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

22,562 篇 会议
3,220 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

25,807 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,149 篇 工学
- 3,093 篇 计算机科学与技术...
- 2,542 篇 电气工程
- 1,383 篇 信息与通信工程
- 686 篇 控制科学与工程
- 593 篇 软件工程
- 285 篇 仪器科学与技术
- 217 篇 环境科学与工程（可...
- 189 篇 机械工程
- 183 篇 生物医学工程（可授...
- 163 篇 交通运输工程
- 149 篇 测绘科学与技术
- 130 篇 生物工程
- 122 篇 石油与天然气工程
- 121 篇 电子科学与技术（可...
- 117 篇 材料科学与工程（可...
- 111 篇 网络空间安全
- 98 篇 土木工程
- 92 篇 动力工程及工程热...
- 66 篇 水利工程
943 篇 理学
- 289 篇 生物学
- 188 篇 物理学
- 155 篇 化学
- 139 篇 地球物理学
- 132 篇 数学
- 84 篇 系统科学
611 篇 管理学
- 524 篇 管理科学与工程(可...
- 69 篇 图书情报与档案管...
437 篇 医学
- 284 篇 临床医学
- 95 篇 基础医学(可授医学...
227 篇 农学
- 148 篇 作物学
53 篇 经济学
52 篇 法学
49 篇 文学
47 篇 教育学
17 篇 艺术学
3 篇 军事学
2 篇 哲学

主题

25,808 篇 machine learning...
8,337 篇 machine learning
4,247 篇 support vector m...
4,161 篇 prediction algor...
4,141 篇 accuracy
3,577 篇 predictive model...
3,462 篇 training
3,199 篇 classification a...
3,156 篇 feature extracti...
1,930 篇 data models
1,793 篇 computational mo...
1,774 篇 deep learning
1,639 篇 random forests
1,463 篇 neural networks
1,386 篇 clustering algor...
1,368 篇 real-time system...
1,271 篇 data mining
1,121 篇 optimization
995 篇 decision trees
971 篇 measurement

机构

120 篇 chitkara univers...
37 篇 lovely professio...
36 篇 chitkara centre ...
30 篇 computer science...
30 篇 centre of resear...
28 篇 department of co...
26 篇 school of comput...
21 篇 univ chinese aca...
21 篇 computer science...
21 篇 school of comput...
20 篇 computer science...
18 篇 school of comput...
17 篇 school of comput...
17 篇 department of co...
16 篇 brac university ...
16 篇 khulna universit...
15 篇 symbiosis instit...
15 篇 department of co...
13 篇 school of comput...
13 篇 computer science...

作者

25 篇 ashwini kodipall...
24 篇 mahendra kumar g...
15 篇 rakesh kumar
13 篇 wei liu
13 篇 atul kumar
12 篇 qiangfu zhao
12 篇 amit kumar
12 篇 surendran r
12 篇 satvik vats
11 篇 jun zhang
11 篇 li li
11 篇 liu yang
11 篇 wei li
10 篇 sudeep tanwar
10 篇 himanshu sharma
10 篇 amiya ranjan pan...
9 篇 lei wang
9 篇 tohari ahmad
9 篇 shanmugasundaram...
9 篇 rishabh sharma

语言

25,604 篇 英文
147 篇 其他
32 篇 中文
5 篇 法文
4 篇 土耳其文
2 篇 西班牙文
2 篇 荷兰文
1 篇 德文

检索条件"主题词=Machine Learning Algorithms"

共 25808 条记录，以下是461-470 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：

Perceptual learning Technology Based on Computer Vision and machine learning algorithms

Perceptual Learning Technology Based on Computer Vision and ...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling of Cryptocurrency Prices Using machine learning algorithms

Predictive Modeling of Cryptocurrency Prices Using Machine L...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Rishita Chakma Tikle Barua Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling.

关键词： Deep learning machine learning algorithms Accuracy Statistical analysis Computational modeling Linear regression Predictive models Prediction algorithms Long short term memory Investment

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of machine learning algorithms for Classifying Bearing Failures Using Time and Frequency Domain Features

Comparative Analysis of Machine Learning Algorithms for Clas...

