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

1,195 篇 会议
37 篇 期刊文献

馆藏范围

1,232 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

642 篇 工学
- 551 篇 计算机科学与技术...
- 455 篇 软件工程
- 225 篇 信息与通信工程
- 125 篇 控制科学与工程
- 96 篇 电气工程
- 94 篇 生物工程
- 91 篇 生物医学工程（可授...
- 72 篇 电子科学与技术（可...
- 69 篇 网络空间安全
- 53 篇 光学工程
- 46 篇 安全科学与工程
- 34 篇 仪器科学与技术
- 32 篇 机械工程
- 30 篇 动力工程及工程热...
- 29 篇 化学工程与技术
- 27 篇 交通运输工程
368 篇 理学
- 226 篇 数学
- 114 篇 统计学（可授理学、...
- 102 篇 生物学
- 54 篇 物理学
- 33 篇 化学
- 29 篇 系统科学
241 篇 管理学
- 166 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 55 篇 工商管理
85 篇 医学
- 80 篇 基础医学(可授医学...
- 79 篇 临床医学
- 43 篇 公共卫生与预防医...
- 43 篇 药学(可授医学、理...
39 篇 法学
- 36 篇 社会学
25 篇 经济学
21 篇 农学
7 篇 教育学
3 篇 军事学

主题

165 篇 feature extracti...
157 篇 accuracy
108 篇 convolutional ne...
80 篇 deep learning
54 篇 optimization
51 篇 support vector m...
50 篇 predictive model...
49 篇 internet of thin...
42 篇 computational mo...
35 篇 training
34 篇 long short term ...
33 篇 real-time system...
33 篇 machine learning
33 篇 data models
31 篇 classification a...
25 篇 security
25 篇 prediction algor...
25 篇 measurement
24 篇 visualization
24 篇 network security

机构

194 篇 department of co...
135 篇 department of el...
117 篇 the islamic univ...
98 篇 nitte meenakshi ...
87 篇 school of comput...
82 篇 the islamic univ...
58 篇 sr university sc...
53 篇 college of techn...
44 篇 school of comput...
37 篇 department of co...
36 篇 department of ec...
33 篇 nitte meenakshi ...
33 篇 nitte meenakshi ...
31 篇 nitte meenakshi ...
28 篇 department of ar...
28 篇 school of comput...
26 篇 east west instit...
26 篇 department of co...
26 篇 sr university sc...
25 篇 department of co...

作者

74 篇 piyush kumar par...
73 篇 pareek piyush ku...
24 篇 ramachandra a.c.
23 篇 muntather almusa...
23 篇 almusawi muntath...
21 篇 ramachandra a c
21 篇 parameshachari b...
20 篇 myasar mundher a...
20 篇 srinivas aluvala
20 篇 aluvala srinivas
19 篇 al-jawahry hassa...
18 篇 alabdeli haider
18 篇 hassan m. al-jaw...
18 篇 adnan myasar mun...
18 篇 haider alabdeli
16 篇 mohammed al-faro...
16 篇 nalini n.
15 篇 sundaram s. meen...
15 篇 alzubaidi laith ...
15 篇 mohammed i. habe...

语言

1,055 篇 英文
177 篇 其他
2 篇 中文

检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1232 条记录，以下是271-280 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：

Detection of Military Vehicles based on Edge Devices from Unmanned Aerial Vehicle 2

Detection of Military Vehicles based on Edge Devices from Un...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Umasankar, L. Anitha, D. Trisheela, S. Al-Jawahry, Hassan M. Borra, Subba Reddy S.A.Engineering College Electrical and Electronics Engineering Tiruvallur Poonamallee India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India Nitte Meenakshi Institute of Technology Dept of Cse Bengaluru India The Islamic University Najaf Iraq Computer Science and Engineering Maisammaguda Hyderabad India

ISBN: (纸本)9798350382952

The recent technology in surveillance for military vehicle parts forms the basis of intelligence warfare through information realized through monitoring and tracking operations in complex situations. Therefore, the process of identifying and classifying vehicles by an aerial vehicle installed with a resource-limited device and an intelligent object detection algorithm significantly assists security agencies. It is possible to make use of this vehicle either manually or autonomously. To overcome these issues, edge device based military vehicle detection from Unmanned Aerial Vehicle (UAV) is proposed. At present, no publicly available dataset exists that includes different military classes. Therefore, the proposed approach use a dataset consisting of 6772 images labeled as Tanks, Military Trucks, Helicopters, Aircraft, Civilian Cars and Civilian Aircraft. This data set is used to train deep learning models such as Quantized SSD Mobilenetv2 and Tiny Yolo v5 that are then compared with resource limited edge devices. The results reveal that Tiny Yolo v5 outperforms other models, showing high efficiency and suitability for edge-based devices due to its lightweight design. © 2024 IEEE.

