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

4,364 篇 会议
99 篇 期刊文献
3 册 图书
2 篇 学位论文

馆藏范围

4,468 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,345 篇 工学
- 1,092 篇 信息与通信工程
- 1,060 篇 计算机科学与技术...
- 704 篇 电气工程
- 642 篇 软件工程
- 442 篇 控制科学与工程
- 278 篇 测绘科学与技术
- 255 篇 仪器科学与技术
- 247 篇 电子科学与技术（可...
- 206 篇 光学工程
- 202 篇 机械工程
- 197 篇 交通运输工程
- 169 篇 航空宇航科学与技...
- 122 篇 土木工程
- 96 篇 建筑学
- 86 篇 安全科学与工程
- 75 篇 材料科学与工程（可...
- 69 篇 生物工程
- 63 篇 环境科学与工程（可...
- 59 篇 生物医学工程（可授...
1,011 篇 理学
- 643 篇 物理学
- 191 篇 数学
- 73 篇 生物学
- 58 篇 统计学（可授理学、...
- 57 篇 大气科学
- 56 篇 化学
- 55 篇 系统科学
297 篇 管理学
- 201 篇 管理科学与工程(可...
- 123 篇 图书情报与档案管...
- 109 篇 工商管理
255 篇 医学
- 247 篇 临床医学
37 篇 农学
29 篇 经济学
26 篇 军事学
25 篇 法学
12 篇 教育学
8 篇 文学

主题

548 篇 laser radar
517 篇 visualization
383 篇 data visualizati...
342 篇 radar
302 篇 sensors
301 篇 sensor systems
281 篇 wireless sensor ...
261 篇 three-dimensiona...
232 篇 sensor fusion
229 篇 synthetic apertu...
224 篇 cameras
218 篇 radar tracking
212 篇 real-time system...
207 篇 monitoring
161 篇 radar imaging
160 篇 robot sensing sy...
159 篇 accuracy
152 篇 target tracking
132 篇 feature extracti...
121 篇 object detection

机构

26 篇 penn state univ ...
18 篇 res lab adelphi ...
16 篇 lincoln laborato...
15 篇 sandia natl labs...
14 篇 us army res lab ...
9 篇 penn state univ ...
9 篇 jet propulsion l...
8 篇 consultant
8 篇 sandia natl labs...
7 篇 school of inform...
6 篇 university of sc...
6 篇 sandia natl labs...
6 篇 shibaura institu...
6 篇 research center ...
6 篇 massachusetts in...
6 篇 sandia natl labs...
6 篇 villanova univ c...
6 篇 univ st andrews ...
5 篇 department of ea...
5 篇 beijing key labo...

作者

32 篇 narayanan ram m.
20 篇 ranney kenneth
13 篇 sherbondy kelly ...
12 篇 zhang yan (rocke...
12 篇 doerry a. w.
11 篇 ahmad fauzia
10 篇 dogaru traian
10 篇 gallagher kyle a...
9 篇 amin moeness g.
9 篇 kirose getachew
8 篇 gerd wanielik
8 篇 lam nguyen
8 篇 martone anthony
8 篇 nguyen lam
8 篇 sherbondy kelly
7 篇 ming liu
7 篇 nguyen lam h.
7 篇 rahman samiur
7 篇 robert weigel
7 篇 ryoichi shinkuma

语言

4,394 篇 英文
34 篇 其他
33 篇 中文
3 篇 德文
3 篇 土耳其文
2 篇 西班牙文

检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

共 4468 条记录，以下是101-110 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：

Three-Dimensional Point Cloud Map Construction Based On Multi-sensor Coupling

Three-Dimensional Point Cloud Map Construction Based On Mult...