引用

Intelligent Systems and machine learning Conference (ISML)

作者： Pallavi Meshram Shilpa Sondkar Department of Instrumentation Engineering Vishwakarma Institute of Technology Pune Maharashtra India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Amongst the most crucial components of rotating machinery, bearings failure rate is high and it can directly impact the efficiency of the complete piece of machinery. As a result, diagnosing bearing faults is crucial because it can help ensure running stability and upkeep, boosting production efficiency and economic gains. The objective of the study is to evaluate various machine learning algorithms to find out the best method for categorizing bearing failure into three types: (a) Normal bearing, (b) Inner race fault (c) Outer race fault. The raw vibration signal is processed to obtain the temporal and frequency domain information. The classification is obtained using the MFPT dataset of rolling bearings. To evaluate the classification accuracy receiver operating characteristics (ROC) is employed as a performance metric. The classification models are compared: K-nearest neighbor, Support Vector machine, Neural Network, and Ensemble. The Ensemble subspace KNN and Cubic SVM algorithms are proven to be the most effective among other algorithms. The cubic SVM has shown better accuracy with shorter training times. The dataset of normal bearing and the faults (inner race and outer race) were compared for the time domain and frequency domain analysis. According to the study, it is found that power spectrum-frequency domain parameters and time domain parameter kurtosis show major variations amongst the normal and the faulty bearings.

关键词： Support vector machines Training machine learning algorithms Accuracy Frequency-domain analysis Neural networks Rolling bearings Nearest neighbor methods Classification algorithms Time-domain analysis

来源：评论

学校读者我要写书评

暂无评论

Automated School Lunch Menu Planning with machine learning algorithms

Automated School Lunch Menu Planning with Machine Learning A...

引用

IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Hao-Yu Lin Jeen-Shing Wang Ya-Ting C. Yang Dept. of Electrical Engineering National Cheng Kung University Tainan Taiwan Institute of Education National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

This study introduces an automated menu planning system for schools that leverages machine learning to optimize and streamline menu creation, focusing on nutritional guidelines, cost-effectiveness, and variety. The system comprises two main algorithms: the menu combination algorithm, which employs content-based filtering to select diverse recipes by analyzing and quantifying recipe features like color, flavor, and ingredients; and the menu optimization algorithm, which adjusts ingredient quantities to adhere to budget and nutritional standards. To evaluate the system's effectiveness, satisfaction surveys were conducted among school lunch menu planners, using a seven-point Likert scale to measure feedback on menu variety and cost-effectiveness. Results showed that the system aligns well with nutritional and budgetary requirements, with minimal errors in nutritional content and ingredient costs, and substantially reduces menu planning time.

关键词： Surveys machine learning algorithms Costs Process control machine learning Planning Informatics Standards Optimization Guidelines

来源：评论

学校读者我要写书评

暂无评论

Studying Dengue spread using Climatic conditions and machine learning algorithms

Studying Dengue spread using Climatic conditions and Machine...

引用

Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Manas Bisht Gautam Tripathi Manisha Aeri Kumar Shantanu Akash Singh Shiv Ashish Dhondiyal Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To be University Dehradun India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

Dengue is one of the serious mosquito-borne disease, which causes flu-like symptoms like high fever, sever headache, pain in joint, muscles, eyes etc. It is suggested to control mosquito populations in an area to avoid the disease. Our research aims on identifying climatic conditions which influence mosquito population to grow rapidly in an area, and find a machine learning algorithm that best predicts dengue risk in an area based on these climatic conditions. The dataset used was sourced from ***, in which after data cleaning we implemented algorithms like K-Nearest Neighbor, Logistic regression, Gaussian Naive Bayes and Random Forest on the cleaned dataset. Each algorithm was evaluated and compared on the basis of accuracy and confusion matrix out of which K-Nearest neighbor gave best results with 99.00% accuracy while offering superior performance in confusion matrix respectively, also Temperature, Humidity and Wind displayed best relation with spread of dengue diseases among other climatic conditions.

