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

4,745 篇 会议
55 册 图书
12 篇 期刊文献

馆藏范围

4,812 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,154 篇 工学
- 2,814 篇 计算机科学与技术...
- 2,228 篇 软件工程
- 1,265 篇 信息与通信工程
- 761 篇 控制科学与工程
- 691 篇 生物工程
- 666 篇 光学工程
- 530 篇 电气工程
- 454 篇 生物医学工程（可授...
- 419 篇 电子科学与技术（可...
- 300 篇 网络空间安全
- 182 篇 安全科学与工程
- 164 篇 机械工程
- 160 篇 仪器科学与技术
- 159 篇 交通运输工程
- 153 篇 动力工程及工程热...
- 153 篇 化学工程与技术
- 143 篇 石油与天然气工程
1,643 篇 理学
- 709 篇 生物学
- 636 篇 物理学
- 605 篇 数学
- 242 篇 化学
- 180 篇 统计学（可授理学、...
828 篇 管理学
- 464 篇 图书情报与档案管...
- 443 篇 管理科学与工程(可...
- 119 篇 工商管理
434 篇 医学
- 363 篇 临床医学
- 246 篇 基础医学(可授医学...
- 214 篇 药学(可授医学、理...
- 106 篇 公共卫生与预防医...
125 篇 法学
- 109 篇 社会学
95 篇 农学
45 篇 经济学
44 篇 文学
33 篇 教育学
12 篇 军事学

主题

321 篇 image processing
312 篇 deep learning
230 篇 image segmentati...
191 篇 signal processin...
172 篇 image classifica...
167 篇 image enhancemen...
162 篇 computer vision
140 篇 image recognitio...
138 篇 convolutional ne...
136 篇 feature extracti...
125 篇 training
115 篇 machine learning
96 篇 computer applica...
83 篇 neural networks
83 篇 computational mo...
79 篇 visualization
78 篇 object detection
71 篇 natural language...
69 篇 semantics
68 篇 artificial intel...

机构

24 篇 chitkara univers...
17 篇 maharishi school...
15 篇 lovely professio...
15 篇 tianjin universi...
13 篇 university of ch...
12 篇 the islamic univ...
12 篇 beijing institut...
11 篇 school of comput...
11 篇 centre of interd...
10 篇 peng cheng labor...
10 篇 graphic era hill...
9 篇 national univers...
9 篇 sun yat-sen univ...
9 篇 school of comput...
9 篇 vivekananda glob...
9 篇 college of compu...
8 篇 harbin engineeri...
8 篇 school of comput...
8 篇 maharishi school...
8 篇 gla university d...

作者

13 篇 chauhan rahul
11 篇 yang yang
10 篇 anand vatsala
8 篇 kukreja vinay
8 篇 gupta rupesh
7 篇 luo xiaonan
7 篇 zhao yang
6 篇 bin wang
6 篇 pei jihong
6 篇 kumar ankit
6 篇 choudhury tanupr...
6 篇 zhu ce
6 篇 ce zhu
6 篇 kumar gaurav
6 篇 bing li
6 篇 li wei
5 篇 gill kanwarparta...
5 篇 zhiguo cao
5 篇 nagaraj p.
5 篇 singh kamred udh...

语言

4,761 篇 英文
50 篇 其他
17 篇 中文
1 篇 土耳其文

检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是4651-4660 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：

CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images

CG-3DSRGAN: A classification guided 3D generative adversaria...

引用

Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Yuxin Xue Yige Peng Lei Bi Dagan Feng Jinman Kim School of Computer Science Faculty of Engineering The University of Sydney Sydney Australia Institute of Translational Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China School of Information Technologies The University of Sydney Med-X Research Institute Shanghai Jiao Tong University

