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

1,380 篇 会议
11 册 图书
10 篇 期刊文献

馆藏范围

1,401 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

937 篇 工学
- 795 篇 计算机科学与技术...
- 463 篇 软件工程
- 183 篇 电气工程
- 109 篇 信息与通信工程
- 109 篇 控制科学与工程
- 89 篇 动力工程及工程热...
- 53 篇 机械工程
- 49 篇 电子科学与技术（可...
- 45 篇 环境科学与工程（可...
- 43 篇 生物工程
- 29 篇 仪器科学与技术
- 21 篇 石油与天然气工程
- 21 篇 网络空间安全
- 19 篇 力学（可授工学、理...
- 19 篇 建筑学
- 19 篇 安全科学与工程
- 18 篇 材料科学与工程（可...
- 16 篇 土木工程
275 篇 管理学
- 227 篇 管理科学与工程(可...
- 96 篇 工商管理
- 70 篇 图书情报与档案管...
262 篇 理学
- 156 篇 数学
- 47 篇 生物学
- 35 篇 系统科学
- 33 篇 物理学
- 30 篇 大气科学
- 26 篇 统计学（可授理学、...
32 篇 经济学
- 31 篇 应用经济学
32 篇 法学
- 22 篇 社会学
19 篇 医学
- 18 篇 临床医学
11 篇 教育学
8 篇 农学
5 篇 文学
4 篇 军事学
1 篇 艺术学

主题

248 篇 cloud computing
85 篇 grid computing
50 篇 computational mo...
48 篇 distributed comp...
36 篇 servers
34 篇 parallel computi...
32 篇 quality of servi...
31 篇 resource managem...
30 篇 computer archite...
30 篇 distributed data...
29 篇 big data
23 篇 protocols
22 篇 security
19 篇 monitoring
19 篇 fog computing
18 篇 internet of thin...
18 篇 reliability
18 篇 parallel process...
18 篇 scalability
18 篇 processor schedu...

机构

6 篇 china electric p...
6 篇 the islamic univ...
6 篇 department of co...
6 篇 galgotias univer...
6 篇 school of softwa...
5 篇 school of comput...
4 篇 school of electr...
4 篇 state grid energ...
4 篇 department of la...
4 篇 school of electr...
4 篇 school of inform...
4 篇 chitkara univers...
4 篇 school of comput...
4 篇 guangdong engine...
4 篇 chitkara univers...
4 篇 department of co...
3 篇 russian acad sci...
3 篇 department of el...
3 篇 college of mathe...
3 篇 school of electr...

作者

6 篇 rong chunming
5 篇 sukumar nandi
5 篇 hui liu
5 篇 liu hui
5 篇 pabitra mohan kh...
4 篇 zhangxin chen
4 篇 awadhesh kumar s...
4 篇 fatos xhafa
3 篇 mo zeyao
3 篇 yang bo
3 篇 akash punhani
3 篇 jiapeng xiu
3 篇 li zheng
3 篇 kumari k. aruna
3 篇 kun wang
3 篇 kawata shigeo
3 篇 xhafa fatos
3 篇 abramson david
3 篇 yidong cui
3 篇 cristea valentin

语言

1,372 篇 英文
16 篇 其他
15 篇 中文

检索条件"任意字段=2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering"

共 1401 条记录，以下是311-320 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：

Prediction of Penetration Depth Based on OWMAML

Prediction of Penetration Depth Based on OWMAML

引用

Machine Learning, cloud computing and Intelligent Mining (MLCCIM), international conference on

作者： Jimin Wang Binbin Wu Ying Cao Lei Zhang College of Computer and information Hohai University Nanjing China Institute of Denfense Engineering AMS.PLA Luoyang China

Due to the lack of experimental data, it is difficult to apply machine learning or deep learning methods to conduct related research of penetration depth prediction. However, the existing few-shot learning models such as MAML are almost based on the assumption that the data is uniformly distributed, so such models also struggle to achieve success on penetrating data. In this paper, we propose an Optimization based Weighted MAML algorithm. The data is first partitioned using the K-means algorithm. Then the data from different partitions are randomly extracted and introduce the corresponding weights to train. Finally, to improve the stability of model convergence, the method is combined with an improved activation function and cosine annealing algorithm for evaluation. We compared our method with the original MAML and other existing few-shot learning methods on both concrete and tundra datasets, and the results show that the method in this paper has a relatively significant improvement in prediction accuracy and stability on both datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Study of Smart grid Application and Impact of Renewable Resources When Integrated with Smart grid 2nd

A Study of Smart Grid Application and Impact of Renewable Re...