引用

sensors and Information technology (ICSI), International conference on

作者： Keyuan Zhang Honghua Zhao Sai Zhang Yulong Du Na Wang Lianjiang Zhu School of Mechanical Engineering University of Jinan Ji Nan China Jinan Robot Phoenix Technology Co. Ltd. Ji Nan China Qingdao SIASUN Robot Automation Co. Ltd Qing Dao China Information Management Department University of Jinan Jinan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

The development of robot technology has provided new ideas for unmanned operations in the environment of biomedical cold storage. Focusing on the sample transportation requirements in the complex environment of biological laboratories, this study centers on the biological sample transportation robot, delving into the construction of a three-dimensional point cloud map based on LiDAR, and initially builds a three-dimensional point cloud map simulating the cold storage environment, providing a solid foundation for the subsequent autonomous navigation of the transportation robot. Firstly, the principle of data acquisition by LiDAR is analyzed, the generated point cloud map is preprocessed, the NDT registration algorithm is adopted for the construction of the point cloud map, and finally, the information coupling of LiDAR, IMU and wheel odometry is carried out. This algorithm has a faster front-end registration speed, with a stable loop closure positioning error within 5 cm for every kilometer, and higher accuracy and robustness.

关键词： Point cloud compression Couplings Laser radar Three-dimensional displays Accuracy Transportation Wheels Solids Biology Autonomous robots

来源：评论

学校读者我要写书评

暂无评论

Indoor Navigation System for KCT Using AR Techniques

Indoor Navigation System for KCT Using AR Techniques

引用

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Anitha P Madhav S Nishanth M V GowriShankar S Sanjai Kumar S Jaidev M V Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore TamilNadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Moving around in confusing indoor spaces like malls, airport, campuses is very difficult if the setting is unknown. This system gives real-time turn-by-turn guidance on mobile phones using Unity with AR Foundation and NavMesh. LiDAR cameras enable accurate 3D mapping, and a GPS, Wi-Fi and Bluetooth beacon hybrid positioning system enables accurate pinpoint positioning in GPS-denied environments. The study seeks to improve user experience by simple, real-time guidance and context information as AR visualization. The system improves user disorientation and speeds up navigation speed by superimposition of directional signs over the real-world environment. Major conclusions highlight the precision and consistency of the system, specifically in vast indoor environments. The power of the system is that it can substitute conventional maps and signs to make the process of navigating seamless. Scalability and leveraging AI for smart routing and natural interaction will be addressed in subsequent work, a revolution of AR navigation technology. The study opens up a possibility for indoor navigation solutions to become more intuitive and user-centric.

关键词： Three-dimensional displays Laser radar Accuracy Indoor navigation Routing Airports Real-time systems Augmented reality Wireless fidelity Global Positioning System

来源：评论

学校读者我要写书评

暂无评论

MNCL: Empirical Development of Human Action Recognition Based on Postures Using Modified Neural Classification Logic

MNCL: Empirical Development of Human Action Recognition Base...

引用

Visual Analytics and data visualization (ICVADV), International conference on

作者： S.Rama Krishna M. Pavithra Arul Prasath A Jayendra Gopal Thatipudi Gopinath D K. Rajkumar Department of Computer Science & Engineering GSoT GITAM(Deemed-to-be-University) Hyderabad Dept. of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Dept. of Computer Science University of North Texas Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India Department of ECE School of Engineering SR University Telangana

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In this research, we introduce an innovative method for the recognition of human actions based on skeletal joint data, which offers greater privacy compared to color photos. The methodology includes defining an activity using a sequence of instructive fundamental postures, and combining machine learning methods for activity recognition. Based on normalized skeletal data points, posture features such as absolute joint positions, relative joint positions, and joint angle quaternions are proposed to derive. Feature dimensionality and system complexity are reduced through posture selection, followed by temporal modeling to minimize the effects of rate variation, temporal misalignment and noise. A Modified Neural Classification Logic (MNCL) model is proposed, based on certain principles, to identify actions. Position- and angle-based kinematics are suggested, with angle based features that are naturally normalized and are independent of an individual's physique. To reduce posture similarity and make the method more general, a posture selection method based on the K-means clustering algorithm is implemented. The proposed MNCL model links weights of the MNCL model between the previous layer and the current layer using a linear function instead of the non-linearity provided in the original MNCL model. An innovative inference engine for logical connections based decision making is used to improve the output layer recognition rate. In this work, we propose a system for human action recognition based on skeletal joint properties, intended to provide an efficient and privacy preserving solution.