关键词： machine learning algorithms Accuracy Computational modeling Nearest neighbor methods Predictive models Prediction algorithms Boosting Bayes methods Viruses (medical) Diseases

来源：评论

学校读者我要写书评

暂无评论

Exploration of the Application of Big Data and machine learning algorithms in Electric Shock Prevention Warning

Exploration of the Application of Big Data and Machine Learn...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Kan Zhang Yong Li Yichen Cui Ke Zheng Peiyi Zhang Qian Yang Marketing Service Center of State Grid Henan Electric Power Company Zhengzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In view of the high incidence of electric shock accidents and the imperfect early warning system, big data analysis and machine learning algorithms are applied to precisely predict potential electric shock risks and give early warnings, thereby reducing the occurrence of electric shock accidents. First, various environmental, equipment, and personnel behavior data related to electric shock are collected and integrated, including indicators such as temperature and humidity, current fluctuations, and equipment usage frequency. Then, data preprocessing technology is used to clean the noise data to ensure data quality. Next, the support vector machine (SVM) machine learning algorithm is used to build a prediction model and train it through historical electric shock cases. Finally, the electric shock risk is predicted by inputting the model into the real-time monitoring data, and automatic warning is realized by combining the warning threshold. Studies have shown that the electric shock prevention warning system based on big data and machine learning has significantly improved in accuracy and response speed. Compared with traditional methods, the model can respond within 30 seconds after the electric shock event occurs, and the recall rate is close to 92%. The application of big data and machine learning algorithms in electric shock prevention warning has high practical value and potential. It not only improves the accuracy and real-time nature of warning, but also provides data support for preventive measures and reduces the risk of electric shock accidents.

关键词： Support vector machines machine learning algorithms Accuracy Electric shock Prevention and mitigation Big Data Predictive models Real-time systems Personnel Accidents

来源：评论

学校读者我要写书评

暂无评论

Prediction of Land Use and Land Cover Using Google Earth Engine (GEE) and machine learning algorithms: A Review

Prediction of Land Use and Land Cover Using Google Earth Eng...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Promtucs Garnagee Jackson Paurav Goel Sartajvir Singh University Institute of Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Reliability is a critical requirement for classifiers that generate maps of land use and land cover (LULC), and these classifiers are highly sought after. Particularly, machine learning classifiers yield accurate classification outcomes even with complicated, high-dimensional data. Numerous parameters, including training sample size, quality, thematic correctness, classifier selection, research region size, and others, influence the classified maps’ accuracy. Understanding these aspects will help in attaining the highest possible classification accuracies for a particular requirement. Large-scale satellite picture and feature classification jobs become computationally demanding, giving rise to Big Data issues. Lately, cloud-based platforms that are free to use, like Google Earth Engine (GEE), have made environments for parallel processing available to handle picture categorization tasks. Both land use and land cover (LULC) changes are crucial for disciplines like monitoring natural disasters and assessing the effects of environmental change. Assessing the state and progress of relevant machine learning is the aim of this study to LULC by analyzing several publications published in the last ten years. Trend analysis of the quantity and yearly citations of published works, identification of top institutions, nations/regions, and publication sources, investigation of scientific partnerships between major institutions and nations/regions, and analysis of important research topics and their trends of development are the main areas of focus. Additionally, the paper clarifies how ML is used in LULC, offering insights that may lead future research paths to hasten advancements in this field. This review assesses various Geospatial machine learning algorithms. Among the algorithms are Naïve Bayes (NB), Random Forest (RF), Support Vector machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Gradient Boosting (GTB) as well as Extreme Gradien

关键词： Support vector machines Earth machine learning algorithms Accuracy Reviews Land surface Artificial neural networks Boosting Random forests Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

Modern Approaches to Stock Price Forecasting: Analysis and Application of machine learning algorithms

Modern Approaches to Stock Price Forecasting: Analysis and A...

引用

International Conference on Emerging elearning Technologies and Applications (ICETA)

作者： Dušan Čatloch Eva Chovancová Sylvia Mat'ašová Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798331527716

ISBN: (纸本)9798331527723

This paper discusses the development and application of linear regression (LR) to predict stock prices in the stock market. The main objective is to analyse the efficiency and accuracy of linear regression in relation to financial markets, and specifically to predict stock prices one day ahead. The paper begins with a detailed review of the related theoretical foundations and existing methods in the field of stock market forecasting. Next, it discusses the formulation of the stock price forecasting problem and the choice of linear regression as a tool to solve it. Particular emphasis is placed on understanding the properties of linear regression and its potential contribution to predictive models. The second part of the paper describes the implementation and testing of the LR algorithm, including the design and execution of experiments to evaluate its performance. The results are analysed using graphs and individual numerical predictions that were collected on a sample of multiple firm stocks. These results are described and discussed in detail, assessing their sensitivity to short-term volatility and deviation from fair value. The paper provides a new perspective on the use of linear regression in forecasting stock markets and contributes to a better understanding of its effectiveness and limitations.

