. meen...
15 篇 alzubaidi laith ...
15 篇 mohammed i. habe...

语言

1,055 篇 英文
177 篇 其他
2 篇 中文

检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1232 条记录，以下是1151-1160 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：

Improved Recurrent Convolutional Neural Network for Crack Detection in Concrete Structures

Improved Recurrent Convolutional Neural Network for Crack De...

引用

Software, Systems and Information technology (SSITCON), International Conference on

作者： Muntather Almusawi H Muralidhar Ananthalaxmi R Rana Veer Samara Sihman Bharattej P.S Abdul Lateef Haroon Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Civil Engineering HKE Society’s Sir. M Visvesvaraya College of Engineering Raichur (Affiliated to VTU) Raichur India Department of Computer Science Saint Leo University St Leo USA Department of Electronics and Communication Engineering Ballari Institute of Technology and Management Ballari Karnataka

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Detecting cracks in concrete structures involves advanced imaging and deep learning techniques to identify and assess the extent of structural damage. The problem statement in crack detection methods like an identification of cracks, complex background noise, intricate patterns, and environmental conditions are overcome by segmentation and detection process of this approach. The SDNet2018 dataset and the proposed crack detection method, like U-Net for segmentation, distinguish between cracks and non-cracks. Inception ResNetV2 for pixel-wise feature extraction enhances the identification of crack patterns. The final step involves crack-type detection using an Improved Recurrent Convolutional Neural Network (RCNN). The proposed method achieves performance metrics with an accuracy of $99.67 \%$, precision of $98.78 \%$, recall of $99.45 \%$, and an F1-score of ${9 9. 2 1 \%.}$ Existing methods like Convolutional Neural Network (CNN+U-Net), K-means with Genetic Algorithm (GKA), Multiscale Crack Detection Network (MSCNet), and Transfer Learning (TL) overcome limitations and provide accurate solution for crack detection in surface areas.

关键词： Measurement Accuracy Transfer learning Software systems Software reliability Convolutional neural networks Surface cracks Information technology Surface treatment Testing

来源：评论

学校读者我要写书评

暂无评论

A Hand Gesture Recognition Based on SqueezeNet and Octave Convolution Feature Extraction Approach

A Hand Gesture Recognition Based on SqueezeNet and Octave Co...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Priyanka K K V Rajesh Muntather Muhsin Hassan Sripelli Jagadish Mohammed Kadhim Obaid Dept. of ISE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Malla Reddy Engineering College for Women (Autonomous) Secunderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India College of Arts Ahl Al Bayt University Karbala Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In recent days, sign recognition plays an important role in overcoming the communication barriers between hearing-impaired community and the normal society where sign language is essential. This paper proposes an efficient feature extraction and feature mapping technique by employing SqueezeNet fusion Octave Convolution method (SqueezeNet fusion OctConv). Initially, the input image data is preprocessed to transform the data to a common scale and avoid imbalance of model. The SqueezeNet model with fire module is employed for efficient parameter reduction and significant feature extraction. The Octave Convolutional (OctConv) technique is applied to refine the extracted features that optimizes the feature utilization, handles spatial components and reduces redundancy which enhances the feature representation. The integration of fully connected layers with spatial attention mechanism enables the model to learn complex relationships and assign weights based on relevance ensuring precise classification. The proposed method is evaluated on NUS-II dataset and achieved high accuracy of 99.25% which surpassed the existing sign recognition models such as Intensive Feature Extrication Deep Network, Densely Connected Res2Net.

关键词： Sign language Accuracy Attention mechanisms Convolution Social robots Termination of employment Transforms Feature extraction Data models Optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimization Based Long Short-Term Memory for Android Malware Detection

Hybrid Optimization Based Long Short-Term Memory for Android...

