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

6,378 篇 会议
161 册 图书
46 篇 期刊文献

馆藏范围

6,585 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,749 篇 工学
- 4,176 篇 计算机科学与技术...
- 3,369 篇 软件工程
- 1,157 篇 信息与通信工程
- 762 篇 生物工程
- 563 篇 生物医学工程（可授...
- 497 篇 控制科学与工程
- 420 篇 电气工程
- 326 篇 动力工程及工程热...
- 267 篇 机械工程
- 254 篇 光学工程
- 251 篇 电子科学与技术（可...
- 240 篇 安全科学与工程
- 219 篇 化学工程与技术
- 214 篇 环境科学与工程（可...
2,076 篇 理学
- 942 篇 数学
- 830 篇 生物学
- 413 篇 物理学
- 399 篇 统计学（可授理学、...
- 278 篇 系统科学
- 222 篇 化学
1,415 篇 管理学
- 784 篇 管理科学与工程(可...
- 774 篇 图书情报与档案管...
- 514 篇 工商管理
615 篇 医学
- 580 篇 临床医学
- 555 篇 基础医学(可授医学...
- 331 篇 药学(可授医学、理...
- 302 篇 公共卫生与预防医...
359 篇 法学
- 266 篇 社会学
208 篇 农学
- 199 篇 作物学
197 篇 经济学
- 196 篇 应用经济学
132 篇 教育学
23 篇 军事学
13 篇 文学

主题

278 篇 internet of thin...
262 篇 machine learning
253 篇 cloud computing
230 篇 big data
219 篇 computational mo...
207 篇 deep learning
182 篇 data mining
157 篇 data models
137 篇 predictive model...
135 篇 artificial intel...
133 篇 training
130 篇 feature extracti...
122 篇 real-time system...
119 篇 blockchain
108 篇 support vector m...
107 篇 accuracy
107 篇 machine learning...
102 篇 convolutional ne...
100 篇 forecasting
96 篇 medical services

机构

54 篇 graphic era deem...
44 篇 chitkara univers...
24 篇 department of co...
24 篇 department of co...
21 篇 department of en...
19 篇 school of comput...
19 篇 teerthanker maha...
18 篇 school of inform...
17 篇 jaipur national ...
16 篇 university of ch...
16 篇 uttaranchal inst...
15 篇 fujian provincia...
14 篇 srm institute of...
14 篇 department of co...
14 篇 uttaranchal inst...
13 篇 computer science...
13 篇 school of comput...
11 篇 uttaranchal inst...
10 篇 college of compu...
10 篇 school of manage...

作者

21 篇 pant bhasker
20 篇 chang kuo-chi
13 篇 tripathi vikas
12 篇 kukreja vinay
12 篇 vinay kukreja
11 篇 jacob lija
11 篇 joshi kapil
11 篇 gangodkar durgap...
10 篇 iwendi celestine
10 篇 lourens melanie
10 篇 chu kai-chun
10 篇 sujithra m.
10 篇 alanya-beltran j...
9 篇 rajeswari d.
9 篇 chang fu-hsiang
9 篇 singh rajesh
8 篇 kapil joshi
8 篇 kapila dhiraj
8 篇 pavithra g.
8 篇 sanjay misra

语言

6,395 篇 英文
184 篇 其他
15 篇 中文

检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6585 条记录，以下是61-70 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：

Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven Analytics 5

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Nimma, Divya Anudeep, Ch. Sahu, Tripti Shah, Nusrat Hamid Shah, Lubna Hamid Bala, B. Kiran University of Southern Mississippi Ummc United States Department of Management studies Bhimavaram India Mima Institute of Management Maharashtra Pune India College of Engineering and Computer Science Jazan University Saudi Arabia College of Business Jazan University Department of Management and Marketing Saudi Arabia K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a Multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world. © 2025 IEEE.

关键词： Consumer behavior

来源：评论

学校读者我要写书评

暂无评论

Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

引用

8th IFIP TC 12 international conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP international Federation for Information Processing 2025.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Learning Techniques 5

Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Lea...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Shams, Karib Saha, Ratul Rodela, Mahmuda Islam Simran, Sanjana Rashid Khan, Musharrat East West University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798350356236

Tuberculosis (TB) is a significant global health challenge, necessitating precise and efficient diagnostic tools. This work investigates the effectiveness of various deep learning models such as EfficientNetB3, DenseNet121, VGG19, DenseNet201, and ResNet50 in detecting TB from chest X-ray images. Utilizing the TB Chest X-ray database, we evaluated pre-trained models, developed custom architectures, and employed the k-fold validation method. The EfficientNetB3, with a customized design, demonstrated superior performance, while the DenseNet201 excelled in the k-fold validation setup. Our findings highlight the potential of the EfficientNetB3 and DenseNet201 in advancing TB diagnostics, offering significant improvements in speed and accuracy. We got 100% accuracy in EfficientnetB3. This study not only contributes to the growing field of AI in medical imaging but also underscores the ethical considerations required in deploying AI for healthcare, ensuring patient privacy and equitable algorithmic decisions. The outcomes of this study hold promise for improving real-time healthcare through more effective TB detection and patient care. © 2025 IEEE.

