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

18,624 篇 会议
113 篇 期刊文献
16 册 图书

馆藏范围

18,753 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

12,057 篇 工学
- 9,596 篇 计算机科学与技术...
- 5,512 篇 软件工程
- 3,082 篇 电气工程
- 2,954 篇 控制科学与工程
- 2,531 篇 信息与通信工程
- 963 篇 仪器科学与技术
- 941 篇 交通运输工程
- 934 篇 机械工程
- 643 篇 生物工程
- 632 篇 电子科学与技术（可...
- 561 篇 动力工程及工程热...
- 541 篇 生物医学工程（可授...
- 491 篇 安全科学与工程
- 395 篇 石油与天然气工程
- 394 篇 网络空间安全
- 259 篇 光学工程
3,012 篇 管理学
- 2,264 篇 管理科学与工程(可...
- 1,139 篇 图书情报与档案管...
- 868 篇 工商管理
2,970 篇 理学
- 1,521 篇 数学
- 791 篇 生物学
- 506 篇 统计学（可授理学、...
- 491 篇 物理学
- 398 篇 化学
- 393 篇 系统科学
622 篇 医学
- 509 篇 临床医学
- 444 篇 基础医学(可授医学...
- 264 篇 公共卫生与预防医...
333 篇 法学
- 273 篇 社会学
279 篇 经济学
- 274 篇 应用经济学
173 篇 教育学
150 篇 农学
33 篇 文学
31 篇 军事学
12 篇 艺术学

主题

1,144 篇 data acquisition
762 篇 data mining
572 篇 real-time system...
567 篇 data models
444 篇 intelligent syst...
420 篇 conferences
420 篇 big data
407 篇 cloud computing
375 篇 internet of thin...
361 篇 computational mo...
346 篇 monitoring
321 篇 computer archite...
319 篇 sensor systems
308 篇 machine learning
306 篇 control systems
304 篇 deep learning
304 篇 training
299 篇 feature extracti...
287 篇 robot sensing sy...
284 篇 neural networks

机构

49 篇 university of ch...
42 篇 lviv polytechnic...
30 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 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...
12 篇 yeshwantrao chav...

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
13 篇 sachenko anatoli...
12 篇 xinyu wu
12 篇 li yang
12 篇 yang yang
12 篇 wang wei
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

语言

17,862 篇 英文
824 篇 其他
83 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 18753 条记录，以下是1011-1020 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：

Design and Implementation of an intelligent Machine Learning System Based on Artificial Intelligence computing 2

Design and Implementation of an Intelligent Machine Learning...

引用

2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Cao, Yanqin Wuhan Institute of Design and Sciences Hubei Province Wuhan City430000 China

ISBN: (纸本)9798350339765

In the field of artificial intelligence, its applications have penetrated into daily life, such as smart home appliances, navigation, and medical treatment. This article introduces the design and implementation of an intelligent machine learning system based on artificial intelligence computing. This system utilizes artificial intelligence algorithms to analyze and process a large amount of data, thereby achieving automated learning and prediction of data. At the same time, the system also has good scalability and maintainability, which can adapt to the needs of different fields and application scenarios. The experimental results show that among the four algorithms, their accuracy has reached over 80%, and the time taken is within 3 seconds. The design and implementation of this system provides an effective solution for intelligent data analysis and decision-making. © 2023 ieee.

关键词： intelligent systems

来源：评论

学校读者我要写书评

暂无评论

IoT Based intelligent Helmet for Miners 6

IoT Based Intelligent Helmet for Miners

引用

6th ieee international conference on Recent Advances in intelligent Computational systems, RAICS 2024

作者： George, Elias M Joseph, Angel FaheemaFahida, M.C. George, Leya Anu Mar Athanasius College of Engineering Department of Electronics and Communication Kerala Kothamangalam India

