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

424 篇 会议
101 篇 期刊文献
5 册 图书

馆藏范围

530 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

247 篇 工学
- 183 篇 计算机科学与技术...
- 159 篇 软件工程
- 66 篇 信息与通信工程
- 54 篇 控制科学与工程
- 43 篇 生物工程
- 33 篇 电气工程
- 31 篇 生物医学工程（可授...
- 29 篇 网络空间安全
- 22 篇 电子科学与技术（可...
- 21 篇 安全科学与工程
- 17 篇 土木工程
- 14 篇 动力工程及工程热...
- 13 篇 机械工程
137 篇 理学
- 65 篇 数学
- 46 篇 生物学
- 32 篇 物理学
- 18 篇 统计学（可授理学、...
- 17 篇 化学
- 13 篇 系统科学
- 12 篇 海洋科学
85 篇 管理学
- 54 篇 管理科学与工程(可...
- 36 篇 图书情报与档案管...
- 26 篇 工商管理
42 篇 医学
- 33 篇 基础医学(可授医学...
- 32 篇 临床医学
- 23 篇 公共卫生与预防医...
- 15 篇 药学(可授医学、理...
25 篇 法学
- 23 篇 社会学
15 篇 农学
- 14 篇 作物学
14 篇 经济学
- 14 篇 应用经济学
6 篇 教育学
3 篇 文学

主题

53 篇 accuracy
32 篇 convolutional ne...
27 篇 deep learning
22 篇 real-time system...
21 篇 computational mo...
21 篇 feature extracti...
17 篇 support vector m...
17 篇 predictive model...
17 篇 machine learning
16 篇 data models
16 篇 training
14 篇 internet of thin...
14 篇 artificial intel...
13 篇 data mining
13 篇 security
13 篇 cloud computing
12 篇 random forests
12 篇 technological in...
11 篇 medical services
11 篇 optimization

机构

16 篇 lovely professio...
14 篇 maharishi school...
12 篇 centre of interd...
12 篇 department of ma...
10 篇 department of ma...
8 篇 program of maste...
8 篇 master of comput...
7 篇 master program o...
7 篇 school of comput...
7 篇 radiology techni...
7 篇 prince shri venk...
6 篇 department of co...
6 篇 department of co...
6 篇 department of ma...
6 篇 department of co...
5 篇 school of comput...
5 篇 department of co...
5 篇 department of ma...
5 篇 school of comput...
5 篇 department of co...

作者

11 篇 bens pardamean
9 篇 teddy suparyanto
7 篇 pardamean bens
7 篇 thotad puneeth n...
7 篇 asha v.
7 篇 jayadurga r.
7 篇 hiroshi hashimot...
6 篇 r. jayadurga
6 篇 puneeth n. thota...
6 篇 binju saju
6 篇 ginni nijhawan
6 篇 pundru chandra s...
6 篇 manish kumar
6 篇 v. asha
5 篇 j. relin francis...
5 篇 ali albawi
5 篇 gaol ford lumban
5 篇 yasuhiro ohyama
5 篇 krisna adiyarta
5 篇 shanta kallur

语言

432 篇 英文
96 篇 其他
3 篇 日文
3 篇 中文
1 篇 德文

检索条件"机构=Master of Science in Computer Science DigiPen Institute of Technology"

共 530 条记录，以下是81-90 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：

Crop Yield Prediction Using Deep Reinforcement Learning 2

Crop Yield Prediction Using Deep Reinforcement Learning

引用

2nd International Conference on Trends in Electrical, Electronics and computer Engineering, TEECCON 2023

作者： Deivasigamani, Sudhakar Jaya Mabel Rani, A. Natchadalingam, R. Vijayakarthik, P. Bala Sendhil Kumar, G. Reddy, Pundru Chandra Shaker Digipulse Technologies Inc Scrum Master NJ United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India R.L.Jalappa Institute of Technology Computer Science and Engineering Karnataka Bengaluru India Sri Manakula Vinayagar Engineering College Master of Business Administration Puducherry India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350339949

Predicting crop production using information about the environment, soil, water, and crops themselves is an area ripe for investigation. Deep learning-based models often extract crop attributes for prediction. These techniques have the following drawbacks: The quality of the extracted characteristics substantially affects the efficacy of those models, and there is no simple non-linear or linear mapping between raw data and crop production estimates. Deep RL guides and inspires to compensate. Deep reinforcement learning uses RL and DL to accurately forecast agricultural yields from raw data. A Deep Recurrent Q-Network model predicts agricultural yield. This model uses Q-Learning reinforcement learning and a Recurrent Neural Network deep learning algorithm. Data parameters feed the Recurrent Neural network's layers. Q-learning networks forecast agricultural yields from data. Q-values are linear mappings from Recurrent Neural Network output values. A threshold and parametric features help the reinforcement learning agent predict crop productivity. Finally, the agent is awarded a final score that takes into account the steps taken to both reduce error and improve forecast accuracy. By maintaining the original data distribution, the suggested model efficiently predicts the crop production with an accuracy of 93.7%, exceeding competing models. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Applying Deep Learning and Transfer Learning Techniques for Improving Animal Intrusion Detection in Agriculture Farms