引用

2nd international conference on Electronic Systems and Intelligent computing, ESIC 2021

作者： Kumari, Deepa Sharma, Ashish Department of Electrical Engineering Chandigarh University Mohali India

ISBN: (纸本)9789811694875

The article focuses on the design and implementation of a Smart grid that utilizes renewable energy sources. Various renewable sources, such as mini-hydropower plants, solar power plants, and wind power plants, are integrated into the smart grid's designed scheme. The current trend of renewable energy resources becoming a larger part of the electricity supply mix has increased the demands on the power system for supply quality. The addition of distributed generating units to the electricity system has posed new planning and operational problems A major priority for future power generation has been to use RER due to the energy crisis. We discuss the benefits, complexity, challenges, and potential solutions for renewable energy deployment in the SG context. Our research also looks at how RERs may be effectively integrated into an energy market with higher generating capacity, better power quality, and more dependability. This paper also looked at the issues surrounding renewable energy integration in SG. A thorough SG model with integrated renewable energy is also given. Furthermore, this paper gives a viewpoint on SG in terms of development, economic progress, and implementation obstacles. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Terms Development of Additive Manufacturing 2

Terms Development of Additive Manufacturing

引用

2nd IEEE international conference on Digital Twins and parallel Intelligence, DTPI 2022

作者： Tamir, Tariku Sinshaw Xiong, Gang Jiang, Jingchao Shen, Zhen Lodhi, Ehtisham Ali, Hub Wan, Li Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266000 China Technology Co. Ltd Foshan528225 China

ISBN: (纸本)9781665492270

Nowadays, additive manufacturing has been increasingly developed and applied in many fields. As a rapid development of additive manufacturing, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyze the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development. © 2022 IEEE.

关键词： 3D printers

来源：评论

学校读者我要写书评

暂无评论

AAWAS: An Application and Workload-Aware Scheduling Technique for Efficient Task Allocation in cloud computing Environments

AAWAS: An Application and Workload-Aware Scheduling Techniqu...

引用

Advancements and Key Challenges in Green Energy and computing (AKGEC), international conference on

作者： Vidhyasagar Bs M. Arvindhan J. Arunnehru H. Anwarbasha Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Amity School of Engineering & Technology Gwalior Madhya Pradesh India SRM Institute of Science and Technology Chennai India Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

One interesting aspect of distributed systems is cloud computing. It offers its services on an as-needed, pay-per-use basis. Research challenges in cloud computing include the scheduling and balance of tasks. Scheduling tasks entails assigning them to available resources (Virtual Machines), whereas load balancing is spreading out the work over multiple available resources. We offer a job scheduling technique that takes into account anticipated demands on compute nodes. We begin by investigating the reasons for the disparity in workloads and the viability of redistributing computing resources. All of the complex simulations are run in clouds. The process consisted of two distinct parts. The simulation begins with a randomly generated workload. Second, we combine the workload prediction model with an application and workload-aware scheduling algorithm (AAWAS) [14]. We present a parallel job scheduling approach using computational node workload prediction AAWAS to simplify the AAWAS algorithm. Experimental results reveal that AAWA is superior to other algorithms in terms of minimizing makespan and optimizing resource utilization, and the proposed method is compared to FCFS, SJF, Min-Min, and EDF. To make Task scheduling in compute clouds faces various challenges due to the highly dynamic environment, such as availability, access policies, security, and reliability of computing resources. Furthermore, the collaborative nature of cloud computing projects on the internet adds complexity to task scheduling. From simulation results, it is evident that our proposed scheduler AAWAS outperforms A2C, DQN, and PPO across the board by 4.1%, 2.6%, and 3.4%, respectively. Because AAWS relies on policy for learning rather than Q-value, its superiority can be defended. Several environmental factors affect AAWAS and contribute to its consistent learning process and the high chance of accurate predictions. As a result, AAWASC may learn from experiences independently of target networks,

关键词： cloud computing Protocols Scheduling algorithms Computational modeling Throughput Prediction algorithms Virtual machines Resource management Faces Load modeling

来源：评论

学校读者我要写书评

暂无评论

Reliability Evaluation of Distribution grid Considering Flexible grid Connection of distributed Photovoltaic 2

Reliability Evaluation of Distribution Grid Considering Flex...

