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

1,472 篇 会议
892 篇 期刊文献
40 册 图书

馆藏范围

2,404 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,480 篇 工学
- 838 篇 计算机科学与技术...
- 639 篇 软件工程
- 231 篇 信息与通信工程
- 171 篇 控制科学与工程
- 148 篇 电气工程
- 146 篇 仪器科学与技术
- 139 篇 电子科学与技术（可...
- 136 篇 生物工程
- 112 篇 动力工程及工程热...
- 103 篇 机械工程
- 103 篇 土木工程
- 103 篇 生物医学工程（可授...
- 98 篇 核科学与技术
- 84 篇 建筑学
- 82 篇 光学工程
- 75 篇 化学工程与技术
- 53 篇 航空宇航科学与技...
940 篇 理学
- 365 篇 数学
- 351 篇 物理学
- 160 篇 生物学
- 125 篇 地球物理学
- 118 篇 统计学（可授理学、...
- 95 篇 化学
- 79 篇 系统科学
332 篇 管理学
- 225 篇 管理科学与工程(可...
- 133 篇 工商管理
- 115 篇 图书情报与档案管...
98 篇 医学
- 92 篇 临床医学
- 73 篇 基础医学(可授医学...
56 篇 法学
- 50 篇 社会学
49 篇 经济学
32 篇 农学
30 篇 教育学
7 篇 艺术学
5 篇 军事学
3 篇 文学
2 篇 哲学
1 篇 历史学

主题

151 篇 computer archite...
106 篇 hadron colliders
55 篇 hardware
45 篇 artificial intel...
44 篇 computer science
43 篇 application soft...
42 篇 computational mo...
32 篇 optimization
31 篇 deep learning
31 篇 neutrons
31 篇 delay
29 篇 laboratories
28 篇 w & z bosons
27 篇 parallel process...
27 篇 genetic algorith...
27 篇 feature extracti...
26 篇 electronic mail
26 篇 neural networks
26 篇 machine learning
26 篇 protocols

机构

150 篇 centro brasileir...
149 篇 national and kap...
149 篇 istanbul technic...
149 篇 yerevan physics ...
148 篇 university of sp...
147 篇 universidad iber...
147 篇 indian institute...
147 篇 charles universi...
145 篇 brunel universit...
144 篇 academy of scien...
141 篇 university of de...
140 篇 imperial college...
140 篇 rwth aachen univ...
138 篇 università di to...
136 篇 department of ph...
135 篇 infn sezione di ...
133 篇 zhejiang univers...
132 篇 université de st...
131 篇 department of ph...
127 篇 indian institute...

作者

146 篇 ardid m.
132 篇 ferrara g.
114 篇 coleiro a.
113 篇 kalekin o.
113 篇 díaz a.f.
110 篇 g. tonelli
110 篇 d. barney
110 篇 l. valencia palo...
110 篇 enzenhöfer a.
109 篇 t. liu
109 篇 c. hensel
109 篇 a. garcia-bellid...
109 篇 a. steen
109 篇 n. cartiglia
109 篇 n. colino
109 篇 f. moortgat
109 篇 b. clerbaux
109 篇 g. sultanov
109 篇 celli s.
109 篇 brunner j.

语言

2,193 篇 英文
175 篇 其他
36 篇 中文
2 篇 斯洛文尼亚文
1 篇 法文
1 篇 土耳其文

检索条件"机构=the Department of Computer Architecture and Technology"

共 2404 条记录，以下是291-300 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：

Development of Reflex technology of Action Identification in Project Planning Systems

Development of Reflex Technology of Action Identification in...