Applying Deep Learning and Transfer Learning Techniques for ...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Saraswathi, R. Shobarani, G. Subramani, A. Tamilarasan, D. Sreenivasa Institute of Technology and Management Studies Department of Master of Computer Applications Andhra Pradesh Chittoor India Islamiah Women's Arts and Science College Department of Artificial Intelligence Thiruppatur Tamil Nadu Vaniyambadi India M.V.Muthiah Government Arts College for Women Department of Computer Science Tamilnadu Dindigul India Sri Venkateswara College of Engineering and Technology Department of Master of Computer Applications Andhra Pradesh Chittoor India

ISBN: (纸本)9798350348910

Due to the nature of farmlands or provincial landscapes, monitoring is required to keep unauthorized persons out of the region and to safeguard the district from wildlife. Various techniques point straight towards perception, which is primarily for human gatecrashers, but we sometimes forget that the primary opponents of such farmers are the animals who trash the crops. Crop damage caused by monster attacks is one of the most serious dangers to collect yield. Crop striking is becoming one of the most distant human-natural life conflicts as created land expands into formerly untamed life domain. The principle is compelling and forceful checking of the wild creatures' squarely at home. This research paper is focused to develop computational techniques for identifying the animals that swoop into the agricultural area. Because there are so many various kinds of organisms, genuinely recognizing them may be a difficult task. In this paper, we have used a camera to capture routine images of the surrounding area and to observe the entire homestead at regular intervals. With the aid of further developed learning models, we can distinguish between different stages of a creature's development and play appropriate noises to frighten them off. The various convolutional brain network libraries and ideas that went into creating the model are acknowledged in this study. We have worked on the path of creature interruption discovery in agribusiness areas using deep learning and move learning techniques. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

引用

Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Handwritten Arithmetic Equation Solver 4

Deep Learning based Handwritten Arithmetic Equation Solver

引用

4th International Conference on Emerging Research in Electronics, computer science and technology, ICERECT 2022

作者： Karegowda, Asha Gowda Leenarani, A. Poornima, D. Pooja, R. Bhatt, Shreetha Bharathi, P.T. Siddaganga Institute of Technology Master of Computer Applications Tumkur India MS Ramaiah College of Arts Science & Commerce Dept. of Computer Science Bangalore India School of CSA Reva University Bangalore India

ISBN: (纸本)9781665456357

Manual recognition of different styles of handwritten characters, digits, symbols, and operators is quite intricate. This paper attempts to automate the process of recognition of digits (0 to 9), decimal point, opening and closing parenthesis ('(' and ')') brackets, and five binary arithmetic operators: sum (+), difference (-), product (×), exponent (A) and division in input handwritten equation followed by evaluation of expression. Firstly the segmentation based on binary threshold and contouring is applied to segregate the components of input handwritten equation. The individual segregated components are further input to Convolution Neural Networks(CNN) for recognition as part of second step, followed by evaluation of valid expression as the final step. The publicly available MNIST dataset handwritten digits (0-9) are considered and rest of the images for parenthesis, decimal point and five arithmetic operators are manually generated. The CNN model identified the handwritten expression with a high accuracy of 99% followed by a correct evaluation of the valid expression. © 2022 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks 6

Enhanced Demand-Side Management for Electric Vehicle Chargin...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Venkateswaran, N. Ramprabu, J. Marimuthu, Ramaprabha Ramana, Nagella Venkata Kotha, Mahesh Mallala, Balasubbareddy Department of Master of Business Administration Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Management Studies Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle517325 India CMR Technical Campus Telangana Hyderabad501401 India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

ISBN: (纸本)9798331522667

With the improvement in EV’s popularity, controlling the load demand of EVCS’s has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management. © 2025 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

Research and Innovation in Next Generation Security and Privacy in Industry 5.0 Iot 6

Research and Innovation in Next Generation Security and Priv...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Navale, Geeta S. Madala, Ravikiran Managuli, Manjunath Jayalakshmi, N. Kadiravan, G. Rawat, Ruchira Sinhgad Institute of Technology and Science Department of Computer Engineering Maharashtra Pune India Ph.D Information Technology Network and Data Center Engineer University of Cumberlands DurhamNC United States KLS Gogte Institute of Technology Department of Electronics and Communication Engineering Karnataka Belagavi India PSNA College of Engineering and Technology Master of Computer Applications Tamilnadu Dindigul India KL Deemed to Be University Department of Computer Science and Information Technology Andhra Pradsh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

The objective of the newest manufacturing paradigm, known as 'Industry 5.0,'is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers. © 2023 IEEE.

关键词： artificial intelligence cyber-physical systems human-machine collaboration Industry 5.0 Internet of Things IoT manufacturing privacy robotics security

来源：评论

学校读者我要写书评

暂无评论

Image To Text Description Platform For Yakshagana Images By Detecting Crown Using CNN

Image To Text Description Platform For Yakshagana Images By ...

