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

13,239 篇 会议
191 册 图书
158 篇 期刊文献

馆藏范围

13,587 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

9,037 篇 工学
- 7,539 篇 计算机科学与技术...
- 4,285 篇 软件工程
- 1,624 篇 信息与通信工程
- 1,528 篇 电气工程
- 1,231 篇 控制科学与工程
- 962 篇 生物工程
- 513 篇 机械工程
- 500 篇 生物医学工程（可授...
- 470 篇 电子科学与技术（可...
- 461 篇 动力工程及工程热...
- 326 篇 光学工程
- 308 篇 交通运输工程
- 294 篇 安全科学与工程
- 267 篇 材料科学与工程（可...
- 248 篇 化学工程与技术
3,513 篇 理学
- 2,202 篇 数学
- 1,058 篇 生物学
- 605 篇 物理学
- 582 篇 统计学（可授理学、...
- 472 篇 系统科学
- 241 篇 化学
1,861 篇 管理学
- 1,292 篇 管理科学与工程(可...
- 681 篇 图书情报与档案管...
- 542 篇 工商管理
595 篇 医学
- 497 篇 临床医学
- 406 篇 基础医学(可授医学...
- 223 篇 药学(可授医学、理...
306 篇 教育学
- 296 篇 教育学
251 篇 法学
- 228 篇 社会学
221 篇 经济学
- 219 篇 应用经济学
136 篇 农学
37 篇 军事学
21 篇 文学
6 篇 艺术学

主题

1,321 篇 optimization
1,090 篇 machine learning
1,031 篇 deep learning
506 篇 accuracy
462 篇 reinforcement le...
328 篇 computational mo...
313 篇 machine learning...
299 篇 training
279 篇 support vector m...
279 篇 feature extracti...
260 篇 predictive model...
248 篇 neural networks
241 篇 real-time system...
217 篇 genetic algorith...
207 篇 adversarial mach...
163 篇 particle swarm o...
157 篇 convolutional ne...
152 篇 learning algorit...
142 篇 internet of thin...
131 篇 data mining

机构

46 篇 chitkara centre ...
36 篇 chitkara univers...
30 篇 chitkara univers...
29 篇 the islamic univ...
24 篇 department of co...
18 篇 centre of resear...
18 篇 al-farahidi univ...
17 篇 lloyd institute ...
17 篇 department of co...
15 篇 school of comput...
14 篇 school of comput...
14 篇 gla university d...
13 篇 vishwakarma inst...
12 篇 university of ch...
12 篇 college of manag...
11 篇 chitkara univers...
11 篇 school of comput...
10 篇 columbia univers...
10 篇 univ chinese aca...
10 篇 lovely professio...

作者

13 篇 lima jose
13 篇 pereira ana i.
11 篇 wang wei
9 篇 rakesh kumar
9 篇 wang hui
9 篇 de-shuang huang
9 篇 ye jieping
8 篇 yang tianbao
8 篇 neeraj varshney
8 篇 r. santhana kris...
7 篇 zhang tao
7 篇 zhang lei
7 篇 li li
7 篇 buzdalov maxim
7 篇 wang jian
7 篇 huang heng
7 篇 hu rong
7 篇 yang yang
7 篇 todorov venelin
7 篇 zivkovic miodrag

语言

11,970 篇 英文
1,588 篇 其他
137 篇 中文
1 篇 阿拉伯文
1 篇 西班牙文
1 篇 俄文
1 篇 土耳其文

检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13588 条记录，以下是871-880 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：

Research on Comprehensive Identification and Classification Technology for Latent Faults in High- Voltage Switchgear Based on Machine learning 4

Research on Comprehensive Identification and Classification ...