引用

2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Teslia, Iurii Yehorchenkova, Nataliia Khlevna, Iulia Yehorchenkov, Oleksii Kataieva, Yevheniia Klevanna, Ganna Cherkasy State Technological University Department Of Information Technology Project Cherkasy Ukraine Kyiv National University Of Construction And Architecture Department Of Project Management Kyiv Ukraine Taras Shevchenko National University Of Kyiv Department Of Technology Management Kyiv Ukraine Cherkasy State Technological University Department Of Automated Systems Software Cherkasy Ukraine Cherkasy State Technological University Department Of Computer Science And Systems Analysis Cherkasy Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The need for intellectualization of project management processes is shown, in particular, the creation of intelligent systems capable of effectively plan projects in the process of natural-language interaction with the project team. The analysis of existing theoretical and practical developments in this field is carried out. It is shown that the issue of intellectualization of the project planning process is still open. It is proposed to use a reflex approach to building an intelligent project planning system. The technology of reflex identification of actions in project planning systems, based on the model of non-force (information) interaction, is proposed. Scenarios of actions have been developed and their distribution has been performed in accordance with the content of natural language requests to the project planning system. Experimental studies are shown a high probability of correct recognition of actions in project planning systems from natural language requests of project team members. The results of the conducted experiments unequivocally testify to the effectiveness of the developed information technology of reflex identification of action scenarios in project planning systems. © 2022 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

引用

International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Timing System for IFMIF-DONES Facility Based on Ethernet Time Transfer Protocols

An Efficient Timing System for IFMIF-DONES Facility Based on...

引用

2022 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium (EFTF/IFCS)

作者： Carlos Megí as Ví ctor Vá zquez Eduardo Ros Javier Dí az Mauro Cappelli Department of Computer Architecture and Technology Universidad de Granada Granada Spain Department of Fusion and Nuclear Safety ENEA Rome Italy

ISBN: (数字)9781665497183

ISBN: (纸本)9781665497190

This work analyses the synchronization requirements of the IFMIF-DONES infrastructure systems and proposes a cost-effective solution based on standard protocols: Network Time Protocol (NTP) and IEEE-1588 profiles, which are compliant with Ethernet networks. This approach simplifies the deployment of the timing system and its integration in already existing networks, being able to provide high scalability in the number of devices and limiting the number of time-aware devices or independent timing networks in the solution. The proposal takes full advantage of the capabilities of White-Rabbit technology to disseminate low phase noise frequency over the network without requiring wired radiofrequency solutions. Final considerations are made to understand the simplicity and viability of this solution.

关键词： Radio frequency Phase noise Time-frequency analysis Protocols Scalability Ethernet Synchronization

来源：评论

学校读者我要写书评

暂无评论

CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ALEXNET

CONSTRUCTION PROGRESS MONITORING IN SMART CITIES USING DEEP ...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Rajavel, A. Karkuzhali, S. Sharma, Sarvdaman Singh, Sitesh Kumar Senthilkumar, P. Ida, S. Jhansi Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar India Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Vaddeswaram Greenfield AP Guntur India Department of Computer Science and Business Systems R.M.K Engineering College India

ISBN: (纸本)9798350305708

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle. © 2023 IEEE.

关键词： Construction progress monitoring Deep AlexNet Deep learning Infrastructure projects Smart cities

来源：评论

学校读者我要写书评

暂无评论

Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

引用

IEEE International Conference on Networking, Sensing and Control

作者： Qingyan Yin Wangwang Chen Ruiping Wu Zhi Wei School of Science Xi'an University of Architecture and Technology Xi'an China Department of Computer Science New Jersey Institute of Technology Newark USA

ISBN: (纸本)9781665472449

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.

关键词： Analytical models Biological system modeling Computational modeling Genomics Data models Gene expression Bioinformatics

来源：评论

学校读者我要写书评

暂无评论

AgileWatts: An Energy-Efficient CPU Core Idle-State architecture for Latency-Sensitive Server Applications 22

AgileWatts: An Energy-Efficient CPU Core Idle-State Architec...

引用

Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Jawad Haj Yahya Haris Volos Davide B. Bartolini Georgia Antoniou Jeremie S. Kim Zhe Wang Kleovoulos Kalaitzidis Tom Rollet Zhirui Chen Ye Geng Onur Mutlu Yiannakis Sazeides Rivos Inc Mountain View California United States Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781665462723

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30--250μs). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100μs) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive *** drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of saving/restoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) retaining context in the power-ungated domain. Second, AW eliminates the flush latency (several tens of microseconds) of the L1/L2 caches when entering a deep idle state by keeping L1/L2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core's voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power *** evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumption of Memcached by up to 71% (35% on average) with <1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Ro...