引用

2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Kiranraj, M. Murthy, Anantha Manish Prathwini, Prathwini Savitha, G. Harshitha, G.M. NMAM Institute of Technology Department of Master of Computer Applications Karnataka Nitte574110 India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India Manipal Academy of Higher Education Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798331527518

This research called "Yakshakatha - Image to Text Platform for Yakshagana Images."It employs deep learning to classify crowns in Yakshagana Kirita using CNN classification. Yakshagana is a traditional South Indian art form, well known for its intricate costumes and headgear, posing specific challenges in identifying and classifying crowns. The system aims to classify various Kirita styles and provide detailed descriptions of the respective Yakshagana characters, or Vesha, offering valuable cultural and symbolic insights. The model was trained on a self-labelled dataset of Yakshagana images and used convolutional and pooling layers to capture fine details in crown designs. It achieved an impressive accuracy of 99%, ensuring precise identification and classification. Real-time predictions for the input images are made, which have predetermined mappings and description content for recognized characters. The system employs Google Gemini AI, in order to enrich the text description of the image by integrating cultural background. The responsive interface is provided for users on the image upload, real-time result, and enriched visual feedback using annotated output. This project combines modern image recognition with rich cultural content to preserve and promote the traditional heritage of Yakshagana. It will help the users to understand and appreciate the vibrancy of this art form. © 2025 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of Satellite Images for Historical Maps Classification using Machine Learning Algorithms 4

Analysis of Satellite Images for Historical Maps Classificat...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Hebri, Dheeraj Kirubakaran, S. Jasmine, R. Rajitha Poosamooper, Anitha Devi, S. Rukmani Joseph, D Raja Srinivas Institute of Technology Department of Master of Computer Applications Karnataka Valachil Mangalore574143 India CMR College of Engineering and Technology Department of Computer Science Engineering Telangana Hyderabad501401 India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai Thiruvallur601206 India Vel Tech Ranga Sanku Arts College Department of Computer Applications Tamil Nadu Chennai600062 India Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Department of Computer Science Tamil Nadu Chennai600124 India School of Aeronautical Sciences Hindustan Institute of Technology and Science Padur Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350385793

Remote sensing is concerned with high-resolution geographical data. Its applications include farming, water resources, ecology, forests, deserts, marine applications, soil types, disaster alerts, and management. To extract the necessary information from satellite images, considerable image processing is required. Image segmentation and categorization into land cover categories is an important stage in satellite image processing. This article describes a method for categorizing and detecting different forms of land cover from satellite data. In this study, we examine satellite images for historical map classification using machine learning algorithms. We acquire Landsat images to complete this tedious task. The images are carefully labeled using the ArcMap software. We categorize areas into five types: forest, building, water, barren, and agricultural land. The images are then sent to the Convolutional Neural Network (CNN) Model for feature extraction. The retrieved features were then exposed to three Machine Learning (ML) models: Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT). Standard classification measures are used to analyze the outcomes of all three models. When compared to the other two models in satellite image classification, CNN feature extraction with SVM classifier achieves the greatest accuracy of 95%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Architecture for Developing IoT Solutions Applied to Healthcare 2

A Novel Architecture for Developing IoT Solutions Applied to...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Levina Kaliyamoorthi, P. Dattu, Vinjamuri S.N.Ch. Dineshnath, G. Jaiganesh, S. Dobhal, Dinesh Chandra Robin, C. R. Rene Kns Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle517325 India Aditya Engineering College Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Department of Master of Computer Applications Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

The objective of this research study is to reduce the complexity and increase the flexibility to create and maintain computational solutions applied to health, where the scenarios are quite complex and dynamic. The main contributions of HealthDash are: (1) Provide dynamism in the process of creating IoT applications, both in the fog layer and in the cloud layer, through unification action of technologies adopted at both levels;(2) provide means to reduce the volume of information transmitted between the fog layer and the cloud layer, enabling the analysis and filtering of data closer to the edge of the network. This article proposes a solution for the development of IoT applications for health called HealthDash, based on the data flow-oriented programming paradigm, allowing to indicate in which layer of the architecture (fog or cloud) the data flows, which process data from the monitored patients, will be executed. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Deep Learning Framework for Prediction of Agricultural Yield 2

An Adaptive Deep Learning Framework for Prediction of Agricu...

引用

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

作者： Rao, K Rama Swapna Kumari, Manthena Eklarker, Ravindra Shaker Reddy, Pundru Chandra Muley, Kirti Burugari, Vijay Kumar Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Hyderabad India Vardhaman College of Engineering Department of Ai &ds Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's Institute of Management and Information Technology Master of Computer Applications Navi Mumbai India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350317558

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ML) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos. © 2024 IEEE.

关键词： DL Ensemble-Methods ML Smart-farms Yield-Prediction

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53页 << < 5 6 7 8 9 10 11 12 13 14 > >>

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：