引用

4th international conference on Advances in Electrical Engineering and Computer applications, AEECA 2023

作者： Tang, Bin Liang, Qinqin Han, Fangyuan Yu, Min Zhang, Longfei Luo, Zongchang Luo, Chuansheng Hu, Mengzhu Wang, Jialin Zhu, Liping Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi China Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd Nanning China Guangxi Power Grid Co. Ltd Guangxi China

ISBN: (纸本)9798350308082

With the development of ultra-high voltage projects in China, the scale of power grids has been expanding. High-voltage switchgear is widely utilized in various substations and the power industry, gradually becoming a critical component of the power system. In response to the issue of latent faults in high-voltage switchgear, this paper proposes a pulse current detection method based on machine learning to achieve detection and classification of latent faults in high-voltage switchgear. the Hilbert-Huang transform is employed along with the theory of energy entropy to extract discharge characteristic parameters, which serve as input vectors for the pattern recognition network. the discharge types are recognized using a Probabilistic Neural Network (PNN), and optimization algorithms are applied to enhance the performance of the PNN. the recognition accuracy before and after optimization is compared. the results indicate that although this method requires a longer recognition time, the recognition accuracy is relatively high, reaching 98.7%. this approach plays a significant role in achieving comprehensive identification and classification of latent faults in high-voltage switchgear. © 2023 IEEE.

关键词： Hilbert-Huang transform

来源：评论

学校读者我要写书评

暂无评论

Exploring the Use of Machine learning and the Internet of things in Intelligent Agriculture: Short Survey 10

Exploring the Use of Machine Learning and the Internet of Th...

引用

10th international conference on optimization and applications, ICOA 2024

作者： El Majdouby, Karima El Mohadab, Mohamed Daoud, Rachid Ait Abouelmehdi, Karim University of Chouaib Doukkali Elites laboratory Faculty of Sciences Computer Science department Eljadida Morocco Sultan Moulay Slimane University Systems Engineering Laboratory Computer Science department Beni Mellal Morocco

ISBN: (纸本)9798350387353

Agriculture is evolving towards more sustainable practices thanks to the integration of the machine learning and Internet of things, which addresses many of the issues related to agricultural production and leads to increased yields with reduced input and labor costs. Smart farming has developed solutions to many conventional agricultural problems to automatically manage and monitor farmland. these technologies reduce the risk of field management in the event of bad weather, and also provide benefits to farmers in the event of labor shortages. the Internet of things ensures the connection of physical objects that are equipped with sensors to the internet, enabling them to collect and exchange data and communicate with each other and with computer systems to gather information, perform analysis and take actions in real time that require rapid data analysis, and this task can be accomplished through machine learning algorithms with greater efficiency and high-quality decision-making. In addition, we carry out an in-depth state-of-the-art survey to identify very recent work that has implemented the Internet of things and machine learning in smart agriculture, analyzing the development of practical solutions and discussing recent research trends and future prospects. © 2024 IEEE.

关键词： Internet of things Machine learning Smart Agriculture

来源：评论

学校读者我要写书评

暂无评论

Research on traffic scheduling algorithm and parameter optimization based on load balancing in time-sensitive network 4

Research on traffic scheduling algorithm and parameter optim...

引用

4th international conference on Machine learning and Computer Application, ICMLCA 2023

作者： Chen, Zhao Beijing Institute of Mechanical Equipment Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Time-sensitive network (TSN) guarantees deterministic delivery of traffic by prioritizing it differently. Commonly used traffic scheduling algorithm is time-aware shaper (TAS) combined with credit-based shaper (CBS). Aiming at the problem of excessive reserved bandwidth for stream reservation (SR) traffic during CBS scheduling, a traffic scheduling strategy based on load balancing was proposed. And the reserved bandwidth parameters of the SR traffic were optimized after completing the modeling of its delay problem. this strategy sliced and encapsulated the SR traffic packets into equal-length data frames at the end nodes and made them uniformly transmitted during the sending cycle. It reduced the end-to-end worst response time of the SR traffic due to packet congestion. After that the delay model was solved using simulated annealing algorithm to get the optimal reserved bandwidth. Simulation results showed that the proposed SR traffic scheduling strategy effectively reduced the reserved bandwidth and the delay of traffic transmission inside the switch on the basis of satisfying the traffic schedulability. © 2024 SPIE.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

Deep learning Transformations in Content-Based Image Retrieval: Exploring the Visual Geometry Group (VGG16) Model 4

Deep Learning Transformations in Content-Based Image Retriev...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bala, Myneni Madhu Bai, K. Jhansi Lakshmi Kattubadi, Sarayu Pulipati, Gayathri Balguri, Aashritha Vnr Vignana Jyothi Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798331518981

Content-based image retrieval (CBIR) is evolving with new computational paradigms such as deep learning in vision applications viz. visual searches, machine translation, and query-based image retrieval. the deep visual learning with Visual Geometry Group (VGG) Model is one of the support layer-based convolution neural networks (CNN) utilized in multiple machine vision tasks like image segmentation, categorization, and object detection within image recovery systems. A VGG architecture consists of numerous blocks, each having a maximum-pooling layer, bus encoder, and 2D complexity. VGG16 consists of 16 layers having extracting features and learning weights to enhance the system's performance. this highlights the challenge of handling large datasets, compounded by the complexity of the network's architecture and resulting features. © 2024 IEEE.