关键词： Military vehicles

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Federated Learning-Convolutional Neural Network for Preserving Data Privacy and Security

Blockchain-Based Federated Learning-Convolutional Neural Net...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Manoj Kumar, D.P. Ananda Babu, J. Selva Lakshmi, C.B. Fatima, Tabeen Rajesh, N. Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Velammal College of Engineering and Technology Department of Cse India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The Internet of Things (IoT) and Industrial cloud computing have completely transferred the healthcare sector due to the rapid rise of distributed healthcare data. The security and privacy of healthcare data are critical issues facing in the healthcare sector. In this research, a BlockChain-based Federated Learning-Convolutional Neural Network (BC-FL-CNN) is proposed for preserving electronic health data privacy by integrating the BC and Deep Learning (DL) approach. CNN is used to classify normal and abnormal users in the processed dataset. The abnormal users were then processed and removed from the database as well as access to the health data utilizing BC with FL approach. Existing methods such as Convolution Neural Network-BlockChain-Cryptography-Federated Learning (CNN-BC-Cryp-FL), FL, and FL Risk-based Authorization Middleware for Healthcare (FRAMH) are used to compare with the proposed BC-FL-CNN approach. The proposed BC-FL-CNN achieves better accuracy of 98.25%, precision of 97.23%, recall of 97.56%, and 97.16% f1-score when compared to the existing approaches like CNN-BC-Cryp-FL, FL, and FRAMH respectively. © 2023 IEEE.

关键词： BlockChain Federated Learning Healthcare data Internet of Things Privacy Security

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Task Scheduling based on Whale Optimization Algorithm Cyber-Physical Systems 2

Energy-Efficient Task Scheduling based on Whale Optimization...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Shankar, Smitha Sharath Chythanya, Kanegonda Ravi Maharajan, K. Swathi, Akuthota The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Krishnankoil India Cvr College of Engineering Department of CSE Telangana India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units and physical plants. Task scheduling (TS) is critical to improving resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time techniques fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task scheduling technique called Whale Optimization Algorithm based Energy-Efficient TS (WOA-EETS) that is specifically designed for the CPS context. WOA-EETSS involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate WOA-EETSS' improved performance. The comparison analysis demonstrated that the WOA-EETSS system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms, Convergence Rate of 511ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 IEEE.

关键词： Cyber-Physical Systems Energy Efficient Task Diversity Task Scheduling Whale Optimization Algorithm

来源：评论

学校读者我要写书评

暂无评论

A Denoising Convolutional Auto Encoder with Bi-LSTM for Corrupted ECG Signals Reduction 2

A Denoising Convolutional Auto Encoder with Bi-LSTM for Corr...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Alsalami, Zaid Satish, E.G. Ponnibala, M. Rajalakshmi, J. Sushama, C. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Velalar College of Engineering and Technology Erode India School of Computing Mohan Babu University Dept. of Cse Tirupati India

ISBN: (纸本)9798350373110

The IoT based scheme e-health uses some measurable device for to monitor the health condition of our body. The long-term measurable system like cardiovascular, electrocardiogram (ECG) are significant methods to diagnose the patient's diseases. The drawbacks of this measurable system such as high computational cost, consume more time for training and ECG signals contained some noise and corrupted signals. To addresses this kind of problem, the Denoising Convolution Auto Encoder (DCAE) with Bidirectional Long Short-Term Memory (Bi-LSTM) method is implemented. The DCAE contains encoder and decoder part to convert the ECG corrupted signals into compressed form. Bi-LSTM attached to encoder part that carries the input signals and trains that. Bi-LSTM carries double layers, which makes the training process easily. This proposed method tested on MIT-BIH databases. The advantages of this method, auto encoder easily convert the corrupted signals without using any parameters and reduce computation cost. Bi-LSTM reducing the training process, time consumption and it consisted of double layers, which increase the compression process of signals fastly. The developed DCAE-Bi-LSTM gives better output rate of 97.85% which is better than other existing methods like CNN-LSTM and SVM. © 2024 IEEE.

