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

5,056 篇 会议
44 篇 期刊文献
5 册 图书

馆藏范围

5,105 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,122 篇 工学
- 3,178 篇 计算机科学与技术...
- 1,609 篇 电气工程
- 1,551 篇 信息与通信工程
- 1,279 篇 软件工程
- 553 篇 控制科学与工程
- 344 篇 动力工程及工程热...
- 262 篇 仪器科学与技术
- 248 篇 电子科学与技术（可...
- 201 篇 机械工程
- 112 篇 测绘科学与技术
- 109 篇 安全科学与工程
- 104 篇 石油与天然气工程
- 104 篇 网络空间安全
- 91 篇 交通运输工程
- 77 篇 生物工程
- 76 篇 环境科学与工程（可...
- 75 篇 生物医学工程（可授...
- 74 篇 材料科学与工程（可...
- 64 篇 化学工程与技术
826 篇 理学
- 439 篇 数学
- 205 篇 物理学
- 123 篇 化学
- 123 篇 统计学（可授理学、...
- 102 篇 系统科学
- 99 篇 生物学
571 篇 管理学
- 439 篇 管理科学与工程(可...
- 199 篇 图书情报与档案管...
- 170 篇 工商管理
101 篇 医学
- 82 篇 临床医学
- 65 篇 基础医学(可授医学...
63 篇 经济学
52 篇 法学
40 篇 农学
25 篇 军事学
14 篇 教育学
6 篇 文学
3 篇 艺术学

主题

620 篇 wireless sensor ...
167 篇 sensor nodes
140 篇 sensor systems
137 篇 distributed comp...
122 篇 wireless sensor ...
95 篇 sensor networks
93 篇 distributed powe...
92 篇 internet of thin...
87 篇 energy efficienc...
85 篇 monitoring
81 篇 cloud computing
81 篇 distributed comp...
72 篇 computer network...
71 篇 sensors
68 篇 protocols
65 篇 routing
63 篇 security
59 篇 wireless communi...
59 篇 real-time system...
58 篇 computer archite...

机构

12 篇 maharishi school...
12 篇 jiangsu high tec...
12 篇 ieee
10 篇 college of elect...
9 篇 chitkara univers...
9 篇 chitkara univers...
8 篇 karnataka bangal...
8 篇 henan univ inst ...
8 篇 payame noor univ...
8 篇 wuhan univ sch c...
7 篇 beijing univ pos...
7 篇 tu wien distribu...
6 篇 dalian univ tech...
6 篇 college of techn...
6 篇 shanghai jiao to...
6 篇 centre for inter...
6 篇 univ pittsburgh ...
6 篇 indian sch mines...
6 篇 centre for cyber...
6 篇 chinese acad sci...

作者

14 篇 han zhijie
10 篇 dustdar schahram
10 篇 china
10 篇 zhang wei
9 篇 barolli leonard
8 篇 wang lei
8 篇 liu xue
7 篇 liu yang
6 篇 stankovic john a...
6 篇 xhafa fatos
6 篇 zakaria a.
6 篇 lin chi
6 篇 li wei
6 篇 abdelzaher tarek
6 篇 mukherjee nandin...
6 篇 li hui
5 篇 sarigiannidis pa...
5 篇 yang yang
5 篇 shakaff a.y.m.
5 篇 prasad ramjee

语言

5,015 篇 英文
83 篇 其他
7 篇 中文

检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

共 5105 条记录，以下是91-100 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：

Federated Learning and Cloud Integration for Enhanced Anomaly Detection in Wireless sensor Networks using LSTM Models and Raspberry Pi Node 3

Federated Learning and Cloud Integration for Enhanced Anomal...

引用

3rd ieee World conference on Applied Intelligence and computing, AIC 2024

作者： Gayathri, S. Surendran, D. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Sathyamangalam638401 India Karpagam College of Engineering Othakkal Mandapam Department of Information Technology Tamil Nadu Coimbatore641032 India

ISBN: (纸本)9798350384598

Wireless sensor Network (WSN) is increasingly utilized for monitoring and surveillance in various domains, including environmental monitoring, industrial automation, and healthcare. However, the sheer volume of data generated by WSN poses challenges for anomaly detection, especially in resource-constrained environments. This paper proposes a novel approach that combines federated learning and cloud integration to address the challenges of anomaly detection in WSN. Leveraging federated learning allows for collaborative model training across distributed sensor nodes while preserving data privacy. Cloud integration provides scalable computational resources for model aggregation and storage, enabling efficient anomaly detection. The implementation utilizes Long Short-Term Memory (LSTM) models, well-suited for capturing temporal dependencies in sensor data. Raspberry Pi-based WSN nodes are employed for their low-power consumption and flexibility, enabling deployment in diverse environments. Communication between sensor nodes and the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, ensuring efficient data transmission. Cloud integration is realized using Microsoft azure, offering robust cloud infrastructure for model aggregation and management. TensorFlow Federated (TFF) framework is utilized for federated learning, enabling collaborative model training across distributed nodes. Experimental evaluation demonstrates the effectiveness of the proposed approach, achieving significant improvements in anomaly detection accuracy and efficiency. Specifically, our approach achieves up to a 20% increase in detection accuracy compared to centralized approaches. Furthermore, integrating federated learning and cloud infrastructure results in a 25% reduction in processing time, enabling real-time anomaly detection capabilities. © 2024 ieee.

