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

2,922 篇 会议
19 篇 期刊文献
14 册 图书

馆藏范围

2,955 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,963 篇 工学
- 1,321 篇 计算机科学与技术...
- 945 篇 软件工程
- 817 篇 信息与通信工程
- 550 篇 电气工程
- 296 篇 控制科学与工程
- 261 篇 电子科学与技术（可...
- 189 篇 仪器科学与技术
- 171 篇 生物医学工程（可授...
- 168 篇 机械工程
- 160 篇 生物工程
- 112 篇 网络空间安全
- 99 篇 动力工程及工程热...
- 96 篇 光学工程
- 83 篇 化学工程与技术
- 78 篇 材料科学与工程（可...
- 71 篇 石油与天然气工程
- 58 篇 交通运输工程
- 51 篇 安全科学与工程
863 篇 理学
- 460 篇 数学
- 261 篇 物理学
- 183 篇 生物学
- 139 篇 化学
- 137 篇 统计学（可授理学、...
- 66 篇 系统科学
397 篇 管理学
- 236 篇 管理科学与工程(可...
- 196 篇 图书情报与档案管...
- 81 篇 工商管理
222 篇 医学
- 196 篇 临床医学
- 114 篇 基础医学(可授医学...
- 81 篇 药学(可授医学、理...
38 篇 法学
30 篇 经济学
23 篇 农学
19 篇 教育学
14 篇 军事学
3 篇 文学

主题

222 篇 signal processin...
117 篇 signal processin...
101 篇 data mining
77 篇 feature extracti...
68 篇 big data
67 篇 digital signal p...
57 篇 data acquisition
54 篇 signal to noise ...
52 篇 machine learning
48 篇 deep learning
46 篇 data models
40 篇 support vector m...
34 篇 training
33 篇 electroencephalo...
31 篇 data processing
31 篇 real-time system...
30 篇 array signal pro...
28 篇 image processing
28 篇 accuracy
26 篇 neural networks

机构

49 篇 maharishi school...
37 篇 centre of interd...
16 篇 department of ar...
15 篇 vishwakarma inst...
12 篇 department of el...
12 篇 department of co...
11 篇 department of sc...
11 篇 vivekananda glob...
9 篇 department of co...
8 篇 department of el...
8 篇 chitkara univers...
8 篇 prince shri venk...
8 篇 prince shri venk...
8 篇 key laboratory o...
8 篇 vivekananda glob...
8 篇 department of el...
7 篇 school of comput...
7 篇 key laboratory o...
7 篇 american interna...
7 篇 insight sfi rese...

作者

7 篇 md. rifat hazari
7 篇 hamdulla askar
7 篇 chakravarthi bha...
6 篇 kumaresan prasan...
6 篇 ablimit mijit
6 篇 ahmad tohari
6 篇 gao yanxia
6 篇 liu yiwen
5 篇 abir ahmed
5 篇 vikrant bhateja
5 篇 suresh chandra s...
5 篇 wang wei
5 篇 bekkay hajji
5 篇 chang chin-chen
5 篇 ramakrishnan ram...
5 篇 md. saniat rahma...
5 篇 zhang ying
4 篇 li li
4 篇 priyadharshini r...
4 篇 liu xin

语言

2,863 篇 英文
85 篇 其他
15 篇 中文
1 篇 俄文

检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

共 2955 条记录，以下是41-50 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：

Comparative Analysis of Neural Network Models for Error Probability Prediction in Vehicular Communication 3rd

Comparative Analysis of Neural Network Models for Error Prob...

引用

3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Reshma, P. Gautam, Jatin Sudha, V. National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9789819719426

Reliable data transfer is a tedious task for various applications that involve users in motion. In vehicular communication systems, for reliable and seamless data transfer an accurate estimation strategy is needed. The principal objective involves construction of the predictive models that can estimate error probabilities on parameters like signal strengths, timestamps, and sender identification outcomes. To establish a proper strategy for vehicle scenarios while maintaining the system’s effectiveness, we present an analysis of our system with error probabilities, exploring the relationship between signal strength, neural network (NN) architectures, and error prediction accuracy. data generated is used to simulate communication messages. Furthermore, this paper boards to calculate the effectiveness of different NN architectures, including single and multi-layer perceptrons, interpreting key relations embedded in data and providing precise error probability predictions. Through a series of insights, we observed signal strength and NN impact on error predictions. Our analysis improves the complex error pattern occurrences and identifies the most suitable architecture for accurate predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy Error prediction Neural networks Reliable data transfer signal strength

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hybrid Beamforming Optimization in Millimeter Wave MIMO Using Deep Deterministic Policy Gradient Based Deep Reinforcement Learning 3

Hybrid Beamforming Optimization in Millimeter Wave MIMO Usin...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Riadhusin, Rame Sudhakar, K. Prakash, D. Bhanu Kumar, S. Senthil Rajasubha, J. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331508456