关键词： Signal denoising

来源：评论

学校读者我要写书评

暂无评论

An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal

An Automated Sleep Stage Classification for Healthcare Monit...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar Gurumoorthy, Sasi Chethana, D.T. Ramesh, M. Upendra Roy, B.P. Sapna Kumari, C. J.J.College of Engineering and Technology Department of Computer Science and Engineering Trichy India Gitam School of Technology Gitam University Department of Computer Science & Engineering India K L University Department of Computer Science & Engineering Vadeeshwaram Vijayawada India Channabasaveshwara Institute of Technology Department of Electronics and Communication Engineering Tumkur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

It is well-established that biomedical signals convey crucial data regarding the functioning of living systems. The physiological and clinical information included in these signals can be improved with adequate processing. Modern qualitative and quantitative analyses of physiological systems and events rely on digital signal processing and pattern recognition methods. Analysis and interpretation of a medical practitioner's signal carry the weight of the analyst's knowledge and expertise, yet such analysis is inherently subjective. If done logically, computer analysis of biomedical information might provide credibility to the expert's interpretation by providing an objective second opinion. Furthermore, it allows for enhanced diagnosis and online monitoring of critically ill patients. The current research intends to develop effective methods for utilizing sleep-monitoring health gadgets. When it comes to handling complex classification or pattern recognition issues, the Support Vector Machine (SVM) is the instrument of choice. In this article, we focus on using support vector machines (SVMs) to identify and categorize apnea. When compared to other methods of categorization, such as sophisticated statistical approaches, SVM performed better. Both an adaptive classification model and a novel approach to merging the decisions of ensemble-based classification models are proposed in the work. The current method relies on an ensemble classifier system and a huge number of features, making it both effective and trustworthy. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Hybrid of Ant Colony and Artificial Bee Colony Optimization for Resource Allocation in Cognitive Radio Networks 2

Hybrid of Ant Colony and Artificial Bee Colony Optimization ...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Lakshmiramana, P. Al-Farouni, Mohammed Senthil, P. Shashidhara, K.S. Mutalik, Pramod Madanapalle Institute of Technology and Science Computer Science and Technology Madanapalle Angallu India The Islamic University Najaf Iraq S.A. Engineering College Department of Information Technology Chennai India Nitte Meenakshi Institute of Technology Dept. of Ece Bengaluru India Visvesvaraya Technological University Karnataka Raichur India

ISBN: (纸本)9798350382952

The resource allocation in heterogeneous cognitive radio networks (CRN) system has been considered a key technology in wireless networks. However, the main challenge of the CRN system is resource allocation with minimized and maximum transmission in the spectrum band, enhancing optimum throughput increase, load balance and global optimization. The proposed method is hybrid optimization, comprising Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), allocating resources while considering power, throughput and equal to distributed the allocation. The load balancing technique should ensure virtual independent task scheduling approaches of the hybrid optimization and optimize scheduling and resource utilization to maximize throughput and create load balancing. Performance analysis of the proposed method was compared using resource allocation with existing load balancing and scheduling algorithms. Although the proposed ACO-ABC method exhibits high performance compared to existing methods such as Dynamic Fuzzy-based Primary User Aware Clustering (DFPC), non-orthogonal multiple access (NOMA) and Hybrid Tabu Search-based Stimulated Algorithm (HTSA), experimental results demonstrate high performance achieving a Packet Delivery Ratio (PDR) of 97%, Delay of 0.009s, Energy consumed of 0.01 (nJ), and Throughput of 2.0 (Mpbs). © 2024 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Chaotic Grey Wolf Optimization for Energy-Efficient Clustering and Routing in Wireless Sensor Networks 2

Chaotic Grey Wolf Optimization for Energy-Efficient Clusteri...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Almusawi, Muntather Ravindran, Gobinath Parameshachari, B.D. Bhasker, B. Lavanya, N.L. The Islamic University Najaf Iraq SR University Department of Civil Engineering Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India REVA University School of Computing and Information Technology Bengaluru India Sapthagiri College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317558

Wireless Sensor Networks (WSNs), the imperative challenge of energy efficiency takes center stage. This paper delves into inventive methodologies to combat energy depletion in sensor nodes, introducing the Chaotic Grey Wolf Optimization Algorithm (CGWOA) for optimal Cluster Head (CH) selection in WSNs. An evolution strategy and a mutation operation operator, CGWOA aims to amplify energy efficiency, scalability, and minimize network overhead through streamlined clustering. The outcomes underscore CGWOA's potential as a solution for advancing energy-efficient clustering and routing in WSNs, particularly in optimizing CH selection. The ensuing numerical validation underscores CGWOA's revealing an energy consumption of 1100 J, a throughput ratio of 69,785 kbps, 4900 active nodes, and a network lifetime extension to 8021. This comparative analysis shows the emphasis on problems to overcome existing methods like Particle Swarm Optimization for Energy-Efficient Clustering (PSO-EEC), Adaptive Hybrid Clustering Scheme with Grey Wolf Optimization (AHCS-GWO), Hybrid Optimization for Cluster Head Selection (HOCK). © 2024 IEEE.