关键词： sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

引用

3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 ieee.

关键词： distributed ledger

来源：评论

学校读者我要写书评

暂无评论

Research on wide-area distributed PV regional power prediction technology 3

Research on wide-area distributed PV regional power predicti...

引用

3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Huan, Jiafei Zhang, Weitao Jiang, Jixuan Zhang, Liping Zhou, Ling Wu, Han State Grid North China Branch Dept Dispatching & Control Ctr Beijing Peoples R China State Power Rixin Technol Co Ltd Dept Power Grid Business Beijing Peoples R China State Power Rixin Technolog Co Ltd Dept Power Grid Business Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The power grid in China urgently needs to strengthen the monitoring of extensive distributed PV plants and improve power prediction accuracy. The traditional single-station prediction model for centralized PV plants is not suitable for wide-area distributed photovoltaic plants, which are characterized by decentralized installation and small capacity. This paper proposes third-generation regional prediction technology for distributed PV. This technology considers the distribution of installed capacity and geographical topography, thereby avoiding issues such as differences in power generation efficiency and errors from single selection points. It proposes building a regional prediction model based on various actual data inputs, using districts and counties as the basic units. To enhance the accuracy of regional predictions, the average daily root mean square error (RMSE) accuracy rate has reached more than 85%.

关键词： renewable energy distributed PV power prediction regional forecast model

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Framework for Sign Language Recognition Using Inception V3 with Transfer Learning 3

Deep Learning Framework for Sign Language Recognition Using ...

引用

3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kirubavathi, G. GuruAkshya, C. Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Sign language recognition poses unique difficulties due to the complexities of hand gestures and movements. Real-world applications often require robust models capable of handling diverse environmental conditions and variations in signing styles. Transfer learning, a technique that utilizes knowledge from pre-trained models, has emerged as a promising method to address these challenges. Although transfer learning has been successful in various visual recognition tasks, its application to American Sign Language (ASL) recognition remains relatively unexplored. This research showcases the effectiveness of combining transfer learning with advanced CNN architectures like Inception V3 for American Sign Language (ASL) recognition. Through fine-tuning Inception V3, adjusting learning rates, applying data augmentation, and rigorous evaluation, the model achieved an impressive 99.12% accuracy on test datasets. These findings represent a significant improvement in ASL recognition accuracy compared to baseline models. The success of this method underscores the importance of fine-tuning and data augmentation in advancing accessible and reliable sign language recognition technology for practical use. © 2024 ieee.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Research on Optimal Allocation of Integrated Resources of Mergers and Acquisitions Based on distributed Cloud computing 3

Research on Optimal Allocation of Integrated Resources of Me...

引用

3rd international Joint conference on Information and Communication Engineering (JCICE)

作者： Zhou, Lifen Guangdong Baiyun Univ Sch Accountancy Guangzhou Peoples R China

ISBN: (纸本)9798350388732;9798350388725

This paper proposes a scheduling algorithm based on fast edge resource placement, so as to further improve the effect of bilateral resource integration of the mergers and acquisitions (M&A). Among them, the random demand model is taken as the basic resource scheduling method, and the algorithm is optimized by introducing edge computing and user random demand, thereby further improving the resource scheduling effect of mergers and acquisitions. The simulation results show that under different merger and acquisition resources, Zipf values, and types of resources, compared with traditional mean-based scheduling algorithms, the scheduling algorithm based on fast edge resource placement have higher resource scheduling benefits and significantly reduced time complexity. Therefore, it can be concluded that the scheduling algorithm based on fast edge resource placement has good performance, and it can carry out resource scheduling with good effect. In addition, applying the proposed algorithm in the actual enterprise M&A scenario can help enterprises to carry out better resource management, which is feasible and has certain practical application value.

