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

147,719 篇 会议
1,641 篇 期刊文献
441 册 图书
3 篇 学位论文

馆藏范围

149,803 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

87,780 篇 工学
- 67,316 篇 计算机科学与技术...
- 33,966 篇 软件工程
- 24,645 篇 电气工程
- 19,210 篇 信息与通信工程
- 13,412 篇 控制科学与工程
- 4,465 篇 电子科学与技术（可...
- 4,403 篇 机械工程
- 3,301 篇 仪器科学与技术
- 3,267 篇 动力工程及工程热...
- 2,867 篇 生物工程
- 2,192 篇 石油与天然气工程
- 2,083 篇 生物医学工程（可授...
- 2,060 篇 交通运输工程
- 2,042 篇 网络空间安全
- 1,752 篇 安全科学与工程
- 1,329 篇 环境科学与工程（可...
- 1,156 篇 光学工程
18,612 篇 理学
- 10,418 篇 数学
- 3,333 篇 物理学
- 3,327 篇 系统科学
- 3,148 篇 生物学
- 2,603 篇 统计学（可授理学、...
- 1,089 篇 化学
15,876 篇 管理学
- 12,091 篇 管理科学与工程(可...
- 5,118 篇 工商管理
- 4,985 篇 图书情报与档案管...
2,688 篇 医学
- 2,098 篇 临床医学
- 1,606 篇 基础医学(可授医学...
1,801 篇 法学
- 1,335 篇 社会学
1,601 篇 经济学
- 1,563 篇 应用经济学
779 篇 教育学
601 篇 农学
370 篇 文学
208 篇 军事学
125 篇 艺术学
4 篇 哲学
4 篇 历史学

主题

11,252 篇 cloud computing
11,090 篇 distributed comp...
6,068 篇 computational mo...
5,958 篇 computer archite...
4,548 篇 computer science
4,384 篇 protocols
4,270 篇 application soft...
4,079 篇 computer network...
3,938 篇 peer to peer com...
3,794 篇 concurrent compu...
3,541 篇 resource managem...
3,475 篇 mobile computing
3,444 篇 distributed comp...
3,372 篇 control systems
3,327 篇 hardware
3,254 篇 servers
3,168 篇 costs
3,121 篇 grid computing
3,041 篇 distributed data...
2,855 篇 monitoring

机构

255 篇 institute of com...
166 篇 school of comput...
123 篇 department of co...
122 篇 university of ch...
87 篇 college of compu...
79 篇 ieee
76 篇 department of co...
63 篇 college of compu...
60 篇 school of automa...
58 篇 georgia inst tec...
56 篇 inria
56 篇 school of comput...
54 篇 shanghai jiao to...
53 篇 university of sc...
52 篇 huazhong univers...
52 篇 tsinghua univers...
52 篇 college of intel...
51 篇 school of comput...
50 篇 institute for in...
50 篇 distributed syst...

作者

118 篇 dustdar schahram
70 篇 hong-linh truong
58 篇 hai jin
54 篇 wang lei
48 篇 lei wang
45 篇 schahram dustdar
40 篇 k.g. shin
40 篇 duerr frank
38 篇 zhang lei
37 篇 buyya rajkumar
35 篇 xiao liu
33 篇 dhabaleswar k. p...
33 篇 chen shu-ching
32 篇 dan meng
31 篇 nectarios koziri...
31 篇 china
30 篇 wang wei
30 篇 jin hai
29 篇 li xin
27 篇 xi chen

语言

146,225 篇 英文
2,831 篇 其他
703 篇 中文
35 篇 俄文
13 篇 西班牙文
11 篇 葡萄牙文
9 篇 法文
5 篇 土耳其文
2 篇 乌克兰文

检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149804 条记录，以下是4621-4630 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：

Memristor Based Federated Learning for Network Security on the Edge using Processing in Memory (PIM) computing

Memristor Based Federated Learning for Network Security on t...

