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

18,465 篇 会议
113 篇 期刊文献
15 册 图书

馆藏范围

18,593 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

11,933 篇 工学
- 9,501 篇 计算机科学与技术...
- 5,441 篇 软件工程
- 3,065 篇 电气工程
- 2,918 篇 控制科学与工程
- 2,503 篇 信息与通信工程
- 952 篇 仪器科学与技术
- 926 篇 交通运输工程
- 923 篇 机械工程
- 639 篇 生物工程
- 619 篇 电子科学与技术（可...
- 551 篇 动力工程及工程热...
- 536 篇 生物医学工程（可授...
- 480 篇 安全科学与工程
- 380 篇 网络空间安全
- 377 篇 石油与天然气工程
- 257 篇 光学工程
2,970 篇 管理学
- 2,240 篇 管理科学与工程(可...
- 1,119 篇 图书情报与档案管...
- 855 篇 工商管理
2,929 篇 理学
- 1,497 篇 数学
- 783 篇 生物学
- 500 篇 统计学（可授理学、...
- 481 篇 物理学
- 390 篇 系统科学
- 389 篇 化学
606 篇 医学
- 502 篇 临床医学
- 438 篇 基础医学(可授医学...
- 250 篇 公共卫生与预防医...
325 篇 法学
- 273 篇 社会学
277 篇 经济学
- 272 篇 应用经济学
172 篇 教育学
149 篇 农学
32 篇 文学
31 篇 军事学
11 篇 艺术学

主题

1,144 篇 data acquisition
765 篇 data mining
564 篇 data models
560 篇 real-time system...
443 篇 intelligent syst...
424 篇 big data
420 篇 conferences
406 篇 cloud computing
377 篇 internet of thin...
354 篇 computational mo...
343 篇 monitoring
320 篇 computer archite...
318 篇 sensor systems
313 篇 machine learning
306 篇 control systems
303 篇 training
296 篇 feature extracti...
295 篇 deep learning
285 篇 robot sensing sy...
281 篇 neural networks

机构

49 篇 university of ch...
42 篇 lviv polytechnic...
29 篇 hubei key labora...
25 篇 school of comput...
20 篇 west ukrainian n...
18 篇 beijing key labo...
17 篇 shenzhen institu...
17 篇 chitkara univers...
16 篇 university of el...
15 篇 lovely professio...
15 篇 school of comput...
15 篇 institute of aut...
14 篇 faculty of compu...
14 篇 school of artifi...
14 篇 shenzhen institu...
14 篇 peng cheng labor...
13 篇 ieee
13 篇 college of compu...
12 篇 key laboratory o...
12 篇 beijing univ pos...

作者

25 篇 anatoliy sachenk...
22 篇 wu xinyu
18 篇 anatoly sachenko
16 篇 a. sachenko
16 篇 liu yang
16 篇 xiong gang
15 篇 chen chen
14 篇 sachenko anatoli...
12 篇 xinyu wu
12 篇 li yang
12 篇 yang yang
11 篇 zhang tao
11 篇 wang lei
11 篇 li peng
11 篇 li li
11 篇 kurosh madani
11 篇 sachenko a.
11 篇 wu bin
11 篇 iryna turchenko
11 篇 golovko vladimir

语言

18,118 篇 英文
408 篇 其他
81 篇 中文
2 篇 西班牙文

检索条件"任意字段=IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems"

共 18593 条记录，以下是141-150 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：

Mastering Distributed Storage: Architectural Design, Performance Analysis, and Cost Analysis of Modern Distributed File systems: HDFS and NFS

Mastering Distributed Storage: Architectural Design, Perform...

引用

2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Hazarika, Akaash Vishal Shah, Mahak Shah, Milavkumar Banerjee, Monojit North Carolina State University Department of Computer Science Raleigh United States Columbia University Department of Computer Science New York United States Seattle United States Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331527495

As data storage and management demands grow, distributed file systems (DFS) have become critical for large-scale data handling. This paper discusses two of the most widely used DFSs: Hadoop Distributed File System (HDFS) and Network File System (NFS). We discuss some of the key concepts like data distribution, replication strategies, and consistency models;then we present a comparison between their architectures and operational characteristics. The results of the assessment show our metrics of throughput, latency, and scalability under different workloads, pointing out that HDFS is optimized for big data applications while NFS is more suited for traditional file sharing. Moreover, we present a cost analysis by assessing initial setup and operational costs to detail financial implications of each system and hence advise organizations on the basis of performance and cost-effectiveness in making informed decisions. © 2025 ieee.

关键词： Big data Distributed File systems HDFS Large Scale systems NFS Performance Analysis

来源：评论

学校读者我要写书评

暂无评论

Dear participants of the 9th international conference “intelligent data acquisition and advanced computing systems”!

Dear participants of the 9th International conference “Inte...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dear participants of the 9th international conference “intelligent data acquisition and advanced computing systems”!

Dear participants of the 9th International Conference “Inte...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Application of Digital Empowerment Combined with intelligent Algorithms of Computer Technology in Decision Support systems

The Application of Digital Empowerment Combined with Intelli...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Wan Lu Li Chen Fei Gao Chang Wang Wuhan Windoor Information &Technology Co. Ltd Wuhan Hubei China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), this paper constructs a new IDSS framework aimed at improving decision-making efficiency and accuracy. In terms of research methods, large-scale distributed computing is performed in the cloud based on parallel computing technology; the logical reasoning of expert systems and the self-learning ability of graph neural networks are combined to achieve dynamic updates and efficient processing of data by unstructured knowledge bases. Compared with traditional DSS, the accuracy of IDSS based on the fusion of GNNs and ES in financial forecasting and risk assessment scenarios is improved to more than 90%, and resource utilization is significantly optimized. This study shows that the integrated application of intelligent algorithms has broad prospects in improving the dynamic adaptability of decision support systems and coping with complex decision-making environments.