ISBN: (纸本)9798350381689

Utilizing Internet of Things (IoT) technology in the mining sector has the potential to transform safety protocols for workers, especially those from low-income backgrounds who may lack awareness of their rights. Given the substantial impact of the mining industry on a country's GDP, addressing safety concerns is urgent. The miner's helmet, a crucial element of their uniform, is essential for preventing head injuries in hazardous mining environments. Unfortunately, current practices often neglect worker well-being due to a lack of awareness. To tackle this issue, proposing the integration of diverse sensors into the miner's helmet takes a proactive stance. These sensors can monitor vital signs in real-time, providing crucial health data. Simultaneously, they can assess environmental conditions in mines, offering insights into potential safety risks. Additionally, incorporating a location-tracking solution ensures continuous monitoring of a miner's whereabouts, crucial for swift responses in emergencies. This approach reduces response times significantly, aiding in mitigating the impact of potential disasters. Embracing these technological advancements not only enhances worker safety but also showcases the mining industry's commitment to ethical practices and social responsibility. © 2024 ieee.

关键词： Miners

来源：评论

学校读者我要写书评

暂无评论

Enhanced Implementation of intelligent Transportation systems (ITS) based on Machine Learning Approaches 20

Enhanced Implementation of Intelligent Transportation System...

引用

20th ACS/ieee international conference on Computer systems and Applications (AICCSA)

作者： Radi, Wafaa El Badawy, Hesham M. Canadian Int Coll CIC Dept Commun & Elect Engn Cairo Egypt Natl Telecommun Inst Network Planning Dept Cairo Egypt

ISBN: (纸本)9798350319439

intelligent Transportation systems (ITS) present a range of challenges that need to be addressed to ensure successful deployment, seamless integration, robust interconnection, and robust security. These challenges arise from the complex nature of ITS, the multitude of stakeholders involved, and the need to balance technological advancements with privacy and security concerns. Vehicle trajectory prediction is a critical task for improving traffic management and safety. It can be used to develop advanced traffic control systems, enhance autonomous vehicle navigation, and support real-world applications in intelligent transportation systems. This paper presents a detailed comparison and study on predicting vehicle trajectories using the NGSIM dataset, a publicly available dataset of high-quality vehicle trajectory data. A practical data set had been used to extract the main features of ITS, then we implemented two machine learning (ML) models for vehicle trajectory prediction by applying classification and regression tasks of both machine learning models, Random Forest (RF) and Support Vector Machine (SVM). We evaluated the models using classification accuracy, precision, recall, F1-score, mean squared error (MSE), and R-squared. The results show that both models can predict vehicle velocity and type with reasonable accuracy. However, the random forest outperformed the SVM on the regression task, while the SVM outperformed the random forest on the classification task. We also studied key factors that influence the performance of vehicle trajectory prediction models and proposed suggestions for improving their performance.

关键词： intelligent Transportation systems (ITS) machine learning Support Vector Regression (SVR) Support Vector Classifier (SVC) Random Forest Regressor Random Forest Classifier

来源：评论

学校读者我要写书评

暂无评论

BuildEnVR: An Immersive Analysis System for Environmental Field 30

BuildEnVR: An Immersive Analysis System for Environmental Fi...

引用

30th ieee international conference on Parallel and Distributed systems, ICPADS 2024

作者： Zhou, Zhenghan Liu, Kebin Xie, Yantong Jin, Hangxu Liu, Shi Wang, Ruiqing Zhao, Haitian Lin, Borong Mu, Xiaofang Qi, Hui Tsinghua University Global Innovation Exchange China Fuzhou Fuyao Institute for Advanced Study China Carnegie Mellon University School of Architecture United States Tsinghua University School of Architecture China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Amidst global warming and escalating extreme weather events, indoor environmental quality's impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR's intuitiveness and precision, and it is suitable for both experts and general users. © 2024 ieee.