引用

2nd international conference on Electronics, Communications and Information Technology, CECIT 2021

作者： Li, Zecheng Sun, Yanying Feng, Chen State Grid Ningxia Electric Power Research Institute Yinchuan China School of Mechanical Electronic and Information Engineering Beijing100083 China Shandong University The Key Laboratory of Power System Intelligent Dispatch and Control Jinan China

ISBN: (纸本)9781665437578

Aiming at the problem that the flexible access of distributed photovoltaics to the distribution network affects its reliability, this paper establishes a model of the distributed photovoltaic output probability based on the typical output characteristics of distributed photovoltaics firstly. Subsequently, the power grid risk assessment indicators are determined to facilitate the quantification of distribution network risks, including voltage over-limit, power over-limit, and load loss risk indicators. Finally, a distribution network model containing distributed photovoltaic grid-connected is established on the MATLAB simulation platform, and the risk assessment of the distribution network containing distributed photovoltaic grid-connected is carried out. © 2021 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Seagull-optimized Deep Convolutional Neural Network for Detecting distributed Denial of Service Attack in cloud computing

Seagull-optimized Deep Convolutional Neural Network for Dete...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： Yogesh B. Sanap Pushpalata G. Aher Computer Science and Engineering Sandip University Nashik Sandip University Nashik Maharashtra

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Due to the enormous benefits and applications of cloud computing, there is a lot of interest from researchers on cloud computing. The most significant security risk associated with cloud computing is insider distributed denial of service (DDoS), which affects the entire deactivation of services due to its dispersed architecture and utter dependency on the internet for service supply. Machine learning and Deep learning have appeared as a potent tool in cyber security recently, and numerous attack detection methods have been put out in recent studies. Nevertheless, existing studies on attack detection encounter several challenges, including data imbalance, interpretability issues, inaccurate detection, overfitting, and time complexity issues. Therefore, in order to overcome these difficulties, a deep learning model, Seagull optimized Synthetic Minority Over-sampling Technique enabled Deep Convolutional Neural Network (SgO-SCNN) is developed in this research to detect DDoS attacks. The optimized SMOTE employed in the model addresses the imbalanced data issue and improves the model’s performance by effectively rebalancing the data. The DCNN classifier can detect the attacks more efficiently and reduces the computation of the model. The seagull optimization tunes the DCNN classifier’s parameters and improves the model’s performance in attack detection with fast convergence, which reduces the model’s time complexity. The experimental outcomes indicate that the developed $\mathrm{SgO}-\mathrm{SCNN}$ model outperforms the existing approaches by attaining enhanced values of accuracy 96.05%, F1-score 94.66%, precision 94.84%, and recall 94.48%.

关键词： cloud computing Accuracy Computational modeling Denial-of-service attack Data models Convolutional neural networks Computer crime Time complexity Optimization Overfitting

来源：评论

学校读者我要写书评

暂无评论

A Genetic Algorithm-Based Demand Side Management Program for Implementation of Virtual Power Plant Integrating distributed Energy Resources 2nd

A Genetic Algorithm-Based Demand Side Management Program for...

引用

2nd international conference on Innovation in Electrical Power engineering, Communication, and computing Technology, IEPCCT 2021

作者： Mohanty, Sarthak Panda, Subhasis Sahu, Binod Kumar Rout, Pravat Kumar Department of Electrical Engineering Siksha ‘O’ Anusandhan University Bhubaneswar751030 India Department of Electrical and Electronics Engineering Siksha ‘O’ Anusandhan University Bhubaneswar751030 India

ISBN: (纸本)9789811670756

The concept of Virtual Power Plant (VPP) provides flexibility in the operation of current Smart grid (SG) based electric power systems. Demand Side Management (DSM) programs present a lot of scope in the optimization of the load consumption and curtailing of loads during peak usage periods. VPP and DSM together enable communication-based dispatching of loads depending upon the consumption pattern of consumers and generation capacity of distributed Energy Resources (DER). Various optimization techniques have been proposed for efficient and cost-effective dispatch schedules within the intended power networks. In this paper, a Genetic Algorithm (GA) based optimization approach is presented to allow for reduction of the overall cost of the market price of electricity and reduction of burden during peak periods of use after integration of DERs and Demand Response (DR) strategy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtual Power Plants

来源：评论

学校读者我要写书评

暂无评论

Fed-STP: An Improved Federated Learning Approach via Stage-wise Training and Pruning

Fed-STP: An Improved Federated Learning Approach via Stage-w...