引用

Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Jayapal Lande M. Maheswari S. M. Kamali R. Dineshkumar Priyanka M S School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of Computing SRM Institue of Science and Technology Chennai India Department of Electrical and Electronics Engineering Annapoorana Engineering College Salem India Department of ECE Saveetha School of Engineering Sriperumbudur Thandalam India Department of AI and ML Nitte Meenakshi Institute of Technology Yelahanka Bengaluru India

Malware detection is process of detecting the suspicious files and activities, that can classify as benign and malicious depends on different characters like capability of infection, structure of malware and speed rate. In this paper, proposed a Particle Swarm Optimization and Ant Colony Optimization (HPSALO) based Long Short-Term Memory (LSTM) model for malware detection and classification. The combination of these two algorithms that leads to the balance between exploration and exploitation. The dataset utilized for the research is DREBIN dataset and process of data cleaning is performed by NumPy library. The feature selection is performed by combination of hybrid PSALO algorithms and selected features are given to Long Short-Term Memory (LSTM) classifier that classifies as benign and malware files. The performance of proposed method is estimated by performance metrics of accuracy, precision, recall and f1-score. The proposed method attained accuracy of 99.38%, precision of 99.30%, recall of 99.08% and f1-score of 99.42% which is comparatively higher than other existing methods like Whale Optimization Algorithm-LSTM (WOA-LSTM), Cat Swarm Optimization-LSTM (CSO-LSTM), Particle Swarm Optimization-LSTM and Ant Lion Optimization-LSTM.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Avoidance of Ship Collision Using Deep Reinforcement Learning with Bi-Directional Long Short-Term Memory in Continuous Action Spaces

Avoidance of Ship Collision Using Deep Reinforcement Learnin...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Myasar Mundher Adnan Sunil Kumar V T. Aditya Sai Srinivas Srinivas Aluvala Artificial Intelligence & Machine Learning Department Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India School of Computer Science and Engineering SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent days, with the globalization of the world economy the ships count are increasing for marine transportation and the Waterways are growing increasingly overcrowded than previous. This situation may leads to problem of collision of ships which may cause losing of life and damage for property and to nature. Many automatically collision models of ships are implemented but many focused only on the ship-ship encounter situation only. By using a grid sensor which is virtual sensor, agents of Deep Reinforcement Learning (DRL) classify approach of a multiple ships. This framework introduces an automatically collision detection algorithm for ships using DRL in continuous action spaces. DRL is used for avoidance of collision with a maximum distance of safe passing between ships. A unique method is developed named inside Obstacle Zone by Target (OZT) used to change learning capability that expands the OZT. Using Bi-directional Long Short-Term Memory (BI-LSTM) cell, network is redesigned and continuous action spaces training is carried out to train a model with longer safe distance of ships. In collision detection model that the bow cross range is effective for COLREGs compliant collision avoidance that is proposed in this model. The propose model also validates a scenario that included more ships and have passed that Imazu problem. The proposed BI-LSTM model achieved 80.43 % of accuracy, 95.67% of precision, 85.89% of recall and 93.54% of f1 score values.

关键词： Accuracy Biological system modeling Bidirectional control Power system stability Deep reinforcement learning Stability analysis Vectors Marine vehicles Collision avoidance Long short term memory

来源：评论

学校读者我要写书评

暂无评论

A Distributed Pricing Mechanism and Optimal Schedulingin Smart Grid using Improved Grey Wolf Optimization

A Distributed Pricing Mechanism and Optimal Schedulingin Sma...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Y. Nagendar Haider Alabdeli Prashanth V Aarthy G C. Thangavel School of Computer Science and Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computing and Information Technology REVA University Bengaluru India Department of Mechanical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In recent days, energy is most important resource new techniques and methods which introduced to fulfill energy demand for price-based demand response (DR). The preface to this study has shown that load management strategies implemented based on Costlier scheduling for low or consistent energy users results from price-based demand response strategies. Suggested work has taken into account and broadened the same concept to include numerous EV charging regions and corresponding loads from an analytical and implementation point of view. This research provides an innovative approach called Improved Grey Wolf Optimization (IGWO) that uses the distinct energy consumption patterns of EVs in each location to calculate EV charging costs. Dynamic price signal that is non-discriminatory is sent to all EV regions and stations. First, a brand-new mathematical model based on market dynamics and real-time load demand is provided to determine charging prices. The global optimum solution produced by the optimization algorithm successfully converges, confirming the efficacy of the suggested mechanism. Lastly, analysis and simulation outcomes are performed to demonstrate the accomplishments of our suggested task in standings of reasonable cost high user satisfaction distribution. IGWO demonstrated usefulness's revenue is modest by either mechanism or cost saving is 0.69-6.89%.