关键词： Cluster head Clustering Energy consumption Network Lifetime Wireless senor network

来源：评论

学校读者我要写书评

暂无评论

The Voiceless Assistant for The Specially Abled

The Voiceless Assistant for The Specially Abled

引用

Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： S Vijaya Shetty Ajith Ram Arjun Bhargav A Harshinee Kanchan Pathak Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Bengaluru India

A novel system aimed at bridging the communication gap between deaf and blind individuals and those who are not familiar with sign language or Braille is proposed. Google’s Speech Recognition library is used to convert speech to text and the pyttsx3 library is used to convert text to speech. The system is designed to interconvert between Indian sign language, Braille, and English text, creating a unified platform for individuals with different disabilities to communicate effectively. The sign language recognition component of the system was trained with a training accuracy of 97.73 percent and test accuracy of 98 percent. The Braille recognition component was trained with a training accuracy of 97 percent and test accuracy of 95.5 percent. The integration of sign language and Braille into one platform is a unique feature that sets this system apart from existing solutions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Strengthening Cybersecurity using a Hybrid Classification Model with SCO Optimization for Enhanced Network Intrusion Detection System 2

Strengthening Cybersecurity using a Hybrid Classification Mo...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Vallabhaneni, Rohith Pareek, Piyush Kumar Dontu, Sravanthi School of Electrical Engineering and Computer Science University of North Dakota Grand ForksND United States University of the Cumberlands United States Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India University of the Cumberlands Department of Information Technology United States

ISBN: (纸本)9798350382952

Polymorphic malware and encrypted traffic hinder Network Intrusion Detection Systems (NIDS) from detecting complex attacks. Cybercriminals exploit NIDS algorithm vulnerabilities, showing how attack tactics and cybersecurity defenses change. This study suggests improving Network Intrusion Detection Systems. A thorough preprocessing phase with normalization functions improves data accuracy and consistency. The Single Candidate Optimization (SCO) feature selection algorithm optimizes NIDS efficacy. A hybrid model using Wavelet Transform, Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN) is used for classification because it can identify sequential dependencies in network traffic data. A second SCO iteration for hyperparameter tuning optimizes model performance and refines. The evaluation stage uses the BoT-IoT dataset, a prominent benchmark. SCO can improve hyperparameter optimization and feature selection to create a more accurate and cyberattack-resistant NIDS. The method's 99.6% accuracy is confirmed by experiments and performance evaluations. This shows how effective it is compared to current models, which strengthens cybersecurity defenses against changing attack landscapes. Similar trends are seen in the F1-scores, which range from 96.3% (ResNet50) to 99.4% (Proposed model). The Projected model performs exceptionally well in terms of stands out with the highest values in all metrics. © 2024 IEEE.

关键词： Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Shallow Convolution Neural Network for Real-Time Change Detection in Agricultural Lands

Shallow Convolution Neural Network for Real-Time Change Dete...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Ramyateja, O. Al-Jawahry, Hassan M. Shreyas, A.V. Nagendar, Y. Preetha, R. Maisammaguda Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Sr University School of Computer Science and Engineering Warangal India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India

ISBN: (纸本)9798350375442

The remote sensing technology is helpful for collecting information regarding usage of multispectral data from backscattered coefficients and optical sensors through sensors. The main challenge for an unsupervised change detection is distinguishing among a potential change to significant changes from noise. The Shallow Convolution Neural Network (SCNN) is proposed for change detection in agriculture lands. The Onera Satellite Change Detection (OSCD) dataset is used and it is preprocessed by Non-Local Means (NLM) filters which is helpful to denoise hyperspectral or multispectral images. Through preserving land structures, it enables accurate detection of changes in agricultural areas. Then, the CNN is used for extracting features in which primary layer captures low-level features such as textures and edges while deep layers recognize much complex structures and patterns. The SCNN mitigates overfitting issues particularly when training which leads in satellite-based monitoring thereby providing effective change detection. The Overall Accuracy (OA), Kappa, IoU and mIoU are taken to estimate SCNN performance. The SCNN obtains 95.19%, 0.896, 0.842 and 0.875 of OA, kappa, IoU and mIoU for OSCD dataset which is better than Deep Neural Network (DNN). © 2024 IEEE.

关键词： Change detection

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124页 << < 24 25 26 27 28 29 30 31 32 33 > >>

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：