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

15,153 篇 会议
230 篇 期刊文献
227 册 图书
3 篇 学位论文

馆藏范围

15,613 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

8,090 篇 工学
- 5,416 篇 计算机科学与技术...
- 2,792 篇 软件工程
- 2,444 篇 电气工程
- 1,573 篇 信息与通信工程
- 1,005 篇 控制科学与工程
- 833 篇 光学工程
- 572 篇 电子科学与技术（可...
- 552 篇 生物医学工程（可授...
- 549 篇 机械工程
- 506 篇 仪器科学与技术
- 414 篇 生物工程
- 284 篇 测绘科学与技术
- 204 篇 航空宇航科学与技...
- 189 篇 交通运输工程
- 180 篇 材料科学与工程（可...
- 145 篇 安全科学与工程
- 119 篇 化学工程与技术
4,110 篇 理学
- 2,773 篇 物理学
- 1,130 篇 数学
- 467 篇 生物学
- 325 篇 系统科学
- 271 篇 统计学（可授理学、...
- 146 篇 化学
1,616 篇 医学
- 1,438 篇 临床医学
- 215 篇 特种医学
- 201 篇 基础医学(可授医学...
- 134 篇 药学(可授医学、理...
858 篇 管理学
- 515 篇 管理科学与工程(可...
- 378 篇 图书情报与档案管...
- 130 篇 工商管理
77 篇 法学
67 篇 农学
42 篇 经济学
40 篇 教育学
27 篇 文学
25 篇 军事学
20 篇 艺术学
1 篇 哲学

主题

2,700 篇 image processing
1,407 篇 image segmentati...
1,150 篇 signal processin...
802 篇 real-time system...
757 篇 feature extracti...
746 篇 cameras
557 篇 image edge detec...
534 篇 signal processin...
480 篇 image coding
475 篇 image reconstruc...
447 篇 algorithm design...
378 篇 deep learning
370 篇 object detection
342 篇 computer vision
340 篇 accuracy
338 篇 hardware
331 篇 clustering algor...
330 篇 real time system...
327 篇 image color anal...
317 篇 computer archite...

机构

23 篇 signal and image...
19 篇 samara national ...
17 篇 school of electr...
12 篇 information syst...
11 篇 samara national ...
11 篇 univ texas san a...
11 篇 department of el...
11 篇 school of electr...
10 篇 department of el...
10 篇 electrical compu...
10 篇 center for signa...
9 篇 school of electr...
9 篇 institute of ima...
9 篇 tongji universit...
9 篇 purdue univ sch ...
9 篇 university of ch...
8 篇 shanghai jiao to...
8 篇 information syst...
8 篇 university of to...
8 篇 image processing...

作者

22 篇 anon
13 篇 de-shuang huang
11 篇 jie yang
11 篇 p. pirsch
11 篇 jagdish chand ba...
10 篇 hannig frank
10 篇 panetta karen
10 篇 c.-c.j. kuo
10 篇 j.m. mendel
10 篇 plaza antonio
10 篇 r. chellappa
9 篇 semenishchev e.
9 篇 zelensky a.
9 篇 bovik alan c.
9 篇 wei li
8 篇 frank hannig
8 篇 yves wiaux
8 篇 a.s. willsky
8 篇 mahalanobis a
8 篇 satoshi goto

语言

14,975 篇 英文
538 篇 其他
90 篇 中文
26 篇 土耳其文
4 篇 俄文
3 篇 葡萄牙文
2 篇 法文
1 篇 西班牙文

检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15613 条记录，以下是711-720 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：

Fault Identification of Solar Panel Using image processing and IoT

Fault Identification of Solar Panel Using Image Processing a...

引用

2024 International conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Shanthosh, K. Sugunesh, R. vijayabharani, D. Saranbabu, B.C. Maniraj, P. M Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350309140

Solar photovoltaic systems have emerged as a prominent source of renewable energy. However, their performance can be hindered by various factors, including faults within the PV modules or the overall system. Rapid and accurate detection of these faults is crucial for maintaining optimal energy production and ensuring system safety. This project proposes a novel approach for Solar Fault Detection using Convolutional Neural Network algorithm. The proposed system leverages the power of deep learning techniques to automatically analyse images of solar panels and identify potential faults. A comprehensive dataset of annotated solar panel images is used for training the CNN model. To account for real-world scenarios, the dataset includes images captured under various lighting conditions and angles. The CNN architecture is designed to extract intricate features from the images, allowing for precise fault identification. The model is trained on a diverse set of fault types, including but not limited to, micro cracks, hot spots, shading effects, and soiling. Transfer learning techniques are also employed to improve model performance and reduce training time. To account for real-world scenarios, the dataset includes images taken under different lighting circumstances and angles. The results demonstrate high accuracy, sensitivity, and specificity in detecting faults. Additionally, the system exhibits robustness to environmental variables such as weather conditions and time of day. In this user-friendly interface is developed to facilitate easy integration of the solar panel. The interface provides real-time feedback on the status of solar panels, highlighting any detected faults and their severity levels. This project presents a state-of-the-art approach for solar fault Detection using CNN algorithms, offers a best maintenance for the solar panel damages and fault. The proposed system holds significant potential for the solar panel damages and fault. The proposed system holds