关键词： resource scheduling distributed structure cloud computing algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

Automated Mining Equipment Monitoring: Enhancing Efficiency through sensor Technology and Predictive Maintenance 3

Automated Mining Equipment Monitoring: Enhancing Efficiency ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Dharun Anand, S. Divakar, S. Cephas, I. Ganiss Roger, L. St. Joseph's Institute of Technology Department of Electrical and Electronics Engineering OMR Chennai India

ISBN: (纸本)9798331509675

The mining industry is pivotal to the economy, yet it faces significant challenges in maintaining equipment reliability and efficiency. Downtime of machinery can lead to substantial financial losses, operational delays, and safety risks. Mining operations are inherently complex, involving significant investments in machinery and equipment [1]. The efficiency and reliability of mining equipment are crucial for maintaining operational profitability and safety. With advancements in sensor technology and data analytics, automated equipment monitoring systems are becoming pivotal in enhancing operational effectiveness. This paper presents a comprehensive design for a system that utilizes sensors for real-time monitoring of mining equipment performance and health. By implementing predictive maintenance alerts, this system aims to mitigate downtime and enhance operational efficiency in mining operations [2]. © 2025 ieee.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Quality of Service in Wireless sensor Networks: A Comparison of Routing Protocols based on Machine Learning 3

Quality of Service in Wireless Sensor Networks: A Comparison...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Prabha, K. Lakshmi Vijayasri, P. Saumiya, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331509675

This paper focuses on the improvement of Quality of Service (QoS) in Wireless sensor Networks by using Machine Learning (ML) techniques to optimize routing protocols. It addresses the essential QoS challenges such as network lifespan, energy efficiency, delay minimization, and packet loss reduction using ML-based methods against those of conventional routing techniques. The study sheds light on how ML could be used to detect and overcome bottlenecks in the performance of a network to improve the overall efficiency. Simulation results show the massive improvements in QoS parameters, thereby strongly reaffirming the feasibility and potential of the ML-driven approaches to overcome the inherent limitations of traditional routing protocols in WSNs. © 2025 ieee.

关键词： Packet loss

来源：评论

学校读者我要写书评

暂无评论

Remaining-Capacity-Based distributed Control for Harmonic Sharing in Islanded Microgrids 3

Remaining-Capacity-Based Distributed Control for Harmonic Sh...

引用

3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Du, Haochen Huang, Lucong Zhou, Xiang Duan, Jiaqi Hao, Yuxin Yang, Hengchen Yang, Jiadong Wang, Yang Sichuan Univ Coll Elect Engn Chengdu Peoples R China State Grid Corp China State Grid Sichuan Elect Power Co Chengdu Peoples R China

ISBN: (纸本)9798350375145;9798350375138

One of the challenges encountered by islanded microgrids is voltage distortion, which is induced by non-linear loads. The unreasonable distribution of harmonic power among distributed inverters can lead to the overloading of inverters. Existing studies achieve harmonic power sharing based on fixed value, which lacks flexibility. Therefore, this paper proposes a distributed harmonic sharing strategy which is based on remaining capacity. The main objective of this strategy is to achieve harmonic power sharing according to the remaining capacity by means of a consensus algorithm. To achieve this, harmonic power and remaining capacity are calculated in real-time, and a consensus protocol is designed to guide the adjustment of virtual impedance. Additionally, a fuzzy controller is designed to adjust the speed of virtual impedance changes based on the operating state of each inverter to achieve better dynamic performance. Finally, comparative analysis and experimental verification were conducted.

关键词： microgrids harmonic sharing fuzzy control consensus protocol

来源：评论

学校读者我要写书评

暂无评论

Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 ieee.

关键词： distributed cloud

来源：评论

学校读者我要写书评

暂无评论

Enhanced Soil Nutrient-NPK Measurement Using Electrical Conductivity and Temperature 3

Enhanced Soil Nutrient-NPK Measurement Using Electrical Cond...

引用

3rd international conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Dattatreya, Sneha Sain, Manoj Khurana, Archit Jena, K. Chatterjee, Gaurav The Lnmiit Department of Ece Jaipur India

ISBN: (纸本)9798331528201

To satisfy the growing demand of population, opti-mizing the level of soil micronutrients, i.e., Nitrogen (N), Phosphorus (P), and Potassium (K) (known as NPK), plays a crucial role. However, the traditional methods for determining the soil nutrient level are accurate but time-consuming, expensive, and laborious. This study uses a deep learning approach to predict the soil NPK levels, holding electrical conductivity (EC) and temperature (T) as inputs and using a 7-in-1 soil sensor. A dataset was developed using different soil samples. A neural network model was developed to train and test the discovered dataset. The model achieved promising results with 99% accuracy with lower mean squared error (MSE) value, demonstrating rapid and accurate soil nutrient sensing potential. This research provides a cost-effective solution to model a soil sensor to streamline precision agriculture and optimize fertilization techniques. © 2024 ieee.

关键词： agriculture deep learning electrical conductivity Soil NPK sensor temperature

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 << < 6 7 8 9 10 11 12 13 14 15 > >>

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：