关键词： Visual analytics Quaternions Clustering algorithms Kinematics sensor phenomena and characterization Feature extraction Skeleton Logic Human activity recognition Videos

来源：评论

学校读者我要写书评

暂无评论

Positioning Algorithm of Cold Source Underwater Pollution Inspection Robot Based on Multi-sensor Fusion

Positioning Algorithm of Cold Source Underwater Pollution In...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Jiale Jian Jia Yang Yu Zhang Yinfan Jian China Nuclear Power Engineering Co. Ltd. Beijing Hainan Nuclear Power Co. Ltd. Hainan Province Shanghai Jingyi Intelligent Control Technology Co. Ltd. Shanghai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the rapid development of robotic systems, their application areas have expanded from traditional land to underwater, and have achieved better application range. In order to expand the application scenarios, this study proposes a cold source underwater pollution detection robot positioning algorithm based on multi-sensor fusion. The algorithm aims to solve the shortcomings of existing robots in sensor fusion and positioning accuracy, and provide a reasonable path. First, this study designed a mathematical model of the six-degree-of-freedom motion of the underwater robot based on mathematical modeling. In the optimization part, the fourth-order Runge-Kutta method was used to achieve a stable solution, and it was extended to robot control, and the PID algorithm was used to optimize the motion control framework. Subsequently, the designed model improves the accuracy of robot path planning and ground point cloud removal by introducing a 3D point cloud to 2D grid conversion algorithm, thereby achieving stable positioning. Then, the model proposes a multi-sensor fusion algorithm for underwater acoustic channels, combining multiple sensor data to effectively address the challenges of signal multi-path effects and interference. The experiment is carried out in a simulated underwater environment. The test results showed that the proposed algorithm performed well in terms of positioning accuracy and operation stability. At the same time, a visualization simulation of underwater robot path was carried out to prove the universality of the proposed framework.

关键词： Point cloud compression Autonomous underwater vehicles Accuracy Pollution Service robots Inspection Robot sensing systems Mathematical models Stability analysis Robots

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Automated Smart Parking Fee Management System using RFID technology

IoT-Based Automated Smart Parking Fee Management System usin...

引用

IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Akshaj Singh Bisht Armaano Ajay S. Prasanna Bharathi School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai India School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Parking systems are essential for urban infrastructure, ensuring efficient vehicle management and optimal use of space. As urban areas become more congested, there is an increasing need for organised and automated parking solutions. Traditional parking systems are primarily manual, resulting in inefficiencies, long wait times and security issues. The paper presents an IoT-based smart car parking fee collection system that automates the parking and fee collection process. The proposed system employs Arduino Uno and ESP32 microcontrollers with RFID technology to regulate parking entry and exit, ensuring secure and authorised access. It is integrated with ThingSpeak to enable real-time data collection and visualisation, providing administrators with valuable insights into parking usage and fee collection. Parking fees are calculated based on the duration of stay and sent to users via email, promoting an environmentally friendly and paperless solution. The system includes an automated barrier control using servo motors, smart lighting control with LDR sensors and email notifications through the ESP32 module. This system enhances the user experience by reducing wait times and providing a seamless parking process while offering administrators the tools needed for effective parking management. This comprehensive solution effectively addresses the challenges of conventional parking systems and meets the growing demands of modern urban environments.

关键词： Vehicle detection Urban areas data visualization Manuals Automated parking Real-time systems sensor systems Security Intelligent sensors Radiofrequency identification

来源：评论

学校读者我要写书评

暂无评论

Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras

Event Masked Autoencoder: Point-wise Action Recognition with...