Positron emission tomography (PET) is the most sensitive molecular imaging modality routinely applied in our modern healthcare. High radioactivity caused by the injected tracer dose is a major concern in PET imaging and limits its clinical applications. However, reducing the dose leads to inadequate image quality for diagnostic practice. Motivated by the need to produce high quality images with minimum ‘low-dose’, convolutional neural networks (CNNs) based methods have been developed for high quality PET synthesis from its low-dose counterparts. Previous CNNs-based studies usually directly map low-dose PET into features space without consideration of different dose reduction level. In this study, a novel approach named CG-3DSRGAN (Classification-Guided Generative Adversarial Network with Super Resolution Refinement) is presented. Specifically, a multi-tasking coarse generator, guided by a classification head, allows for a more comprehensive understanding of the noise-level features present in the low-dose data, resulting in improved image synthesis. Moreover, to recover spatial details of standard PET, an auxiliary super resolution network – Contextual-Net – is proposed as a second-stage training to narrow the gap between coarse prediction and standard PET. We compared our method to the state-of-the-art methods on whole-body PET with different dose reduction factors (DRF). Experiments demonstrate our method can outperform others on all *** Relevance— Low-Dose PET, PET recovery, GAN, task driven image synthesis, super resolution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

引用

Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), ieee international conference on

作者： Ankita Agarwal Aravindan M K Manish Shrivastava S. Sathya Devika A. Verma Jagmeet Sohal Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai - 127 Department of Computer Engineering Centre of Vishwakarma Institute of Information Technology Pune India Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN).

关键词： Support vector machines Machine learning algorithms Recurrent neural networks Statistical analysis Text categorization Prediction algorithms Natural language processing

来源：评论

学校读者我要写书评

暂无评论

MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure image

MassNet: A Deep Learning Approach for Body Weight Extraction...

引用

ieee international conference on Pervasive Computing and Communications (PerCom)

作者： Ziyu Wu Quan Wan Mingjie Zhao Yi Ke Yiran Fang Zhen Liang Fangting Xie Jingyuan Cheng School of Computer Science and Technology University of Science and Technology of China Hefei China

Body weight, as an essential physiological trait, is of considerable significance in many applications like body management, rehabilitation, and drug dosing for patient-specific treatments. Previous works on the body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especially privacy issues. The pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface, which strongly correlates with the body weight of the lying person. To extract the body weight from this image, we propose a deep learning-based model, including a dual-branch network to extract the deep features and pose features respectively. A contrastive learning module is also combined to the deep-feature branch to help mine the mutual factors across different postures of every single subject. The two groups of features are then concatenated for the body weight regression task. To test the model's performance over different hardware and posture settings, we create a pressure image dataset of 10 subjects and 23 postures, using a self-made pressure-sensing bedsheet. This dataset, which is made public together with this paper, together with a public dataset, are used for the validation. The results show that our model outperforms the state-of-the-art algorithms over both 2 datasets. Our research constitutes an important step toward fully automatic weight estimation in both clinical and at-home practice. Our dataset is available for research purposes at: https://***/USTCWzy/MassEstimation.

关键词： Pervasive computing Drugs Privacy Estimation Lighting Feature extraction Physiology

来源：评论

学校读者我要写书评

暂无评论

Application of Remote Sensing image processing for Classification and Recognition

Application of Remote Sensing Image Processing for Classific...

引用

international conference on Advanced Infocomm Technology (ICAIT)

作者： Xiaolong Shi Dan Huang Wenjie Li Xianjie Wang College of Computer Science and Engineering Chongqing University of Technology Chongqing China China Research and Development Academy of Machinery Equipment Beijing China Chongqing high-tech Zone Pegasus Innovation Institute Chongqing University of Technology Chongqing China

Aiming at the difficulties in object detection and recognition in remote sensing images caused by high background complexity, large scale variations of targets, and the presence of numerous small objects, an improved method for remote sensing image object detection based on YOLOv7-tiny is proposed. This method combines the loss function based on normalized Gaussian Wasserstein distance (NWD) with the CIoU loss function to address the problem of sensitivity to positional deviation of small objects by IoU-Loss. The addition of a global attention mechanism (GAM) in the backbone network reduces information diffusion and enhances the interaction at the global dimension to mitigate the interference of complex backgrounds in remote sensing images on the model, enabling the model to focus on the feature extraction of the desired targets. Finally, the coupled detection head (Coupled Head) of the model is replaced with a decoupled detection head (Decoupled Head), allowing the classification and regression tasks to output from different branches to achieve decoupling and avoid the decrease in detection accuracy caused by conflicts between classification and regression. The experimental results of this method on the public dataset DIOR achieved 88.73% accuracy, which is an improvement of 1.78% compared to the unimproved method's accuracy of 86.95%. Furthermore, compared to other researchers' methods tested on DIOR, the proposed method also shows improvement, thus validating its effectiveness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Space-operating Linux: An Operating System for computer Vision on Commercial-Grade Equipment in LEO

Space-operating Linux: An Operating System for Computer Visi...