引用

ieee international conference on Fuzzy systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Alam, Shahanur Yakopcic, Chris Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Artificial Intelligence (AI) is moving towards the edge. Training an AI model for edge computing on a centralized server increases latency, and the privacy of edge users is jeopardized due to private data transfer through a less secure communication channels. Additionally, existing high-power computing systems are battling with memory and data transfer bottlenecks between the processor and memory. Federated Learning (FL) is a collaborative AI learning paradigm for distributed local devices that operates without transferring local data. Local participant devices share the updated network parameters with the central server instead of sending the original data. The central server updates the global AI model and deploys the model to the local clients. As the local data resides only on the edge, these devices need to be protected from cyberattacks. The Federated Intrusion Detection System (FIDS) could be a viable system to protect edge devices as opposed to a centralized protection system. However, on-device training of the model in resource constrained devices may suffer from excessive power drain, in addition to memory and area overhead. In this work we present a memristor based system for AI training on edge devices. Memristor devices are ideal candidates for processing in memory, as their dynamic resistance properties allow them to perform multiply-add operations in parallel in the analog domain with extreme efficiency. Alternatively, existing CMOS-based PIM systems are typically developed for edge inference based on pretrained weights, and are not equipped for on-chip training. We show the effectiveness of the system, where successful learning and recognition is achieved completely within edge devices. The classification accuracy of the memristor system shows negligible loss when compared a software implementation. To the best of our knowledge, this first demonstration of a memristor based federated learning system. We demonstrate the effectiveness of this system as

关键词： PIM memristor Federated Learning low power Intrusion Detection

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving distributed Dispatch for Integrated Electricity and Heat systems: A Multi-agent PPO Method 6

Privacy-Preserving Distributed Dispatch for Integrated Elect...

引用

6th international conference on Power and Energy Technology, ICPET 2024

作者： Xu, Guilei Wu, Qiuwei Lin, Zhenjia Chan, Wai Kin Victor Guo, Ye Li, Zepeng Tsinghua Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798350375855

Considering that energy suppliers within the Integrated Electricity and Heat System (IEHS) typically provide only partial information to other suppliers for privacy protection, traditional centralized scheduling methods are no longer applicable. This paper proposes a method based on multi-agent deep reinforcement learning to address the distributed dispatch problem within the IEHS. To transform the optimization problem of the IEHS into a reinforcement learning model in a multi-agent environment, the IEHS is divided according to different stakeholders into electrical system agents and heat system agents, thereby establishing a partially observable Markov decision process. Subsequently, the multi-agent proximal policy optimization algorithm is utilized to solve this problem, creating a training and solution framework suitable for the IEHS that supports centralized training and decentralized execution. © 2024 ieee.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics for Early Electrical Fire Detection in IoT Networks Using Random Forest 2

Predictive Analytics for Early Electrical Fire Detection in ...

引用

2nd ieee international conference on Contemporary computing and Communications, InC4 2024

作者： Raman, Ramakrishnan Rajesh, Shivani Symbiosis Institute of Business Management Pune & Symbiosis International Deemed University Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9798350383652

Ensuring safety against potential fire threats is of utmost significance due to the development of IoT devices in numerous areas. Most of the time, human involvement is required for traditional fire detection systems, which lack proactive capabilities. This research presents a new technique for electrical fire detection in Internet of Things (IoT) networks using predictive analytics, utilizing the Random Forest algorithm. To proactively detect and reduce fire hazards before they become catastrophic by using the abundance of data produced by IoT sensors. Data such as current measurements, temperature, humidity, and other relevant features may be retrieved in real-time via IoT devices that are integrated into electrical systems according to the suggested system. Random Forest classifier is taught to interpret and classified patterns that suggest potential fire dangers using data preparation, feature engineering, and supervised learning methods. Standard validation measures are used to verify the model's performance, providing its dependability and robustness. IoT network infrastructure deployment of the trained model allows for early identification of possible fire hazards and continuous monitoring. The system's capabilities can be including more sensors and advanced machine learning algorithms. By automating the monitoring process and enabling early detection, the suggested solution increases efficiency and safer and more robust IoT environment. © 2024 ieee.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Urban Traffic Forecasting with Scalable, Privacy-Preserving Federated Learning Approach 4

Enhancing Urban Traffic Forecasting with Scalable, Privacy-P...