关键词： Indoor air pollution

来源：评论

学校读者我要写书评

暂无评论

Research on Fault Prediction Method for Cloud Environment Based on Information Intelligence Fusion 4

Research on Fault Prediction Method for Cloud Environment Ba...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Li, Zhihong Feng, Zhipeng Sun, Jiangang Shao, Danni Bai, Dongxia Sun, Jinyi Information and Communication Branch State Grid Corporation Limited Beijing China

ISBN: (纸本)9798331534622

With the rapid development of cloud computing technology, the scarcity of single information acquisition in the event of cloud environment failure leads to the failure of fault discrimination. o solve the above problems, an intelligent fusion technique of information acquisition is proposed. The online operation mechanism of this technique is based on a priori knowledge and deep Boltzmann machine (DBM) model. After classifying and processing multi-source information, dynamic data and static data are acquired. The feature sets of different categories of data are extracted by data-driven extraction, and the Kalman filtering algorithm is used to remove the redundant features in the feature sets and complete the consistent feature description of the information, so as to obtain the information fusion results. The test results show that: the technology has good application performance;the test results of Davidson Boulding Index (DBI) are below 0.017, which can effectively deal with the abnormal data in dynamic data and static data;the results of coefficient of variation are below 0.02. The information fusion results obtained by using this technique can reliably predict the information in the fault state, and the application of this technique is effective. © 2024 ieee.

关键词： data consistency

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Security with Asynchronous Federated Learning for Seismic Inversion 59

Enhancing IoT Security with Asynchronous Federated Learning ...

引用

59th Annual ieee international conference on Communications (ieee ICC)

作者： Manu, Daniel Lin, Youzuo Yao, Jingjing Li, Zhirun Sun, Xiang Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Univ N Carolina Sch Data Sci & Soc Chapel Hill NC 27599 USA Los Alamos Natl Lab Los Alamos NM USA Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9798350304060;9798350304053

Seismic data/images are critical to understand the structure of subsurfaces. However, for accurate structural analysis, seismic images need to be converted into velocity images that can recognize the depth and thickness of subsurface layers, and this conversion is usually achieved by using seismic full-waveform inversion (FWI). Various Deep neural networks (DNNs) have recently been proposed to replace FWI, and well-trained DNNs typically have lower computational costs but generate similar velocity images as compared to FWI. Yet, training a DNN model is non-trivial, which requires transmitting seismic data that are sensed by seismic receivers in the field to a centralized data center, leading to data privacy and security issues. Seismic field tests are normally conducted in rural areas, where data centers and Internet infrastructures are not available. Hence, it is impossible to train a DNN and achieve seismic inversion in real-time. In this paper, we propose the Asynchronous Federated Learning for Seismic Inversion (AsyncFedInv) framework, which applies multiple IoT devices in terms of edge computing boards to collaboratively train a compact UNet model in real-time based on a novel asynchronous federated learning, where 1) a staleness function is applied to mitigate model staleness, and 2) clients that generate similar local models would suspend its training, thus reducing the communication costs and energy consumption. Simulation results demonstrate that AsyncFedInv achieves a similar convergence rate but lower training loss and better testing performance as compared to a baseline algorithm FedAvg.

关键词： Seismic inversion federated learning asynchronous

来源：评论

学校读者我要写书评

暂无评论

Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device 31

Evaluating Multi-Instance DNN Inferencing on Multiple Accele...

引用

31st ieee international conference on High Performance computing, data, and Analytics Workshops, HiPCW 2024

作者： Tayal, Mumuksh Simmhan, Yogesh Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bangalore560012 India Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

Edge devices like Nvidia Jetson have started to have multiple on-board accelerators such as GPU CUDA cores, Tensor Cores and Deep Learning Accelerators (DLA). Maximizing the DNN inferencing performance of such devices requires us to concurrently use these col-located hardware components, but this has not yet been studied. We analyze the performance of accelerators present in Jetson AGX Orin, both independently and concurrently, using multiple instances of ResNet50 model. We assess the effects of using different combinations of the components and varying batch sizes on the inference throughput and latency. Our results indicate that using CUDA Core with Tensor Cores offers a higher throughput, while using them in conjunction with DLAs reduces the benefits. This paves the way to explore more intelligent configurations to maximize the performance of edge platforms for AI workloads. © 2024 ieee

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Malware Detection By Distributed systems with Partial Centralization

Malware Detection By Distributed Systems with Partial Centra...