关键词： Machine vision

来源：评论

学校读者我要写书评

暂无评论

RIEMANNIAN DIFFUSION ADAPTATION OVER GRAPHS WIth APPLICATION TO ONLINE DISTRIBUTED PCA 49

RIEMANNIAN DIFFUSION ADAPTATION OVER GRAPHS WITH APPLICATION...

引用

49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Xiuheng Borsoi, Ricardo Augusto Richard, Cedric Univ Cote dAzur OCA CNRS Nice France Univ Lorraine CRAN CNRS Vandoeuvre Les Nancy France

ISBN: (纸本)9798350344868;9798350344851

Distributed adaptation and learning recently gained considerable attention in solving optimization problems with streaming data collected by multiple agents over a graph. this work focuses on such problems where the solutions lie on a Riemannian manifold. this research topic is of particular interest for many applications, e.g., principal component analysis (PCA). Although several incremental and consensus algorithms have been proposed, there is a lack of methods designed for general Riemannian manifolds with efficient diffusion strategies. In this paper, we devise two Riemannian diffusion adaptation strategies, namely, adaptation-then-combination (ATC) and combination-then-adaptation (CTA), for decentralized Riemannian optimization over graphs. In the adaptation step, a Riemannian stochastic gradient descent method (SGD) is used to estimate the local solution at each node. In the combination step, the local estimates at the different nodes are combined by computing the weighted Frechet mean over the neighborhood of each node. We apply our algorithms to online distributed PCA and compare them to both non-cooperative and centralized solutions.

关键词： Diffusion strategy Riemannian manifolds distributed optimization multi-agent system online

来源：评论

学校读者我要写书评

暂无评论

Human activity detection and classification by AI approaches 4th

Human activity detection and classification by AI approaches

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： thakur, Anish Kaur, Geet Kiran Singh, Ranjit Department of CSE Chandigarh University Mohali Gharaun India

ISBN: (纸本)9781032911571

the real area where it is well researched is Human Activity Recognition in the field of Computer Vision. Computer vision is one of the well-explored areas of interest in the proper processing and analysis of visual information with respect to specified applications, e.g., security, health care, human-computer interaction. this will be a huge step forward in effective identification and categorization of human behaviors based on sensor data due to the development of machine/deep learning techniques. Within the present paper outline, there is an attempt to provide a comprehensive review of recent work and advances in the area of human activity detection based on machine/deep learning approaches. this task will review the major challenges in human activity detection, covering dealing with several activities, sensor data unpredictability, and reliable methodologies for classification. We have thus analyzed these models through machine learning-based techniques: traditional, such as Support Vector Machines (SVM) and Random Forests, and innovative deep learning architectures like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). It also covers the problems of small data, challenges for model interpretability, and difficulties to use the models for its purposes in real time. Next, it also gives a glance at potential use cases under health, sport data analysis, and intelligent environments. © 2025 the Author(s).

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

A Matrix Logic Approach to Efficient Frequent Itemset Discovery in Large Data Sets 4

A Matrix Logic Approach to Efficient Frequent Itemset Discov...