关键词： Energy consumption Costs Simulation Optimal scheduling Pricing Electric vehicle charging Real-time systems Demand response Scheduling Smart grids

来源：评论

学校读者我要写书评

暂无评论

Trust-aware Routing Protocol using Modified Sand Cat Swarm Optimization in Iot-Based WSN

Trust-aware Routing Protocol using Modified Sand Cat Swarm O...

引用

Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Nagendar Yamsani Anitha D Y. M. Mahaboob John M. Jamuna Rani Sowmya M School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of computing SRM Institute of Science and Technology Kattankulathur India Ece Department Mahendra College Of Engineering Salem India Department of ECE Sona College of Technology Salem India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

The trust-aware secure route selection leverages trust-based metrics for evaluating the credibility of nodes and routes which enables the selection of the most reliable and secure paths for data transmission. Secure routing is of major significance for preventing wireless devices from other threats and effective characteristics in WSN attain an efficient secure routing selection. This research proposed the trust secure route selection utilizing Modified Sand Cat Swarm Optimization (TSRS-MSCSO) for efficient route selection. Initially, the secure nodes are identified by using energy and delay of the fitness function. After identifying secure nodes, the route selection is performed through the Modified Sand Cat Swarm Optimization algorithm. The performance of the proposed model is estimated by metrics like packet delivery ratio, throughput, delay and energy consumption with various nodes of 50, 100, 150, and 200. The proposed model attained a high packet delivery ratio of 98.8%, less delay of 3s, high throughput of 96.27%, and less energy consumption of 2.14J for 50 nodes which ensures better performance than other existing methods like Dynamic Deep Neural Network-based Particle Swarm Optimization (DDNN-PSO), Chaotic Bumble Bees Mating Optimization- Trust Sensing Model (CBBMOR-TSM) and fuzzy TOPSIS based hybrid Jarratt Butterfly Optimization (FTOPSIS-HJBO) models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Rapid Object Recognition Scenario Based on Improved YOLO-V3 Network

A Rapid Object Recognition Scenario Based on Improved YOLO-V...

引用

Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Suvarna Sunil Kumar Shreenath K N G Sunil Vishwanath P Smitha Sharath Shankar IT Department Malla Reddy Engineering College for Women Hyderabad India Department of CSE Siddaganga Institute of Technology Tumakuru India School of Computer Science & Artificial Intelligence SR University Warangal India Electronics and Communication Engineering Sir M. Visvesvaraya College of Engineering Raichur India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

The object recognition is an essential field in computer vision which comprises locating and recognizing objects within a given input image. The basic principle of these techniques is utilized to recognize certain object and detect their location through generating a list of squares to surround the object. This paper proposed a You Only Look Once Version3 (YOLOV3)-MobileNet for recognizing the object. The Caltech101 dataset is utilized in this research and preprocessed through min-max normalization and contrast enhancement techniques. The preprocessed images are fed into the integration of YOLOV3 and MobileNet classification. The YOLOV3 is known for real-time object detection capabilities and using MobileNet makes it more effective. The performance of YOLOV3-MobileNet is estimated through the performance measures of accuracy, precision, sensitivity, specificity and f1-score. The YOLOV3-MobileNet attained high accuracy of 96.82%, precision of 93.66%, sensitivity of 93.47%, specificity of 93.21% and f1-score of 93.08% which is comparatively higher than existing methods like Fuzzy C-means and Saliency-Hierarchical Max-poling (SAL-HMAX).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Intrusion Detection System using Pearson Correlation based Long Short-Term Memory