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingkai Sun Qiang Zhang Jiaxu Wang Jiahang Cao Hao Cheng Renjing Xu Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dynamic vision sensors (DVS) are bio-inspired devices that capture visual information in the form of asynchronous events, which encode changes in pixel intensity with high temporal resolution and low latency. These events provide rich motion cues that can be exploited for various computer vision tasks, such as action recognition. However, most existing DVS-based action recognition methods lose temporal information during data transformation or suffer from noise and outliers caused by sensor imperfections or environmental factors. To address these challenges, we propose a novel framework that preserves and exploits the spatiotemporal structure of event data for action recognition. Our framework consists of two main components: 1) a point-wise event masked autoencoder (MAE) that learns a compact and discriminative representation of event patches by reconstructing them from masked raw event camera points data; 2) an improved event points patch generation algorithm that leverages an event data inlier model and point-wise data augmentation techniques to enhance the quality and diversity of event points patches. To the best of our knowledge, our approach introduces the pre-train method into event camera raw points data for the first time, and we propose a novel event points patch embedding to utilize transformer-based models on event cameras.

关键词： visualization Autoencoders Speech recognition Vision sensors Cameras Transformers data models Spatiotemporal phenomena Voltage control Speech processing

来源：评论

学校读者我要写书评

暂无评论

Efficient Human Activity Recognition through Multi-sensor data and Deep Learning Techniques

Efficient Human Activity Recognition through Multi-Sensor Da...

引用

Advanced Computing Technologies (ICoACT), International conference on

作者： Rhenius A Anitha J Immanuel Alex Pandian S CSE KITS Coimbatore India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Extensive research takes place in the field of Human Activity Recognition (HAR) systems with wearable computing that helps in healthcare applications, fitness tracking and smart environments. sensor technology advances have propelled Human Activity Recognition systems to utilize innovative machine learning methods for accurate and robust activity classification. This work evaluates the effectiveness of four cutting-edge deep learning models-Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU)-through their implementation on publicly accessible accelerometer and gyroscope HAR datasets. These classification models underwent testing through different activation functions namely ReLU, Leaky ReLU, and Swish to assess their practicality for classification operations. Standard evaluation metrics including accuracy, precision, recall, ROC-AUC and F1-score demonstrate the best features of each implemented model while providing descriptive visual representations. TCN delivered 98.59% accuracy and exhibited superior training speed efficiency thus establishing itself as the suitable AI algorithm for real-time implementation. This research offers an extensive breakdown for choosing optimal models in real-time HAR systems which supports advances in wearable computing and activity recognition technologies.

关键词： Deep learning Training visualization Accuracy Computational modeling Real-time systems Human activity recognition Convolutional neural networks Wearable devices Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Synergistic Techniques for Landmass Movement Detection: A Review towards Sustainable Slope Stability Monitoring

Synergistic Techniques for Landmass Movement Detection: A Re...

引用

National conference on GeoPractices for Sustainable Infrastructure, GeoPrac 2024

作者： Kundu, Priyajit Kolathayar, Sreevalsa Pruthviraj, Umesh Department of Civil Engineering National Institute of Technology Karnataka Surathkal India Department of WROE National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9789819632190

The lives of people, infrastructure, and economic stability are seriously threatened by compromised slope stability. This review explores various techniques used for landmass movement detection, highlighting their strengths and limitations. The paper emphasises on the importance of accurate and timely detection of unstable slopes for effective risk mitigation and early warning systems. Traditional methods like field surveys and topographic mapping are obsolete but crucial for understanding baseline conditions. Satellite-based remote sensing techniques like Interferometric Synthetic Aperture radar (InSAR) and Light Detection and Ranging (LiDAR) offer vast area coverage and can detect subtle ground deformations, enabling rapid response strategies. Ground-based sensor networks with real-time monitoring capabilities complement these methods for slope stability assessment. Satellite-based detection methods using techniques like Differential Global Positioning System (DGPS) and Real-Time Kinematic (RTK) offer valuable insights for researchers to select and deploy monitoring suited for specific unstable slopes. Additionally, the Internet of Things (IoT) is revolutionising ground movement detection by enabling seamless sensor-to-central data connectivity for real-time monitoring and analysis. The accuracy of detection of landmass movement is increasing with advancements in detection techniques. Remotely sensed methods provide advantages like remotely detecting movement in difficult terrain but come with shortcomings in real-time measurement. Methods based on sensors and IoT are better suited for real-time monitoring, but devices are comparatively costly and difficult to install. It is suggested that research towards ensemble systems can solve the problem with real-time forecasting of upcoming geohazards and provide a sustainable solution for landslide problems in hilly regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential Global Positioning System (DGPS) Global Navigation Satellite System (GNSS) Interferometric Synthetic Aperture radar (InSAR) Internet of Things (IoT) Landmass movement Real-Time Kinematic (RTK)