In recent years, the rapid growth of Hybrid Beamforming (HB) technology has developed rapidly to improve high spectrum efficiency and data transmission in wireless communication systems. Massive Multiple Input Multiple Output (MIMO) systems are integrated with HB to encourage wireless network performance by improving signal quality. However, combination of digital and analog beamforming increases hardware complexity and quantization errors in multiple streams. Hence, this research proposes Deep Reinforcement Learning (DRL)-based hybrid beamforming optimization framework to learn relevant Channel State Information (CSI) by learning complex non-linear relationships and beam forming weights dynamically. Initially, Deep Multiple Input Multiple Output (Deep MIMO) dataset which consists of channel measurements and capacity to receive and transmit signal processing is used. After that, preprocessing is performed using Amplitude Scaling (AS) for signal compression and Phase Normalization (PN) to reduce phase noise in multiple frequencies. Finally, Deep Deterministic Policy Gradient (DDPG)-based DRL model is used to perform HB optimization through adaptive learning policy by improving sum rate and spectral efficiency while reducing quantization errors. Proposed method results with the Sum-rate (5.35 bps/HZ), Accuracy (99.26%) and Inference time (0.03 sec) when compared with existing CNN-Bi-directional Long Short-Term Memory (CNNBiLSTM). © 2025 IEEE.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Improved Support Vector Machine for ECG signal Classification in Implantable Biomedical Devices 3

Improved Support Vector Machine for ECG Signal Classificatio...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Patchamatla, Pavan Srikanth SubbaRaju Balsem, Zayd Ajsan Parthiban, K.G. Ramachandra, A.C. Latha Bhuvaneswari, S. RKInfo-Tech Llc India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College Of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Electrocardiogram (ECG) signal classification in implantable biomedical devices plays a significant role for continuous heart monitoring and detecting cardiac abnormalities such as arrhythmias. These devices analyse ECG patterns to enable accurate diagnostics and provide necessary interventions. However, traditional ECG monitoring tools faces certain challenges like low power transfer efficiency, failure in capturing most paroxysmal Atrial Fibrillation (AFib), and difficulty in automatic determination of AFib. To solve this issue, the implantable ECG sensor with improved Support Vector Machine (SVM) is proposed to monitor the patient continuously and detect AFib effectively. The QRS complex is observed from ECG signals of 29 AFib patients is extracted by utilizing signal processing techniques such as wavelet transform and statistical analysis. The Minimum Description Length (MDL) method is applied to extract and select 12 features of variability-based heart rate parameters. Then, the improved SVM classifier with a Radial Basis Function (RBF) is used to classify the AFib in ECG data accurately. Experimental results demonstrate that the proposed implantable ECG sensor with optimized SVM achieves a better accuracy of 99.2%, sensitivity of 98.8%, specificity of 99.5%, and an F1-score of 98.9% which outperforms the existing methods like traditional SVM and CNN-based models. © 2025 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic signal Analysis 3

IoT-Integrated Convolutional Neural Network for Accurate Veh...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sasikala, P. Sharmini, K Subha Ponnappan, Venkatesan Sorakka Kavitha, K.R. Gunasekaran, K. Srinivasan, C. Department of Mathematics Salem India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Sathyabama Institute of Science and Technology Department of Mechanical Engineering Chennai India Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health;nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Image Extraction for Linear Segment in 3D Space 3rd

Image Extraction for Linear Segment in 3D Space

引用

3rd international conference on signal and data processing, icsdp 2023

作者： Mamatha, Elliriki Amardeepak, M. Sharma, Rohit Krishna Anand, S. Department of Mathematics GITAM University Bengaluru India Department of Mechanical Engineering CIT-NC Bengaluru India Department of Engineering Garden City University Bangaluru India Department of AI & DS Shridevi Institute of Engineering and Technology Tumkur India

ISBN: (纸本)9789819795772

In computer graphics systems, cropping applications appear to be quite simple from a human perspective as human eyes can easily visualize whether a portion of the picture is inside or outside. However, from a computational perspective, the problem increases multifold as a simple process involves a large number of comparisons and computations. With the added importance given to floating point numbers, the complexity shoots up exponentially. With this viewpoint in perspective, an analytical model has been designed. This model incorporates algorithms using integral calculations in order to find intermediate intersection points. This in turn would improve cropping objects’ speed in three-dimensional spaces. The performance of this algorithm is expected to be robust and can handle computations at a much higher speed for any size of rectangular parallelepiped cropping windows. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D Space CAD/CAM Geometrical slopes Graphics systems Intersection points Object cropping

来源：评论

学校读者我要写书评

暂无评论

Meta-Heuristic Approaches to Parallel processing in signal Representation and Real-Time Analysis 3

Meta-Heuristic Approaches to Parallel Processing in Signal R...

引用

3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Harisha Naik, N.T. Dev, Sudhanshu Shivam, Aniket Singh, Neelesh Shreenidhi, H.S. Mishra, Awakash Bengaluru India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Meta-heuristic techniques have been popular in solving highly complex optimization problems. Parallel processing is very important in signal representation and real-time analysis, as it helps lower the time required for the data process so that algorithms can perform faster. The biggest challenge with the traditional parallel processing techniques is load balancing and higher communication overhead, which may need to scale up to a larger extent. Meta-heuristic techniques reduce the complexity of problems that are NP-complete or NP-hard by providing satisfactory solutions and approximate answers to difficult ones. This has helped in parallel processing for signal representation and real-time analysis. Parallel processing has also been useful in using meta-heuristic algorithms like genetic algorithms, ant colony optimization, and particle swarm optimization. Meta-heuristic algorithms produce flexible, adaptive mechanisms for solving complex problems. © 2025 IEEE.