关键词： Hands Accuracy machine learning algorithms Linear regression machine learning Companies Predictive models Prediction algorithms Stock markets Forecasting

来源：评论

学校读者我要写书评

暂无评论

SONAR Based Under Water Mine Detection Using machine learning algorithms

SONAR Based Under Water Mine Detection Using Machine Learnin...

引用

Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： B. Nikhil Krishna Reddy Rashmi M. R Chockalingam Aravind Vaithilingam S. Kamalakkannan Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India School of Engineering Faculty of Innovation & Technology Taylor's University Malaysia Department of Electrical and Electronics Engineering S. A. Engineering College Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In modern naval warfare, submarines play a vital role in strategic operations. However, submarines face a significant risk of encountering ocean mines, which are lethal and can cause severe damage. To mitigate this risk, submarines are equipped with Sound Navigation and Ranging systems (SONAR) which uses active/passive SONAR to detect objects in the surrounding water. However, distinguishing between the mines and other underwater objects, such as rocks, is a critical challenge. machine learning based object detection is proposed in this paper to distinguish between under water rock versus mines. techniques. By leveraging the capabilities of machine learning algorithms, submarines can make informed decisions in real-time, helping to ensure the safety of naval forces and successful mission outcomes. Real time dataset of SONAR recordings of mines and rocks is collected and considered for classification. Six machine learning algorithms are considered in this work. Support Vector machines (SVM), Decision Tree Classifier, Random Forest Classifier, KNN Classifier, XGboost and Ensemble methods are used to grade whether the detected object is rock or mine. Comparative results of all these algorithms are presented in this paper.

关键词： Support vector machines machine learning algorithms Sonar Rocks Sonar navigation Real-time systems Vectors

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Feature Selection Techniques in machine learning algorithms for Predicting Female School Attrition in Bangladesh

Comparative Study of Feature Selection Techniques in Machine...

引用

IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health

作者： Muminur Rahman Mohammad Nayeem Hasan Faizul Islam Sabrin Sultana Shomi Khan Department of Computer Hazrat Shahjalal (Rh.) Degree College Sylhet Bangladesh Department of Statistics Shahjalal University of Science and Technology Sylhet Bangladesh Department of Banking and Insurance University of Chittagong Chittagong Bangladesh Department of Electrical and Electronic Engineering Shahjalal University of Science and Technology Sylhet Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

In real-world scenarios, such as school attrition, it is important to consider relevant features when applying machine learning algorithms. However, not all features are essential, and adding irrelevant ones can diminish the effectiveness of the algorithm. Feature selection (FS) is a vital process in machine learning to minimize data dimensionality and increase algorithm efficiency. Evolutionary algorithms (EA) have attracted plenty of attention as a technique of solving FS challenges. Surprisingly, no previous research has applied these methods specifically to predict factors related to female school attrition. Thus, the purpose of this study is to use multiple feature selection strategies. Genetic Algorithm (GA), Chi-squared, Mutual Information (MI), Exhaustive Feature Selection (EFS) and evaluate their effectiveness by comparing results with and without the application of feature selection techniques. Data for this study is collected from the Multiple Indicator Cluster Surveys, 2019. Among the methods tested, artificial neural networks (ANN) demonstrated the highest accuracy in predicting school attrition. Using GA, Chi-squared, MI, and EFS techniques, the ANN model’s accuracy was 83.76%, 84.11%, 84.23%, and 84.88%, respectively. Using the same feature selection methods, Support Vector machine (SVM) produced results of 82.49%, 83.18%, 81.53%, and 82.65% whereas Logistic Regression (LR) produced accuracy numbers of 81.72%, 81.38%, 82.29%, and 82.31%. Without applying any feature selection techniques, ANN provided 82.38%, SVM provided 81.17% and LR provided 81.14% of accuracy. Therefore, the findings indicate that all models performed well in classifying students as in-school or out-of-school and we achieved higher accuracies after applying FS approaches than without FS. Thus, tailoring filter measures based on specific FS technique characteristics can significantly enhance efficiency and effectiveness.

关键词： Support vector machines Accuracy machine learning algorithms Artificial neural networks machine learning Evolutionary computation Feature extraction Prediction algorithms Mutual information Genetic algorithms

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 << < 43 44 45 46 47 48 49 50 51 52 > >>

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：