引用

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

作者： Li, Xuan Ruan, Tingyi Li, Yankaiqi Lu, Quanchao Sun, Xiaoxuan Columbia University New York United States Northeastern University Boston United States University of Wisconsin-Madison WI United States Georgia Institute of Technology Atlanta United States Mountain View United States

ISBN: (纸本)9798331534622

this paper proposes a frequent itemset mining algorithm based on the Boolean matrix method, aiming to solve the storage and computational bottlenecks of traditional frequent pattern mining algorithms in high-dimensional and large-scale transaction databases. By representing the itemsets in the transaction database as Boolean matrices, the algorithm uses Boolean logic operations such as AND and OR to efficiently calculate the support of the itemsets, avoiding the generation and storage of a large number of candidates itemsets in traditional algorithms. the algorithm recursively mines frequent itemsets through matrix operations and can flexibly adapt to different data scales and support thresholds. In the experiment, the public Groceries dataset was selected, and the running efficiency test and frequent itemset mining effect test were designed to evaluate the algorithm's performance indicators such as running time, memory usage, and number of frequent itemsets under different transaction numbers and support thresholds. the experimental results show that the algorithm can efficiently mine a large number of frequent itemsets when the support threshold is low, and focus on strong association rules with high support when the threshold is high. In addition, the changing trends of running time and memory usage show that the Boolean matrix method can still maintain good running efficiency when the number of transactions increases significantly and has high scalability and robustness. Future research can improve memory optimization and matrix block operations, and combine distributed computing and deep learning models to further enhance the algorithm's applicability and real-time processing capabilities in ultra-large-scale data environments. the algorithm has broad application potential and development prospects in the fields of market analysis, recommendation systems, and network security. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Graph Neural Networks for Physiological Signal Analysis 4

Multi-Modal Graph Neural Networks for Physiological Signal A...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Arora, Heena Sinha, Raj Jayoti Vidyapeeth Women's University Department of Computer Science Jaipur India School of Computer Applications Lovely Professional University Punjab India

ISBN: (纸本)9798331523923

A critically important application for physiological signal analysis lies in very early diagnosis and monitoring of potential medical conditions: among others, in cardiac dysfunction. this article describes a novel ECG Signal Classifier based on Multi-Modal Graph Neural Networks. We represented the ECG signal as time graphs, the time steps having nodes, so the sequential interrelations are their edges, able to represent both kinds of dependencies inside and among them in such a neural model. the MM-GNN utilizes graph convolutional layers to aggregate node-level features and pooling mechanisms to extract global characteristics. Tested on a standardized ECG dataset, the model achieves 86.10% accuracy, 85.40% precision, 92.20% recall, and an F1-score of 88.67%, showing balanced performance in classifying normal and abnormal signals. this framework opens up the possibility of graph-based deep learning in healthcare and is likely to pave the way for advanced diagnostic and real-time monitoring systems. Future work will be extended to other physiological signals and modalities for better diagnosis. © 2025 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Locational FDIA Detection in Smart Grids: A Hyperparameter optimization Analysis 4

Enhancing Locational FDIA Detection in Smart Grids: A Hyperp...

引用

4th international conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Ibraheem, Rawan Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798350306262

In cyber-physical power systems, mitigating the detrimental effects associated with the stealthy nature of False Data Injection Attacks (FDIAs) is imperative. this paper introduces a deep learning model based on a 1-D Convolutional Neural Network (1-D CNN) for simultaneously localizing and detecting FDIAs. To enhance the predictive accuracy of the CNN model, the hyperparameters must be tweaked. An analysis focused on hyperparameter optimization is performed by comparing various optimizers, including random search, hill climbing, and Bayesian optimization (BO). To this end, the number of filters, kernel size, and number of layers of the CNN structure are optimally tuned using Graphics Processing Unit (GPU) resources. the findings of this study present evidence that the BO method is perfectly tailored for the hyperparameter tuning task with minimum error in comparison with competitive models. the efficiency of the CNN-BO method is demonstrated with two benchmark representative examples from the IEEE 14-bus system and the IEEE 118-bus system. For the IEEE 14-bus system, BO uses 256 filters, five kernels, and four layers;the IEEE 118-bus system uses 128 filters, three kernels, and eight layers. the CNN-BO method outperforms other optimization algorithms by achieving an impressive locational detection accuracy of 96.67% for the IEEE 14-bus system. © 2024 IEEE.

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

Proactive Network optimization Using Deep learning in Predicting IoT Traffic Dynamics 4

Proactive Network Optimization Using Deep Learning in Predic...

引用

4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Priya, S. Anu Bhat, Niyati Kanna, B. Rajesh Rajalakshmi, S. Jeyavathana, R. Beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. this study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. the model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. the heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. this dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. the results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 IEEE.

关键词： Time series analysis

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 << < 84 85 86 87 88 89 90 91 92 93 > >>

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：