引用

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

作者： Bohdan Savenko Antonina Kashtalian Sergii Lysenko Oleg Savenko Khmelnytskyi National University Khmelnytskyi Ukraine

The paper proposes approach to use new tools for malware detection in corporate networks, which are distributed systems with partial centralization. To make decision about malware presence, the components which includes specified decision-making for the system's center are defined as a decentralized subsystem. To determine the states of the system components, characteristic indicators are proposed, and generalized analytical expressions for their calculation are developed. Such calculations make it possible to assess the state of the components in the system in order to determine its further steps. As a result, the system is the basis for usage of different malware detection methods in combination with the system components as an integral sensor. To test the system, a worm-virus detection method was implemented and experiments were conducted. The results of experimental studies approved the efficiency of the proposed solution.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on intelligent adaptive optimization model system of computing system based on reinforcement learning 4

Research on intelligent adaptive optimization model system o...

引用

4th ieee international conference on data Science and Computer Application, ICDSCA 2024

作者： Ma, Jun Sun, Qian Chengdu Neusoft University Sichuan Chengdu611844 China PowerChina Sichuan Electric Power Engineering Co. Ltd. Sichuan Chengdu610041 China

ISBN: (纸本)9798350368239

This paper achieves the dynamic optimization of system performance through the application of Q-learning and deep reinforcement learning (DQN). In traditional systems, decision-making mechanisms often lack self-adjusting capabilities for various requirements. By using adaptive optimization techniques, the system can make real-time adjustments according to the feedback from the operating environment, thus optimizing the decision-making process. The system is mainly composed of two parts: algorithm design and model simulation. Simulation results indicate that in multiple test scenarios, this system significantly enhances the operational efficiency and performance. data analysis reveals that compared with traditional static mechanisms, the system's task completion rate has increased by 15% and the operation time has been reduced by 12%. Moreover, the universality of the adaptive optimization mechanism in different operating scenarios has been verified through precise analysis of simulation data. In conclusion, this study demonstrates that the adaptive system optimization based on reinforcement learning can not only remarkably improve the performance of individualized decision-making, but also provide effective theoretical support and technical reference for the design of future intelligent systems. ©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

Compressed Sensing for Full Matrix Capture data Based on Optimal Reconstruction Algorithm 8

Compressed Sensing for Full Matrix Capture Data Based on Opt...

引用

8th international conference on intelligent computing and Signal Processing, ICSP 2023

作者： Zhao, Zhiyuan Teng, Da Liu, Lishuai Xiang, Yanxun East China University of Science and Technology Shanghai Key Laboratory of Intelligent Sensing and Detection Technology Shanghai China

ISBN: (纸本)9798350302455

Ultrasonic phased array total focusing method (TFM) attracts much interest because of high image resolution and large dynamic range. However, the data acquisition method relied on by TFM is full matrix capture (FMC), which generates huge data and brings great pressure. In this paper, the feasibility of compressed sensing (CS) in the reconstruction of ultrasonic FMC data is investigated. Firstly, four greedy algorithms are used to compress and reconstruct the ultrasonic signal, and the optimal algorithm is selected according to the reconstruction accuracy and the influence of sampling rate is considered. Secondly, the optimal algorithm is applied to the reconstruction of undersampled FMC data, and TFM is performed based on reconstructed FMC data. The simulation results demonstrate that excellent reconstruction can be obtained using measurement points less than the Nyquist sampling limitation required, the spatial resolution of TFM imaging of FMC data reconstructed with CS remained unchanged. Finally, experimental verification is carried out. Although the experimental results are not as ideal as the simulation results, FMC data can still be accurately recovered with a small number of measurements and the identification of defects by TFM is guaranteed. © 2023 ieee.

关键词： data acquisition

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 << < 98 99 100 101 102 103 104 105 106 107 > >>

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：