引用

4th international conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Jiang, Zao The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798331528850

Accurate traffic forecasting is crucial for managing urban congestion and reducing delays. With the expansion of urban areas, traditional traffic prediction models increasingly struggle to adapt to the complexity and variability of modern traffic patterns. This study introduces a novel approach that leverages deep learning models trained through federated learning across distributed edge devices and cloud servers, such as traffic sensors, to predict traffic patterns. The use of federated learning techniques enhances prediction performance while preserving data privacy. To address the resource constraints and communication overhead associated with federated learning, techniques such as model compression, federated averaging, and selective participation are employed. The proposed approach provides a scalable, efficient, and privacy-preserving solution for accurate traffic predictions in smart cities, facilitating proactive traffic management and urban planning. © 2024 ieee.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Fault-tolerant Bayesian Decentralized Data Fusion Using Reliability Variables and Mixture Models 27

Fault-tolerant Bayesian Decentralized Data Fusion Using Reli...

引用

27th international conference on Information Fusion (FUSION)

作者： Shin, Changkyo Dagan, Ofer Ahmed, Nisar Choi, Han-Lim Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon South Korea Univ Colorado Boulder Smead Aerosp Engn Sci Dept Boulder CO USA

ISBN: (纸本)9798350371420;9781737749769

In uncertain and dynamic environments, decentralized data fusion (DDF) techniques have been widely used to estimate the states and the uncertainty levels over large mission spaces in a robust and scalable way. In data fusion frameworks using distributed sensor networks, undetected sensor failures can degrade the quality of fusion results of the entire system. Therefore, DDF methods which are robust to inconsistent data are needed. In this paper, a fault-tolerant Bayesian DDF method using Gaussian mixture models is developed. The probability of agent reliability states, which represent consistency of local estimates that agents share with their neighbors, are modeled as weights of mixture models and estimated together with the target process. The target process and reliability states are updated in a decentralized Bayesian way, exploiting the properties of Gaussian mixture models. To prevent the hypothesis explosion problem of Gaussian mixture models, a mixture compression method considering the physical meaning of mixture weights is utilized. A numerical simulation on a 2D dynamic target tracking problem is presented to verify performance of the suggested algorithm and compared with existing DDF methods. It is shown that the suggested algorithm gives more compact fusion results compared to existing fault-tolerant DDF method.

关键词： State estimation distributed inference Multirobot systems distributed sensor network Fault-tolerant Bayesian DDF

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 2

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

引用

2nd ieee international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Vidhyasagar, B.S. Arvindhan, M. Arunnehru, J. Anwarbasha, H. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Amity School of Engineering & Technology Madhya Pradesh Gwalior India Srm Institute of Science and Technology Chennai India Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798350390179

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 platforms

来源：评论

学校读者我要写书评

暂无评论

Fast distributed Memory Parallel Algorithms for Finding Connected Components in Large Graphs

Fast Distributed Memory Parallel Algorithms for Finding Conn...

引用

2024 ieee international conference on Big Data, BigData 2024

作者： Khan, Maleq Boddu, Sharon Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350362480

Finding the connected components in a graph is a fundamental problem in graph theory and network science. A connected component in a graph is a maximal set of vertices such that there is a path between any two vertices in the component. In a serial setting, connected components can be efficiently computed by running a breadth-first search (BFS). However, BFS-based algorithms do not lead to good parallelization. At the early stage, parallel algorithms for the connected component problem were developed for the PRAM model and shared-memory systems. With the availability of big data and big graphs and the development of a new area called network science, there is a renewed interest in developing parallel algorithms for this problem. In recent years, a number of distributed-memory parallel algorithms have been developed. These algorithms are based on mainly Shiloach and Vishkin's PRAM algorithm, called the SV algorithm, or some variants of SV. In this paper, we present a PRAM algorithm, called SELP, which is elegant and significantly simpler than the SV algorithm. Based on SELP, we develop a distributed-memory parallel algorithm, called DSELP. In addition, we incorporate some novel techniques to minimize communications among the processors. Experimental results show that our algorithms outperform the recent state-of-the-art distributed-memory algorithms significantly on a wide variety of graphs, and scale very well to a large number of processors. © 2024 ieee.