引用

ieee conference on Aerospace

作者： Eric Miller Chris Heistand Deepak Mishra Small Satellite Research Laboratory University of Georgia GA STOKE Space Technologies WA

With recent exponential advances in AI-particularly with respect to the tremendous power and efficiency accessible for data processing-there is now a countless array of applications for aerospace missions and space exploration, even in experimental CubeSats. However, with the large volume of data acquisition required for satellite missions, downlinking presents an increasingly expensive bottleneck that drastically reduces mission efficiency. Hence, there is a skyrocketing demand to perform edge computation on board the payload system, often via a graphics processing unit (GPU). Multiple edge-computing CubeSat missions set to operate in Low Earth Orbit (LEO), including the University of Georgia's Multiview Onboard Computational imager (MOCI), house an Nvidia Jetson TX2i module to perform onboard computer vision. As is the case for many commercial off-the-shelf (COTS) devices used in CubeSats, the TX2i does not come radiation-hardened, and its most vulnerable component is its eMMC disk. Even with the option of radiation shielding, there is nevertheless a possibility of single event effects (SEEs) reaching the TX2i, calling for software-level mitigation as a final line of defense. The MOCI team and partners at Johns Hopkins University's Applied Physics Lab have developed Space Operating Linux (SOL), a minimized Yocto-based operating system with built-in redundancy designed to handle these environmental pressures. While SOL contains patches that enable real-time scheduling in Linux for time-sensitive reliability in flight, the methodologies of operating system minimization, software-based triple modular redundancy in persistent memory with associated bootloader modifications, and a RAM-based file system allow the device to rely less on its eMMC card and render it less prone to radiation-induced damage. Results from proton SEE tests on the device's chip exhibit lower expected error rates in LEO compared to stock devices. Additionally, the devices tested were less prone t

关键词： Space vehicles computer vision Operating systems Linux image edge detection Redundancy Low earth orbit satellites

来源：评论

学校读者我要写书评

暂无评论

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

引用

2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fuquan Wu, Yu Xu, Xinxin Wang, Chuansheng Ma, Minjia Grau, Antoni Huang, Jiayan School of Computer and Big Data Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (纸本)9798350386226

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices. © 2024 ieee.

关键词： Electrocardiography

来源：评论

学校读者我要写书评

暂无评论

CalorieMe: An image-based Calorie Estimator System

CalorieMe: An Image-based Calorie Estimator System

引用

ieee international conference on Intelligent Computing and Information Systems (ICICIS)

作者： Bavlly Magid Mohamed Ibrahim Yomna A. Kawashti Mazen Mohamed Mohamed Sabry Hanan Hindy Mazen Khaled Waleed Mohamed Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

In recent years, with the growing interest in healthy eating, various food photo recognition applications for tracking meals have emerged. However, some of these applications still require human intervention for calorie estimation, such as manual input or consultation with a nutrition expert. Furthermore, even automated systems often have limitations in food category recognition, or they demand multiple viewpoints for accurate results. Meanwhile, advancements in image recognition have been substantial, thanks to the advent of Convolutional Neural Networks (CNN). CNNs have significantly improved the accuracy of various image recognition tasks, including classification and object detection. This paper presents a comprehensive solution for estimating the calories in food photos containing multiple ingredients. The proposed method employs two deep learning models: one to detect ingredients and their respective locations, and another to segment the ingredients and measure their portion sizes. Moreover, the proposed method incorporates a reference object to enhance the precision of portion size measurement. Finally, the proposed model compares the food type and portion size against a dataset of food types and their corresponding calorific values per standard serving, thus estimating the total calorie count. In this study, the two-model methodology resulted in a 7% improvement in pixel accuracy and a 23% improvement in mean Intersection Over Union (mIOU) for recognition and segmentation tasks, respectively, compared to the latest state-of-the-art approach, which employed Deeplabv3+ exclusively on the same dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dense Convolution Neural Network for Complex land Cover Classification and change detection using Landsat OLI 8 hyperspectral image

Dense Convolution Neural Network for Complex land Cover Clas...