关键词： Convolutional Neural Network Deep Learning Fault Identification insert image processing Photovoltaic systems Renewable Energy Solar Fault Detection Transfer Learning

来源：评论

学校读者我要写书评

暂无评论

Effective in-the-Moment image Compression on FPGA: A Combinatorial Method Including DWT and IWT Techniques

Effective in-the-Moment Image Compression on FPGA: A Combina...

引用

International conference on Advanced Computing and Communication systems (ICACCS)

作者： U. Dinesh V. Deepak Rajan S. Navaneethan Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situations where resources are limited, the flexibility and adaptability offered by the FPGA architecture are ideal. Our technique strikes a compromise between compression effectiveness and image quality by utilizing DWT for multi-resolution analysis and IWT for spatial redundancy reduction. Real-time processing and resource optimization are ensured by the FPGA implementation. FPGA-optimized algorithms that tackle resource constraints are among the contributions. Evaluations demonstrate enhanced signal-to-noise ratios, compression ratios, and execution times. This study highlights how fast FPGA can compress images, especially for embedded systems and space missions. The study not only improves image compression but also highlights how FPGA can be used to increase the effectiveness of signal processing algorithms.

关键词： image coding Space missions Redundancy Signal processing algorithms Parallel processing Real-time systems Discrete wavelet transforms Field programmable gate arrays Optimization Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Pumpkin Leaf Disease Detection: Convenience of CNN Over Traditional Machine Learning in Terms of image Classification 3rd

Pumpkin Leaf Disease Detection: Convenience of CNN Over Trad...

引用

3rd International conference on Smart systems - Innovations in Computing (SSIC)

作者： Mithu, Mosaddek Ali Momo, Shampa Islam Hasan, Md Mehedi Rahman, Kazi Motiour Sattar, Abdus Daffodil Int Univ 102 Mirpur Rd Dhanmondi Dhaka 1207 Bangladesh

ISBN: (纸本)9789811628771;9789811628764

Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers frequently miss the best time for stopping and treating diseases. Further, early identification and classification of pumpkin leaf diseases extremely needed. This paper proposes to discover the pumpkin leaf diseases by utilizing a modern image processing procedure convolutional neural network (CNN). CNN applied for image classification and recognition because of its high accuracy. Besides, a comparison of traditional machine learning algorithms like support vector machines (SVM), K-nearest neighbor (KNN), decision tree, and Naive Bayes with the performance of CNN is demonstrated in our work. Tensorflow library was adopted to implement the CNN algorithm and Scikit-learn used in terms of utilizing the above-mentioned traditional machine learning algorithms. Finally, we detected the pumpkin leaf diseases by the algorithm that exhibits an assuring accuracy to our suggested approach.

关键词： Classification Convolutional neural network (CNN) Data analysis Detection image processing Leaf disease Machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Model for Brain Tumor Detection Accuracy Using Inceptionresnetv2 compared to VGG19 and MobileNet Models

Enhanced Model for Brain Tumor Detection Accuracy Using Ince...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Tamilselvi, M. Ashwini, R. Ravi, S. Rajalakshmi, S. Rajkumar, Chadge Fawareh, Hamed Saveetha University Department of Ece Chennai India R.M.K College of Engineering and Technology Department of Ece Chennai India Saveetha University Department of Vlsi Microelectronics Chennai India S.A. Engineering College Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Brain tumors, arising from unregulated and accelerated cellular proliferation, provide considerable health hazards if not promptly addressed. Notwithstanding significant progress, precise segmentation and classification continue to pose difficulties. Artificial intelligence (AI) and medical imaging technologies have advanced recently, greatly improving illness analysis and prediction, particularly with regard to brain tumor (BT) identification. Precise classification and segmentation of tumors in the brain are essential for creating individualized treatment plans, and early detection of brain tumors is essential for improving patient prognosis and available treatments. Even with the increasing usage of Magnetic Resonance Imaging (MRI) for brain imaging and the development of AI-based detection techniques, developing a reliable and effective model to identify and classify cancers from MRI images is still a difficult task. Brain tumor analysis is essential for timely diagnosis and effective patient management. Tumor morphology, encompassing features such as dimension, position, appearance, and heteromorphic shape in medical images, poses challenges for analysis. Advancements in DL approaches for the automatic identification of tumors present in the brain using MRI data have led to the utilization of numerous algorithms for this purpose. The InceptionResNetV2 model is employed due to its efficiency and efficacy;it is a simple deep learning architecture, rendering it a suitable candidate for medical image processing applications. The proposed model attains an accuracy rate of 100% at an epoch rate of 30 and surpasses the performance of both MobileNet and VGG19 models. © 2024 IEEE.