关键词： Consensus algorithm

来源：评论

学校读者我要写书评

暂无评论

Substation Demand Reduction using Combined Approach of CVR, VAr Optimization and distributed Generation 2

Substation Demand Reduction using Combined Approach of CVR, ...

引用

2nd ieee international conference on Advances in Electrical, computing, Communication and Sustainable Technologies (ieee ICAECT)

作者： Lakra, Neha Smitha Bag, Baidyanath NIT Raipur Dept Elect Engn Raipur Madhya Pradesh India

ISBN: (纸本)9781665411202

This study proposed, a combined scheme of conservation voltage reduction (CVR), VAr optimization (VO), and distributed generation (DG) to lower apparent substation demand. This research also seeks to optimize the DG placement problem during the implementation of CVR and OCP. The optimal allocation of shunt capacitor and DG has been identified using the grey wolf optimization (GWO) method. The proposed approach is illustrated on the modified ieee-33 feeder system. To show the benefits of proposed approach, four different modes such as normal operation, CVR with VO, CVR with DG, and CVR with VO and DG have been carried out. Simulation results reveal that operating CVR with DG and VO has achieved a higher reduction in apparent substation demand in comparison with the remaining modes.

关键词： Conservation Voltage Reduction (CVR) VAr optimization (VO) distributed generation (DG) apparent substation demand (ASD) Grey wolf optimization (GWO)

来源：评论

学校读者我要写书评

暂无评论

A Complete Piping Identification Solution for Piping and Instrumentation Diagrams 25

A Complete Piping Identification Solution for Piping and Ins...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Shengyong Li, Zhongtao Zhao, Shuai Yang, Lei Zhao, Fu Ge, Chuan University of Jinan School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Jinan Housing and Urban-Rural Development Bureau Jinan China The Shandong Cyberspace Affairs Commission Jinan China Shandong Xiaoya Holding Group Co. Ltd Jinan China Shandong University Jinan China

ISBN: (纸本)9798350330014

This study proposes a pipeline identification method for identifying pipelines in Piping and Instrumentation Diagrams (P&IDs) in image format. Automating this process is an important issue for the process plant industry, as image P &IDs are currently identified manually by humans. As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, this study proposes a method combining deep learning and traditional image detection to identify various types of pipelines in image format P&ID. The proposed method includes image pipeline extraction, pipeline simulation data augmentation, pipeline identification and fusion. For multiple test P &IDs, using yolov8 based on split coordinate spatial attention and augmented with simulated data, the average precision and recall are 90.5 % and 88.5 %, respectively. Combined with LSD line detection, it takes an average of 100 seconds to recognize a complete image, and the overall pipeline recognition rate can reach 95.5 %. © 2023 ieee.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Channel Estimation and Receiver Design for URLLC in distributed MIMO-NOMA systems Uplink

Channel Estimation and Receiver Design for URLLC in Distribu...

引用

2023 ieee/CIC international conference on Communications in China, ICCC 2023

作者： Han, Shihao Zhu, Pengcheng Li, Jiamin Wang, Yan Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

ISBN: (纸本)9798350345384

distributed multiple-input multiple-output non-orthogonal multiple access (D-MIMO-NOMA) is a potential technology for ultra-reliable low-latency communication (URLLC). In this paper, we propose a pilot scheme and derive a minimum mean squared error (MMSE) channel estimation algorithm in detail. Subsequently, a MMSE receiver for distributed MIMO-NOMA systems is designed by considering the channel estimation error and interference channel information, and the simulation results prove that the proposed receiver has better performance than the conventional MMSE receiver. Meanwhile, it is proved that there is an optimal pilot power allocation in any specific environment to minimize the mean squared error (MSE) of the proposed receiver. © 2023 ieee.

关键词： Channel estimation

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 << < 459 460 461 462 463 464 465 466 467 468 > >>

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：