关键词： signaling

来源：评论

学校读者我要写书评

暂无评论

The Multi-class Paradigm: How Transformers Are Reshaping Language Analysis in NLP 3rd

The Multi-class Paradigm: How Transformers Are Reshaping La...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Islam, Mohammad Shariful Rony, Mohammad Abu Tareq Saha, Pritom Ahammad, Mejbah Nazmul Alam, Shah Md. Bappi, Jabed Omor Siddiki, Marjuk Ahmed Noakhali3814 Bangladesh Noakhali3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University TempeAZ United States Electrical and Electronic Engineering Port City International University Chittagong Bangladesh

ISBN: (纸本)9789819739363

Language-based multilabel text classification is a crucial topic in natural language processing (NLP). However, unlike English, the development of natural language processing in Bengali is still in its infancy, and there is little research done specifically for the Bengali language, one of the most widely spoken languages in the world. As a consequence, it’s past time to address this issue for effective information management and data structure. This research presents a novel human-annotated Bangla sentence dataset. The dataset was categorized into five classes (assertive, interrogative, imperative, optative, or exclamatory) containing 10,000 sentences based on the function and purpose of Bangla grammar context. Fine-tuning applied six transformer-based pre-trained multilingual and monolingual BERT models to this multilabeled dataset. The study evaluated the effectiveness of various models, including BERT, AlBERT, RoBERTa, DistilBERT, XLNet, and BanglaBERT, in sentence classification tasks. The results showed that BanglaBERT outperformed all other models with an accuracy of 99.03%. The study concluded that BanglaBERT is superior in achieving exceptional accuracy and precision, surpassing the capabilities of other popular machine learning models such as LSTM, RNN, SVC, DT, KNN, and RF, as well as other BERT models of Bangla text classification. BERT-based models have great potential in understanding complex language nuances and context and are critical to advancing natural language processing tasks, especially in Bangla language processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

The Present Investigation Investigates the Utilization of Big data-Driven Clustering Algorithms in the Field of Electrical Engineering Automation 3rd

The Present Investigation Investigates the Utilization of Bi...

引用

3rd international conference on Information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Cao, Yifan Metallurgy Automation Research and Design Institute Co. Ltd. Beijing100071 China National Key Laboratory of Metallurgical Intelligent Manufacturing System Beijing100071 China

ISBN: (纸本)9789819746194

This study examines the application of big data clustering algorithms in the field of electrical engineering automation. Electrical engineering automation is a key area that covers power system management, smart grids, and industrial automation, and requires effective data processing and analysis methods to optimise operations and decision-making. Big data clustering algorithms have been introduced to cope with the huge datasets generated in electrical engineering, as well as the diversity and complexity in the data. Big data clustering can be used for power load forecasting to help utilities better plan resources and reduce energy waste. It can also be used for equipment fault detection in smart grids to improve grid reliability and stability. In addition, the study highlights challenges such as data quality and privacy protection, which are important issues to consider when adopting big data clustering algorithms in electrical engineering automation. Finally, the study summarises the potential value of big data clustering algorithms in electrical engineering automation and suggests directions for future research to further advance the field. This study provides insights and guidance to professionals in the field of electrical engineering automation on how big data clustering algorithms can be utilised to improve productivity and decision making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Automated Number Plate Detection and Recognition Using Machine Learning Techniques 3rd

Automated Number Plate Detection and Recognition Using Machi...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Shuva, Taslima Ferdaus Hasan, Nahid Rahman, Md. Tanvir Khan, Risala Tasin Daffodil International University Dhaka Bangladesh Mawlana Bhashani Science and Technology University Tangail Bangladesh Jahangirnagar University Savar Bangladesh

ISBN: (纸本)9789819739363

The official collection of numbers and letters shown on the front and rear of a road vehicle is called a license plate. License plate recognition is crucial for many applications, including traffic monitoring, vehicle tracking, and law enforcement. Automatic License Plate Recognition (ALPR) is one of the applications that benefited tremendously from convolutional neural network (CNN) processing, which is now the de facto processing method for complex data. This paper offers a comprehensive analysis and comparison of deep learning-based approaches for identifying license plates. The study also looks a dataset from Kaggle, pre-process the dataset for removing noise and artifact, utilized to train these models into test, train, and validation dataset. Additionally, the study examines the difficulties and prospective possibilities in deep learning-based license plate recognition, highlighting the potential uses of cutting-edge innovations such object detection using attention mechanisms and graph convolutional networks. In this study we utilized six transfer learning model which is VGG16, VGG19, DenseNet121, AlexNet, EfficientNet, and YOLOv4 where DenseNet121 provide the best or highest accuracy 98.66%. The goal of the study is to guide researchers and industry professionals toward powerful license plate detecting systems by employing deep learning approaches by way of this evaluation and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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296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：