来源：评论

学校读者我要写书评

暂无评论

Flood Forecasting System based on Water Monitoring using IoT-Machine Learning

Flood Forecasting System based on Water Monitoring using IoT...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： R. Ramya Mohamed Thanseem K Vimalraj V Vinith Kumar S Electronics and Communication Engineering K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Floods are among the most devastating natural disasters, resulting in massive loss of life, widespread property damage, and overall economic disruption Conventional flood monitoring systems, which depend on manual data gathering and old models, tend to lag behind interventions This project suggests an IoT-based flood forecasting system for real-time monitoring, precise predictions, and timely warnings. IoT sensors monitor water level, rainfall, and moisture in flooded regions and send information to a cloud platform for processing. Machine learning models analyze the data, combining hydrological, meteorological, and geographic variables to improve prediction. The system employs low-power IoT sensors with communication protocols such as LoRa WAN, Zigbee, or cellular networks for real-time monitoring. Modular and scalable, it adjusts to varied environments. visualization interfaces and mobile/web interfaces offer decision-makers and community’s clear data and timely notifications, enabling proactive disaster management, evacuation planning, and resource allocation. Simulated and tested through real-life applications, the system proves to have efficient early flood detection, minimizing damage and enhancing resilience in limited-resource areas.

关键词： Wide area networks Accuracy Rain Biological system modeling Predictive models sensor systems Real-time systems sensors Floods Forecasting

来源：评论

学校读者我要写书评

暂无评论

Extracting and Classifying 3D Medical Image Texture Characteristics using GLCM and LBP-Based Techniques

Extracting and Classifying 3D Medical Image Texture Characte...

引用

data Science, Agents & Artificial Intelligence (ICDSAAI), International conference on

作者： R. Srinath R. Sharmila N. Raghavendran M. C. Babu K. Ramanan R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recent advances in 3D sensor technology have advanced 3D imaging systems. This sparks interest in 3D picture feature extraction and categorization. According to the literature, textural content is essential to visuals. In this case, we extract texture characteristics from three-dimensional photos to improve feature differentiation. Category of volumetric data with texture is possible using this method. We employ feature vectors from Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix methods to achieve this. The visual pattern and volumetric data contrast, homogeneity, and local anisotropy are described. The proposed approach was tested using a publicly accessible dataset of modified volumetric textured pictures. Generative Adversarial Networks (GAN) and recurrent Neural Networks (RNN) are classified. Our solution outperforms manually constructed techniques for extracting texture characteristics from 3D and 2D data and standard deep-learning networks. Even with few photographs per class, the recommended method improves class distinction and yields good results. Show exceptional accuracy. Integrating the Gray Level Co-occurrence Matrix (GLCM) with Local Binary Patterns (LBP) improved classification performance. The GLCM approach, which captures spatial relationships, and the LBP method, which depicts local texture, was advantageous. This hybrid method improved feature extraction and classification, notably for complex 3D medical pictures with subtle texture changes.

关键词： Deep learning visualization Three-dimensional displays Machine learning algorithms Recurrent neural networks Liver Feature extraction Generative adversarial networks Classification algorithms Tumors

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447页 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：