关键词： Disease control

来源：评论

学校读者我要写书评

暂无评论

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge computing and Real-Time Decision-Making in Uncertain IoT Environments 1

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

引用

1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Joshi, B.P. Singh, Akhilesh Kumar, Nagendra Rautela, Siddharth Graphic Era Hill University Department of Mathematics Department of Computer Science & Engineering India VMSB UTU Department of Electrical Engineering Campus Pithoragarh India GL Bajaj Institute of Technology & Management Department of Electrical & Electronics Engineering Greater Noida201306 India Central Board of Secondary Education Department of Mathematics Uttarakhand Bhimtal India

ISBN: (纸本)9798331518578

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff. © 2025 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

The Research on V2V Cooperative Caching Method Based on Vehicle Attributes 24

The Research on V2V Cooperative Caching Method Based on Vehi...

引用

2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Wang, Zirui Wang, Hui Wan, Jianxiong Liu, Chuyi Sun, Pengfei Li, Leixiao School of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Autonomous Region Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798400709869

To address the increasingly severe issue of insufficient vehicular caching in the Internet of Vehicles (IoV), Vehicular edge computing uses base stations (BS) and vehicles with computing and storage capabilities as edge servers to cache content. However, most existing studies focus on reducing task costs, assuming vehicles can simultaneously act as requesters and providers, without considering whether a vehicle is suitable to serve as a provider, willing to share resources or ensure secure data transmission, thus lowering matching efficiency. Deep reinforcement learning and blockchain technology were integrated into the Internet of Vehicles to address distributed vehicle-to-vehicle collaborative caching. An optimal collaborative caching strategy was designed, considering dynamic vehicle attributes, with the system's objective being to maximize vehicle utility to encourage resource sharing. Simulation results confirm that the proposed blockchain-based vehicular edge computing collaborative caching scheme with deep reinforcement learning significantly outperforms other strategies. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Deep Leakage Prevention Model with Mixing Multi-gradients 2nd

A Lightweight Deep Leakage Prevention Model with Mixing Mul...

引用

2nd international conference on Security and Information technologies with AI, Internet computing and Big-data Applications, SITAIBA 2023

作者： Hsiao, Wei-Chun Wang, Chih-Hung Department of Computer Science and Information Engineering National Chiayi University Chiayi Taiwan

ISBN: (纸本)9789819777853

In deep learning model, the training procedure can obtain appropriate weights by leveraging the information of gradient descent. However, the gradients between the weights may leak the original neural network training data if they are not well-designed. Zhu et al. (advances in neural information processing systems, Curran Associates, Inc., [5]) as proposed a kind of attacking scenario named deep leakage from gradients. There were many mitigation strategies proposed in the literature, such as mixing Gaussian noises and model quantization. However, they usually somewhat lost classification accuracy during prevention process. This paper proposes a lightweight attachment model which can be used as an extension on the original classification model to prevent deep leakage from gradients with low information loss. This approach can complete the goal of mitigation on trying to minimize the changes of the original structure of the model and can be easily implemented on some existent deep learning classifiers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Wearable Assistive Device for Enhanced Navigation in Visually Impaired Individuals Using YOLO-Based Object Detection 5

Wearable Assistive Device for Enhanced Navigation in Visuall...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Rao, Abhishek S. Poojary, Ramaprasad Karthik Pai, B.H. Rohan Arya, A.R. Utkarsh Kumar, Harsh Darshan, S. Shenoy, H. Nagesh Nmam Institute of Technology Department of Information Science and Engineering India School of Engineering and It Manipal Academy of Higher Education Dubai Campus PO Box no 345050 Dubai United Arab Emirates Canara Engineering College Department of Computer Science and Engineering Benjanapadavu India

ISBN: (纸本)9798350356236

The proposed study addresses the pressing challenge of enabling visually impaired individuals to navigate their surroundings with enhanced safety and autonomy. To this end, we propose a wearable assistive device designed to improve spatial awareness and facilitate environmental interaction. The primary objectives include developing an accurate object detection system, integrating real-time distance measurement capabilities, and incorporating situational awareness through weather data integration. The study methodology employs cutting-edge computer vision and machine learning techniques, with a YOLO-based object detection model at its core. This model ensures robust identification of objects, achieving an impressive 97% precision and recall. To support safe navigation, real-time distance measurement functionality was implemented. Additionally, the device incorporates real-time weather data via the Open Weather API, delivering comprehensive updates on temperature, precipitation, and wind conditions to assist in user decision-making. Design considerations focused on user-centric aspects such as battery life, portability, and cost-effectiveness to ensure practicality and accessibility. Evaluation results demonstrated an average mAP of 98.34% at IoU 0.5 across all object classes, with some reaching up to 99.50%. User testing validated the system's efficacy, achieving a 95% success rate in object identification and spatial guidance. These findings underscore the device's potential to enhance independence, safety, and quality of life for visually impaired users, promoting inclusivity through advanced assistive technology. © 2025 IEEE.