引用

New Frontiers in Communication, Automation, Management and Security (ICCAMS), international conference on

作者： K. Anitha Devi R. Priya Computer Applications School of Computing Sciences VISTAS Pallavaram Chennai Department of Computer Applications School of Computing Sciences VISTAS Pallavaram Chennai

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Hyperspectral image is employed to observe the earth surface with respect to spectral reflectance value on the electromagnetic spectral ranges of electromagnetic spectrum initiating on the visible regions to infrared region. It identifies the spectral feature space and classifies it with respect to minerals and land covers. Hyperspectral images have been proposed using existing unsupervised and supervised classifier towards classification. Multiple complications have been exploited on the hyperspectral images as it contains the Hughes phenomenon. In order to alleviate those problems, dense convolution neural network methodology is architected in this research. It capable of classifying the spectral images with reduced processing time. Initially pre-processing of the hyperspectral image is carried out to reduce the noise and improve the contrast of the hyperspectral images. preprocessed image is projected to linear discriminant analysis to extract the spectral end members and spatial endmembers of the image. Those extracted spectral and spatial endmembers is employed to Dense Connected Convolution Neural Network as it is highly capable in land cover classification and discrimination. Deep learning model containing the convolution layer and max pooling layer for feature map generation and fully connected layer is highly capable in generating the spectral indices to classify the land cover and detecting the land cover changes on the spatial features. Softmax layer employs the spectral index for classification and change detection. Cross entropy is employed to reduce the loss of the model. Further hyperparameter tuning is carried out using gradient decent to generate the high discriminant classes of the land cover. The experimental outcomes of the current model are verified on using hyperspectral image gathered from Landsat 8 OLI dataset. Performance of the current approach is evaluated with state of art approaches on measures like precision, recall and f measure on cro

关键词： Analytical models Convolution Computational modeling Current measurement Neural networks Land surface Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Plant Disease Management Through image processing Technology

Revolutionizing Plant Disease Management Through Image Proce...

引用

Computational Intelligence and Sustainable Engineering Solutions (CISES), international conference on

作者： Gaurav Kumar Ankit Kumar Manoj Singhal Kamred Udham Singh Lalan Kumar Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Information Technology GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

22.7 DL-VOPU: An Energy-Efficient Domain-Specific Deep-Learning-Based Visual Object processing Unit Supporting Multi-Scale Semantic Feature Extraction for Mobile Object Detection/Tracking applications

22.7 DL-VOPU: An Energy-Efficient Domain-Specific Deep-Learn...

引用

ieee international conference on Solid-State Circuits (ISSCC)

作者： Yuchuan Gong Teng Zhang Hongtao Guo Xiyuan Liu Jingxiao Zheng Hongqiang Wu Conghan Jia Luying Que Liang Zhou Liang Chang Jun Zhou University of Electronic Science and Technology of China Chengdu China

In the recent years, deep learning-based visual object detection/tracking (VODT) has been widely used in intelligent applications such as autonomous driving, UAV, smart robot and $\text{VR}/\text{AR}$ . As general Al hardware platforms, GPUs and general Al processors are often used for accelerating VODT. However, without a domain-specific architecture, it is difficult for these processors to achieve high energy efficiency, making them unsuitable for mobile VODT applications. Recently, some dedicated VODT processors have been proposed with improved energy efficiency [1]–[3]. As shown in $\text{Fig}. 22.7.1$ , these designs have several issues: 1) they only support a single task (either detection or tracking), 2) they lack full support for multi-scale semantic feature extraction $(\text{MSFE}){-}$ based state-of-the-art VODT frameworks [4], and 3) they do not sufficiently exploit domain-specific features for energy efficiency optimization. To address these issues, in this work, a deep learning-based visual object processor (named DL-VOPU) is proposed for mobile VODT applications. It exploits diverse domain-specific features to achieve record-high energy efficiency for VODT, while supporting MSFE-based VODT frameworks with a programmable backbone network. The DL-VOPU features: 1) an energy-efficient MSFE-aware Al architecture, 2) an object-oriented adaptive computing technique for energy-efficient object tracking, 3) a parallel frame-difference computing technique for energy-efficient neural network (NN) computation on video streams, and 4) a unified data compression & computing technique to address data redundancy in VODT processing.

关键词： Visualization Program processors Semantics Redundancy computer architecture Streaming media Feature extraction

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482页 << < 462 463 464 465 466 467 468 469 470 471 > >>

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：