关键词： Decision support systems Accuracy Heuristic algorithms systems architecture Graph neural networks Real-time systems Risk management Resource management Expert systems Optimization

来源：评论

学校读者我要写书评

暂无评论

Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

引用

ieee international conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

来源：评论

学校读者我要写书评

暂无评论

Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

引用

26th international conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston TX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

关键词： Autism Mirror game Motion control Reinforcement learning Safety action filter

来源：评论

学校读者我要写书评

暂无评论

Achieving Balanced Allocation in Human Resources through LSTM Framework Method

Achieving Balanced Allocation in Human Resources through LST...

引用

intelligent systems and Computational Networks (ICISCN), international conference on

作者： A. Giriprakash V. Arunadevi K. Rajalakshmi Shamim Ahmad Khan R. Jeyalakshmi Ponni Valavan M Department of MBA Sakthi Institute of Information and Management Studies India Department of Science and Humanities Dr. Mahalingam College of Engineering and Technology India Department of ECE Glocal School of Science & Technology Glocal University Saharanpur India Master of Business Administration Laki Reddy Bali Reddy College of Engineering (Autonomous) Affiliated to JNTU-K India Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

This paper proposes a novel human resource management system leveraging Long Short-Term Memory (LSTM) networks for optimal HR allocation. The process begins with data acquisition, including performance metrics, attendance records, and employee details, followed by Min-Max normalization for uniform formatting. Principal Component Analysis (PCA) is employed for feature selection, reducing variables while retaining critical information. LSTM networks, with their ability to analyze sequential data and capture temporal dependencies, enable accurate predictions of workforce demands and optimal resource distribution. Experimental results demonstrate the model's superiority over conventional methods, achieving balanced HR allocation, improved workforce control, enhanced employee satisfaction, and higher organizational performance. This scalable approach aligns staffing with organizational objectives, preventing overstaffing or understaffing. By integrating advanced machine learning into HR management, the proposed system offers a significant step toward smarter workforce planning and improved HR practices, paving the way for efficient and adaptable organizational management.

关键词： Measurement data acquisition Machine learning Feature extraction Planning Resource management intelligent systems Human resource management Long short term memory Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Dear participants of the 6th international conference intelligent data acquisition and advanced computing systems: Technology and Applications!

Proceedings of the 6th IEEE International Conference on Inte...

引用

Proceedings of the 6th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS'2011 2011年 1卷 vi页

作者： Havlíček, Václav Ternopil National Economic University Ukraine

来源：评论

学校读者我要写书评

暂无评论

A Missing data Imputation Method for Spatio-temporal Decoupled Remaining Useful Life Prediction of CNC Machines 8th

A Missing Data Imputation Method for Spatio-temporal Decoup...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chen, Weixuan Niu, Ben Xiao, Qinge Gu, Yuntao Yang, Zhile Shenzhen University Shenzhen518060 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9789819602933

Predicting the Remaining Useful Life (RUL) of Computer Numerical Control (CNC) machines is key in Predictive Health Management (PHM), where missing data is a persistent challenge. The spatio-temporal-duration coupling in sensor data complicates missing data recovery. This paper introduces a spatio-temporally decoupled RUL prediction method, STDP, that uses duration-focused imputation under missing data constraints. Employing a cross-dimensional self-attention (CDSA) approach for both imputation and prediction, we also developed a bidirectional reconstruction-prediction training strategy to dynamically adjust losses for data reconstruction and RUL prediction tasks. Our experimental results, across various missing rate scenarios, demonstrate that our method surpasses existing advanced methods in data imputation and RUL prediction, especially at high missing rates, thereby significantly enhancing prediction reliability and model stability in the manufacturing sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

AI2DS: advanced Deep Autoencoder-Driven Method for intelligent Network Intrusion Detection systems

AI2DS: Advanced Deep Autoencoder-Driven Method for Intellige...

引用

AI in Cybersecurity (ICAIC), international conference on

作者： Md Habibur Rahman Leo Martinez Avdesh Mishra Mais Nijim Ayush Goyal David Hicks Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Network security is a growing concern as digital infrastructure expands, and traditional measures struggle against modern cyber threats. With the increasing complexity of attacks, there is a need for more adaptive and intelligent solutions. This research introduces AI2DS (advanced Deep Autoencoder-Driven Method for intelligent Network Intrusion Detection systems), an autoencoder-based architecture that enhances security by identifying deviations from normal network behavior. The model is trained on normal data, using reconstruction errors to detect anomalies through adaptive thresholding. By simplifying attack classification into a single ‘intrusion’ class, AI2DS demonstrates high accuracy and broad applicability. The model shows average improvements of $\mathbf{6. 6 1 \%}, \mathbf{3 1. 1 1 \%}, \mathbf{1 1. 4 6 \%}$, and $\mathbf{1 6. 9 1 \%}$ in Precision, Recall, F1 Score, and Accuracy, respectively, over state-of-the-art methods, with potential for real-time application and future advancements.

关键词： Deep learning intelligent networks Adaptation models Accuracy Autoencoders Intrusion detection US Department of Homeland Security Network security Real-time systems data models

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 << < 11 12 13 14 15 16 17 18 19 20 > >>

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：