关键词： Distance measurement

来源：评论

学校读者我要写书评

暂无评论

WeedsGalore: A Multispectral and Multitemporal UAV-Based dataset for Crop and Weed Segmentation in Agricultural Maize Fields

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dat...

引用

2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Celikkan, Ekin Kunzmann, Timo Yeskaliyev, Yertay Itzerott, Sibylle Klein, Nadja Herold, Martin Gfz German Research Centre for Geosciences Germany Humboldt-Universität zu Berlin Germany Scientific Computing Center Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798331510831

Weeds are one of the major reasons for crop yield loss but current weeding practices fail to manage weeds in an efficient and targeted manner. Effective weed management is especially important for crops with high worldwide production such as maize, to maximize crop yield for meeting increasing global demands. advances in near-sensing and computer vision enable the development of new tools for weed management. Specifically, state-of-the-art segmentation models, coupled with novel sensing technologies, can facilitate timely and accurate weeding and monitoring systems. However, learning-based approaches require annotated data and show a lack of generalization to aerial imaging for different crops. We present a novel dataset for semantic and instance segmentation of crops and weeds in agricultural maize fields. The multispectral UAV-based dataset contains images with RGB, red-edge, and nearinfrared bands, a large number of plant instances, dense annotations for maize and four weed classes, and is multitemporal. We provide extensive baseline results for both tasks, including probabilistic methods to quantify prediction uncertainty, improve model calibration, and demonstrate the approach's applicability to out-of-distribution data. The results show the effectiveness of the two additional bands compared to RGB only, and better performance in our target domain than models trained on existing datasets. We hope our dataset advances research on methods and operational systems for fine-grained weed identification, enhancing the robustness and applicability of UAVbased weed management. The dataset and code are available at https://***/GFZ/weedsgalore. © 2025 IEEE.

关键词： Weed control

来源：评论

学校读者我要写书评

暂无评论

AI-Enabled Muscle Oxygen Sensors for Personalized Exercise Intensity Monitoring and Recovery Analysis 5

AI-Enabled Muscle Oxygen Sensors for Personalized Exercise I...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Balachander, K. Balasubadra, K. Tidke, Bharat Sree Southry, S. Meenakshi, B. Murugan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Kavaraipettai Tamil Nadu Chennai India University Nagpur Campus Pune India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Through the integration of artificial intelligence (AI) with muscle oxygen sensors, this work presents a unique way to exercise intensity monitoring and recovery analysis. Subjective assessments or heavy equipment are the backbone of the traditional approaches to evaluating exercise intensity and recovery. Using the latest developments in AI and wearable sensor technology, our system offers real-time personalized feedback to athletes and fitness enthusiasts. Our Muscle oxygen sensors were vigorously tested to ensure their precision and performance. Wearing the sensors, participants varied their workout regimen while the sensors sent data on the participants' muscle oxygen levels to an AI program. According to the results, the algorithm could correctly determine workout intensity levels and establish individual recuperation timeframes. Moreover, the AI system learns the user's physiological traits and can personalize recommendations and training programs for everyone. Our approach may help improve athletic performance, avoid overtraining injuries, and maximize fitness results by giving practical advice on recovering from exercises at the right intensity. © 2025 IEEE.

关键词： Sensory perception

来源：评论

学校读者我要写书评

暂无评论

Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation 25th

Robotic Precision Fitness: Accurate Pose Training for Elderl...

引用

25th international conference on Intelligent data Engineering and Automated Learning

作者： Rincon, J. A. Marco-Detchart, C. Univ Burgos Dept Digitalizac Escuela Politen Super Ctra Oron 28 Burgos 09200 Spain Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

This study presents a novel robotic assistant tailored for elderly rehabilitation. The system combines state-of-the-art artificial intelligence, computer vision, and robotics to provide customized exercise routines and instant feedback. The core of the assistant is a Grove AI V2 vision system, driven by a dual-core Arm Cortex-M55 processor and an Arm Ethos-U55 neural network component. A custom-built robotic torso fitted with 12 servo motors supports the demonstration of a wide variety of upper body exercises. A crucial element in the experiments is the use of Restricted Radial Equivalence Functions (RREFs) and Radial Similarity Measures (RSMs) to precisely match human poses with the robot's demonstrations. This method ensures an accurate evaluation of exercise performance, enhancing the efficacy of physical therapy for the elderly. Using edge computing technologies, the system provides real-time monitoring and personalised care while addressing important privacy issues. The assistant is additionally characterized by a small-sized design that includes a 124x64 pixel OLED display, capable of displaying bio-signal monitoring and an animated face to engage users. This research advances AI-assisted elderly care, aiming to increase independence, physical health, and overall quality of life for aging populations while offering support to healthcare systems and caregivers.

关键词： Edge computing Elderly Rehabilitation Pose Estimation Robotic Assistance

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500页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：