Enhanced Intrusion Detection System using Pearson Correlatio...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Rekha Phadke K Sudheer Kumar Muntather Almusawi N Baskar Zainab Abed Almoussawi Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science & Artificial Intelligence SR University Warangal India The Islamic university Najaf Iraq CSE Malla Reddy Engineering College for Women Hyderabad India College of Islamic Science Ahl Al Bayt University Karbala Iraq

The Intrusion Detection System (IDS) is one of the common deep learning (DL) use cases which is used to finding and identifying outliers to prevent adversarial attacks, fraud and network intrusions. These systems play an important role in securing computer networks. The improvement of effective performance and security is the most needed in the IDS system. This paper aims to implement an IDS utilizing DL method. The DL model is the most encouraging technique and is widely used to detect intrusions. This paper implemented a Pearson’s Correlation based Long Short-Term Memory (LSTM) model for intrusion detection system. The proposed Pearson’s Correlation is evaluated on NSL-KDD dataset, and Min-Max normalization is a pre-processing method employed for normalize the data. Filter method of Pearson Correlation is utilized in the implemented for selecting the features. The LSTM is utilized to enhance system capability to identify and classify intrusion accurately and effectively. The implemented Pearson’s Correlation based LSTM achieves better accuracy using NSL-KDD dataset with the value of 99.50% in term of accuracy, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Energy Efficient and Reliable Strategies for Intra-Cluster and Inter-Cluster Communications in Wireless Sensor Networks

An Energy Efficient and Reliable Strategies for Intra-Cluste...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Kavitha Rani Balmuri Sujatha J Sudha Arvind Bechoo Lal Sapna Kumari C Department of Information Technology CMR Technical Campus Hyderabad India Department of Computer Science & Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Electronics & Communication Engineering CMR Technical Campus Kandlakoya India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The development and widespread adoption of numerous wireless technologies are opening the door to novel forms of remote monitoring. Because of their adaptability, performance, low cost, and mobility, sensing devices are gaining in popularity. Due to their portability and flexibility, Wireless Sensor Networks (WSN) can be a viable alternative to hardwired infrastructure in geographically dispersed settings. There has been a rise in their popularity due to their capacity to detect and report changes in a wide range of environmental and physical variables, including temperature, sound, vibration, pressure, and many more. The primary goal of this study is to identify intra- and inter-cluster communication protocols that are suitable in terms of energy efficiency, scalability, and dependability. At this stage, the specified region is partitioned into clusters, and the leader nodes of those clusters are chosen according to predetermined criteria. Once a cluster has formed, periodic and aperiodic data can be sensed and communicated among members using appropriate MAC protocols. In order to take advantage of the best features of CSMA/CA and variable TDMA, these protocols need a third, hybrid way of data sensing. As a result, the proposed protocols require less power to run while still providing excellent reliability and scalability for a static network's periodic and event-based data transmission.

关键词： Wireless communication Vibrations Wireless sensor networks Protocols Scalability Routing Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Frameworks for Multimodal Image and Speech Signal Processing

Advanced Machine Learning Frameworks for Multimodal Image an...

引用

Advancement in technology (ICONAT), International Conference for

作者： P Ramesh Naidu Saurabh Shandilya Srividya C N Shantanu Sudhir Gujar Supriya Devi Dankan Gowda V Department OF Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Advance Computing Poornima College of Engineering Jaipur Rajasthan India Dept. of ECE Faculty of Engineering Management & Technology BGSIT Adichunchanagiri University B G Nagara India Department of CyberSecurity Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Department of Mathematics Alliance University Bangalore Karnataka India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework; the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework’s efficiency in predicting the multimodal input and demonstrate superiority over the prior methods.

关键词： Accuracy Surveillance Taxonomy Speech recognition Machine learning Speech enhancement Signal processing Feature extraction Data processing Real-time 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124页 << < 112 113 114 115 116 117 118 119 120 121 > >>

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：