关键词： Medical image processing

来源：评论

学校读者我要写书评

暂无评论

IMDL-BenCo: A Comprehensive Benchmark and Codebase for image Manipulation Detection & Localization 38

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ma, Xiaochen Zhu, Xuekang Su, Lei Du, Bo Jiang, Zhuohang Tong, Bingkui Lei, Zeyu Yang, Xinyu Pun, Chi-Man Lv, Jiancheng Zhou, Jizhe College of Computer Science Sichuan University China Department of Computer and Information Science University of Macao China Engineering Research Center of Machine Learning and Industry Intelligence MOE China

A comprehensive benchmark is yet to be established in the image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark;and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://***/scu-zjz/IMDLBenCo. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Battlefield information and tactics engine (BITE): a multimodal, large language model approach for battlespace management 6

Battlefield information and tactics engine (BITE): a multimo...

引用

conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications vi

作者： Connolly, Brian J. Southwest Res Inst 6220 Culebra Rd San Antonio TX 78238 USA

ISBN: (纸本)9781510674219;9781510674202

Battlespace management requires rapid processing of large amounts of data to facilitate informed decision-making. Large Language Models (LLMs) have demonstrated near or above human performance on a wide range of cognitive tasks. Current LLMs are unreliable, have poor explainability, and are prone to bias and hallucinations. As such they are unsuitable for many defense applications, but their abilities can be studied with future, improved LLMs in mind. Specifically, LLM capabilities to synthesize defense-relevant data and make decisions in a combat environment have been largely unexplored. The battlefield information and tactics engine (BITE) uses LLMs as observers and decision-makers in a military environment. A multiplayer video game focusing on modern mechanized combat, Squad by Offworld Industries Ltd., is used as an operating environment due to its moderate realism levels and focus on audio communication between players. BITE is tasked with ingesting tactical data, providing summaries of the current situation, and giving order to a squad of human players. The present work aims to qualitatively assess the suitability of BITE, and LLMs in general, for use in battlespace management systems. Shortcomings are identified in the areas of spatial awareness, decision-making time, and reliability. However, BITE exhibits instances of competent leadership and demonstrates a generalized understanding of modern mechanized combat. While current LLMs are currently deeply unsuitable for combat environments, BITE and similar approaches show promise in wargaming and training applications.

关键词： image segmentation Object detection Optical character recognition Decision making

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Modern Generative Models for Echocardiography image Synthesis

A Comprehensive Analysis of Modern Generative Models for Ech...

引用

2024 International conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Rakhmetulayeva, Sabina Bolshibayeva, Aigerim Department of Cybersecurity Information Processing and Storage Satbayev University Almaty Kazakhstan Department of Information systems International IT University Almaty Kazakhstan

ISBN: (纸本)9798350353488

The use of machine learning (ML) in the medical field is hindered by the scarcity of high-quality data. This work tackles the deficiency of echocardiogram pictures (echoCG) by using advanced generative models for synthetic creation. We provide a comprehensive practical assessment of these approaches and ascertain their limits. Our study examines the efficacy of a cycle-consistent generative adversarial network (CycleGAN), a contrastive unpaired translation (CUT) approach, and a latent diffusion model (Stable Diffusion 1.5). We outline our data generating approach, exhibit picture examples, describe our assessment strategy, and offer findings from a user study measuring the perceived quality and medical validity of the produced samples, done with licensed cardiologists and surgeons. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

CHARACTERIZATION OF TUNABLE REBOUND PROPERTIES OF MICROSTRUCTURED MAGNETOACTIVE ELASTOMERS 17

CHARACTERIZATION OF TUNABLE REBOUND PROPERTIES OF MICROSTRUC...