引用

International Conference on Advanced Robotics (ICAR)

作者： Nikolaos Stathoulopoulos Emanuele Pagliari Luca Davoli George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Department of Engineering and Architecture Internet of Things (IoT) Lab University of Parma Parma Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USING YOLOV3

ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USIN...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Archana, U. Sharma, Sarvdaman Singh, Sitesh Kumar Sureshkumar, R. Senthilkumar, P. Harish Babu, T. Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Oromia Nekemte Ethiopia Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India KL School of Architecture Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Andhra Pradesh Guntur India Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamilnadu India

ISBN: (纸本)9798350305708

This study focuses on the application of the YOLOv3 (You Only Look Once version 3) object detection algorithm for the analysis of concrete cracks and fatigue within Smart Cities. Concrete infrastructure plays a crucial role in urban environments, and its structural integrity is paramount for ensuring public safety and sustainable development. The proposed approach utilizes YOLOv3 to identify and localize instances of concrete cracks and signs of fatigue in images and videos captured from various urban settings. By leveraging deep learning techniques, this research aims to enhance the efficiency and accuracy of monitoring and maintaining concrete structures, contributing to the overall resilience and longevity of Smart Cities. The findings of this study present valuable insights into the potential of utilizing advanced computer vision methods for proactive infrastructure management in the urban landscape. © 2023 IEEE.

关键词： Concrete cracks Fatigue analysis Infrastructure monitoring Smart Cities YOLOv3

来源：评论

学校读者我要写书评

暂无评论

Recurrent Neural Networks and Efficiency in High-Dimensional EEG Classification 1st

Recurrent Neural Networks and Efficiency in High-Dimensional...

引用

1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： León, Javier Escobar, Juan José González, Jesús Ortega, Julio Arrabal-Campos, Francisco Manuel Ortiz, Andrés Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Granada Spain Department of Engineering University of Almería Almería Spain Department of Communications Engineering University of Málaga Málaga Spain

ISBN: (纸本)9783030881627

The performance of neural networks has granted deep learning a place at the forefront of machine learning in the last decade. Although these models are computationally intensive, their advantage is recognized in a wide array of applications. Nonetheless, the large amount of learnable parameters in neural networks can be a disadvantage for small and high-dimensional datasets. In this context, the relationship between solution quality and model complexity gains significance due to overfitting concerns. This is precisely the case of Electroencephalography (EEG), where the data consists in relatively few patterns obtained from multiple electrode readings. In this paper, we take a look at two widely-known types of Recurrent Neural Networks (RNNs), namely Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), which are evaluated on three EEG Motor Imagery (MI) datasets by measuring not only classification accuracy, but also time and energy consumption. Although the results are not entirely competitive with the state-of-the-art in the datasets considered, the interaction that occurs between the size of the networks and their accuracy provides us with valuable insight about network structure design in the context of the curse of dimensionality. © 2021, Springer Nature Switzerland AG.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Train with Automatic Functioning based on IoT with Solar Energy Source

A Train with Automatic Functioning based on IoT with Solar E...

引用

2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Padmakala, S. Alkawak, Omar A. Reddy, G. Vidya Sagar Narendranathan, S.K. Muthuraman, M.S. Sendilvelan, S. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Al-Mustaqbal University College Department of Air Conditioning and Refrigeration Technical Engineering Babil Hilla Iraq School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Agni College of Technology Department of Mechanical Engineering Tamil Nadu Chennai India Psna College of Engineering and Technology Department of Mathematics Tamil Nadu Dindigul India Dr. M.G.R Educational and Research Institute Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665474511

The railway industry is one of the most sustainable modes of transport. To reduce the carbon footprints, renewable energy sources are initiated. The proposal of attaching solar panels to railway sleepers, smart garbage bin, lighting, automatic closing and opening of doors that are accompanied with real time monitoring system with multiple technologies. Among the multiple technology the Internet of Things (IoT) plays a significant role by sending information and monitoring the overall functions. The solar energy obtained is used to generate electricity and further they are stored in battery for future usage. © 2023 IEEE.

关键词： Carbon footprint

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241页 << < 26 27 28 29 30 31 32 33 34 35 > >>

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：