引用

cloud computing, Big Data Applications and Software engineering (CBASE), international conference on

作者： Youxin Huang Weizhe Chen Shunzhi Zhu Zhicai Huang School of Computer and Information Engineering Xiamen University of Technology Xiamen China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Information and Smart Electromechanical Engineering Xiamen Huaxia University Xiamen China

Federated Learning (FL) is a transformative machine learning paradigm, allowing multiple clients to train a global model while maintaining local data privacy collaboratively. However, each client's unique local data distribution introduces challenges, leading to imbalances in individual model optimization and affecting model performance and convergence in non-Independent and Identically distributed (non-IID) settings. To mitigate these issues, we propose an innovative federated learning algorithm, 'Federated Learning with Stage-wise Training and Pruning' (Fed-STP), aiming to optimize model performance under non-IID conditions. Fed-STP employs client-specific model pruning and a stage-wise training strategy to enhance local models' adaptability to their unique data characteristics. This approach also minimizes the information loss resulting from model parameter variations during the server aggregation process, thus indirectly enhancing the overall performance of the federated global model. Comprehensive experiments and detailed analyses confirm Fed-STP's superior performance over conventional FL methods, showing significant improvements in diverse image classification tasks, mainly when dealing with non-IID client data, with performance enhancements reaching up to 9.25%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy big data automatic desensitization model based on Spark parallel computing framework 2

Energy big data automatic desensitization model based on Spa...

引用

2nd international conference on Big Data Economy and Information Management, BDEIM 2021

作者： Zhu, Dongge Ma, Rui Chai, Yufeng Cai, Bing Han, Liang Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd Yinchuan China

ISBN: (纸本)9781665482882

With the vigorous development of the electric power industry, the large amount of data generated has also brought tremendous pressure to information security. Data desensitization model is limited by the computing efficiency and memory capacity of a single computing node, which is difficult to meet the desensitization needs of massive data. Based on Spark parallel computing framework, an automatic desensitization model of energy big data is proposed. Depending on the regular dependence between data sets, the elastic distributed data set is established, and the big data parallel processing framework is established by using Spark framework, which decomposes the single node processing task into multi node data blocks. The distributed anonymization algorithm is designed to divide the buffer tuple to complete the anonymization before desensitization. The reserved number of generalized nodes is determined, and a distributed desensitization model based on cooperative computing of multiple computing nodes is established. The experimental results show that the privacy protection strength of this model is 0.0763 and 0.1007 higher than that of the data desensitization model based on centralized anonymization algorithm and regular specific format when the data set size reaches the maximum of 60G. Therefore, the designed model has good data desensitization ability, and can ensure the privacy of data under the condition of large amount of data. © 2021 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Optimizing Renewable Energy Integration in Smart grids through IoT-Driven Management Systems

Optimizing Renewable Energy Integration in Smart Grids throu...

引用

Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： V Dankan Gowda S G Surya N M G Kumar Kdv Prasad Vk Satya Prasad Mandeep Kaur Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore India Department of Computer science and Engineering SCMS School of Engineering and Technology Karukutty Ernakulam Kerala India Department of Electrical and Electronics Engineering Sree Vidyanikethan Engineering College Mohan Babu University Tirupathi Andhar Pradesh India Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Symbiosis Institute of Business Management Hyderabad Department of Electronics and Communication Engineering Punjabi University Patiala Punjab India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The incorporation of renewable energy sources into smart grid systems does basically signify a significant move of achieving sustainable energy consumption and management. On the contrary, due to the unpredictability of renewables and the energy grids’ increased complexity, there remain certain issues to be tackled. This study explores enhancing of the capacity of energy to be derived from renewable sources through IoT linked management systems. Through the utilization of modern IoT technologies which contribute towards maximizing the effectiveness of smart grid operations the study aims to accomplish this thereby enhancing the efficiency, reliability, sustainability of smart grids, which in turn facilitate the balancing of energy supply and demand and optimal energy distribution followed by the ready integration of renewable energy sources. This paper focuses on a holistic solution that includes the utilization of IoT sensors and devices to perform real-time energy monitoring, devising smart algorithms for an energy management system, and harnessing the power of cloud computing to analyze data. This part involves implementation explaining the practical steps done to work out the framework: system’s architecture design, IoT devices selection, data management activities and the control mechanism’s integration. Simulation results represent the ability of distributed IoT systems to advance smart grid practices by showing a decrease in energy transportation losses, AC grid stability and the integration of RES potential. Problems that come up during the course of project, like scalability and security, are described, and solutions to those problems proposed.

关键词： Renewable energy sources Supply and demand Wind energy Power system stability Stability analysis Smart grids Internet of Things

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141页 << < 28 29 30 31 32 33 34 35 36 37 > >>

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：