引用

ASME conference on Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Kriegl, Raphael Jezersek, Matija Kravanja, Gaia Hribar, Luka Kokot, Gasper Drevensek-Olenik, Irena Shamonin, Mikhail Ostbayerische Tech Hsch Regensburg Regensburg Germany Univ Ljubljana Fac Mech Engn Ljubljana Slovenia J Stefan Inst Ljubljana Slovenia Univ Ljubljana Fac Math & Phys Ljubljana Slovenia

ISBN: (纸本)9780791888322

We present a novel method to control the rebounding behavior of small mm-sized solid balls by employing magnetoactive elastomers (MAEs) with microstructured surfaces. An MAE is a composite material consisting of mu m-sized ferromagnetic particles dispersed in a soft elastomer (e.g., polydimethylsiloxane) matrix. In the act of rebounding, the ball hits an MAE surface and bounces back. The MAE samples contained 75 wt.% of iron. This composite material is known to respond to an applied magnetic field with increased stiffness (due to the magnetorheological effect) and plasticity. To adjust the rebound properties, the top layer of the MAE material was additionally modified by micromachining lamellar structures with different dimensions on the 100 mu m scale via laser ablation. Due to the resulting high aspect ratio, these surface structures were sensitive to the magnetic field direction. The lamellas could stand up straight or lay down flat. The rebound behavior was evaluated by using a custom build apparatus that facilitates dropping of the balls in a precise and repeatable manner. A ball was dropped from different heights. The ball trajectory was captured with a high-speed camera to investigate the rebound properties. The recorded video was processed using a custom software written in Python. The experimental procedure and data processing algorithms are presented in detail. The results for the samples with different geometrical dimensions are provided as examples. It is made evident that the magnetic field influences the rebound properties of small non-magnetic balls impinging microstructured MAE surfaces. The change in surface topography is an effective way to control the ball rebound. The fabrication flexibility in geometrical dimensions of surface microstructures opens a convenient way to tune the desired response to magnetic fields. The presented idea may find applications in impact mitigation or small-scale sorting machinery, e.g. for recycling.

关键词： magnetoactive elastomer image processing rebound mm-sized ball lamellar array magnetic field microstructured surface

来源：评论

学校读者我要写书评

暂无评论

Automatic regions detection in CT images based on Haralick textures 17

Automatic regions detection in CT images based on Haralick t...

引用

17th Iberian conference on Information systems and Technologies (CISTI)

作者： Caridade, Cristina M. R. Almeida, Duarte Rodrigues, Simao ISEC Polytech Coimbra Coimbra Portugal ISEC Appl Biomech Lab CICGE Res Ctr Geospatial Sci Coimbra Portugal

ISBN: (纸本)9789893334362

The identification of anomalies (such as bone fractures or tendonitis in muscles and soft tissues) through image processing and analysis techniques in Computed Tomography (CT) images is today of great importance to assist doctors and health professionals in making accurate diagnoses. The extraction of relevant information from the CT image is characterized by the calculation of gray level input image attributes. Statistical moments (SM) are calculated using the gray level distribution of an image and are therefore generally calculated from that image's histogram. These characteristics provide a statistical description of the relationship between different gray levels in the CT image. Haralick proposed a methodology for describing textures based on second order statistics, where characteristics are derived from co-occurrence matrices, which are constructed by counting different combinations of gray levels in an image according to certain directions. In this work, it is intended to automatically identify and extract regions in CT images based on textures as an aid for a quick and accurate diagnosis. CT images are first pre-processed for noise reduction and image enhancement, followed by the application of Haralick textures to segment and detect zones of interest. Classifiers trained on the Haralick invariant features showed good accuracy and performance. Despite the presence of low contrast and noise in some images, the proposed algorithms present promising results in the segmentation and automatic identification of regions of tomographic images, being an important contribution to support health professionals in the characterization of anomalies and their extension. Good results are expected for the next step of this work in the detection and segmentation of anomalies in CT images.

关键词： Digital image processing image segmentation Haralick textures

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Medical image Analysis: A Survey 1

引用

International conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Fettah, Amina Menassel, Rafik Gattal, Abdeljalil Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9783031352485

ISBN: (纸本)9783031352478

Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a variety of diseases, including Brain Tumors, Breast Cancer, diagnosis of diabetic retinopathy, as well as, more recently in dealing with the COviD-19 pandemic and its associated medical challenges. To that end, medical image processing employs certain strategies and techniques for manipulating images, which include image acquisition, storage, presentation, and transmission. In this paper, we have overviewed several machine learning and deep learning approaches that are commonly utilized for various image processing tasks, such as image segmentation, image detection, image classification, and image compression on different medical imaging modalities, among which are computed tomography (CT), magnetic resonance imaging (MRI), and x-ray radiography. Focusing on supervised learning and deep learning techniques when applied to medical imaging analysis to enhance the performance in different medical applications using a number of algorithms such as CNNs that lead to efficient outcomes in automatic feature extraction and acceleration of the training process in the detection and classification of different diseases. The methods collected in this survey paper and the results are discussed, and the main conclusions are highlighted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Medical imaging

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 << < 68 69 70 71 72 73 74 75 76 77 > >>

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：