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

336 篇 会议

馆藏范围

336 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

302 篇 工学
- 225 篇 控制科学与工程
- 178 篇 计算机科学与技术...
- 123 篇 软件工程
- 41 篇 电气工程
- 35 篇 信息与通信工程
- 21 篇 机械工程
- 21 篇 生物工程
- 19 篇 电子科学与技术（可...
- 17 篇 化学工程与技术
- 16 篇 生物医学工程（可授...
- 14 篇 动力工程及工程热...
- 13 篇 石油与天然气工程
- 13 篇 交通运输工程
- 10 篇 航空宇航科学与技...
- 7 篇 力学（可授工学、理...
- 7 篇 仪器科学与技术
- 7 篇 材料科学与工程（可...
- 7 篇 安全科学与工程
200 篇 理学
- 150 篇 系统科学
- 119 篇 数学
- 34 篇 统计学（可授理学、...
- 23 篇 生物学
- 17 篇 物理学
- 11 篇 化学
51 篇 管理学
- 36 篇 管理科学与工程(可...
- 21 篇 图书情报与档案管...
- 11 篇 工商管理
14 篇 医学
- 13 篇 临床医学
- 9 篇 基础医学(可授医学...
7 篇 农学
- 7 篇 作物学
5 篇 经济学
4 篇 法学
2 篇 教育学

主题

12 篇 reinforcement le...
10 篇 deep learning
10 篇 multi agent syst...
10 篇 controllers
8 篇 failure analysis
8 篇 sliding mode con...
8 篇 machine learning
6 篇 nonlinear system...
6 篇 data-driven
5 篇 learning systems
5 篇 closed loop syst...
5 篇 state estimation
5 篇 real-time system...
5 篇 linear systems
4 篇 big data
4 篇 neural networks
4 篇 learning algorit...
4 篇 photovoltaic sys...
4 篇 linear matrix in...
4 篇 lyapunov functio...

机构

6 篇 school of electr...
6 篇 school of automa...
5 篇 school of automa...
4 篇 school of automa...
4 篇 college of engin...
4 篇 college of infor...
4 篇 state key labora...
4 篇 beijing key lab ...
4 篇 college of infor...
4 篇 school of contro...
3 篇 guangdong univ t...
3 篇 college of artif...
3 篇 school of electr...
3 篇 school of electr...
3 篇 changchun univer...
3 篇 ningbo universit...
3 篇 college of infor...
3 篇 dalian maritime ...
3 篇 guangdong univ t...
2 篇 shenyang univ ch...

作者

7 篇 wang li
7 篇 bu xuhui
6 篇 chi ronghu
6 篇 ge zhiqiang
5 篇 lu renquan
5 篇 ren hongru
5 篇 sun zhongbo
5 篇 huang deqing
5 篇 song zhihuan
4 篇 zhao chunhui
4 篇 liu keping
4 篇 ren xuemei
4 篇 ji honghai
4 篇 hou zhongsheng
4 篇 honghai ji
4 篇 li wang
4 篇 yao le
4 篇 liu yi
3 篇 li jiapeng
3 篇 wang jing

语言

328 篇 英文
8 篇 其他
3 篇 中文

检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是281-290 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：

HALIDS: a Hardware-Assisted Machine learning IDS for in-Network Monitoring 8

HALIDS: a Hardware-Assisted Machine Learning IDS for in-Netw...

引用

8th Network Traffic Measurement and Analysis conference (TMA)

作者： Brandino, B. Grampin, E. Dietz, K. Wehner, N. Seufert, M. Hossfeld, T. Casas, P. Univ Republica INCO FING Montevideo Uruguay AIT Austrian Inst Technol Vienna Austria Univ Wurzburg Wurzburg Germany Univ Augsburg Augsburg Germany

ISBN: (纸本)9798350378887;9783903176645

Early decision-making at the network device level is crucial for network security. this entails moving beyond traditional forwarding functions towards more intelligent network devices. Integrating Machine learning (ML) models into the data plane enables quicker processing and reduced reliance on the control plane. this paper explores the development of a ML-driven Intrusion Detection System (IDS) where network devices autonomously make security decisions or defer to an expert Oracle, relying on in-band and off-band traffic analysis. Programmable devices, such as those using P4, are essential to enable these functionalities and allow for network device re-training to adapt to changing traffic patterns. We introduce HALIDS, a prototype for in-band ML-IDS using P4, complemented with off-band Oracles which support in-network ML-driven classification with more confident classifications, targeting an active learning logic for more accurate in-band analysis. We implement HALIDS using the open source software switch BMv2, and show its operation with real traffic traces publicly available.

关键词： In-network machine learning Programmable data plane P4 Active learning Oracles

来源：评论

学校读者我要写书评

暂无评论

Automatic Docking of Drone Using Machine learning for Power Line Inspection 8

Automatic Docking of Drone Using Machine Learning for Power ...

引用

8th International conference on Robotics and Automation Engineering, ICRAE 2023

作者： Aiswarya, A. Kiran, R. College of Engineering Robotics and Automation Dept of Ece Kerala Trivandrum India College of Engineering Department of Electronics and Communication Kerala Trivandrum India

ISBN: (纸本)9798350327656

Unmanned aerial vehicles (UAVs) have gained sig-nificant attention in the field of power line inspection due to their ability to access challenging locations and capture high-resolution imagery. However, the manual control and landing of UAVs on power line structures pose risks and operational challenges. To address these issues, we proposes a machine vision based automatic docking system for drones, specifically designed for power line inspection. A specially engineered gripper module enables the system to securely dock onto a power line. the proposed system incorporates a machine learning-based model that detects and tracks the power line using onboard cameras, enabling the drone to align itself accurately with the power line. Experimental evaluations demonstrate the effectiveness and fea-sibility of the automatic docking system, showcasing its potential to streamline power line inspection processes. Utilizing machine learning for automatic drone docking in power line inspections is driven by the pursuit of safety, as drones eliminate human exposure to high-risk situations commonly encountered during manual inspections. this approach is time-efficient and cost-effective, offering the capability for real-time, highly accurate data analysis. the scalability of machine learning models enables consistent inspections over vast power line networks. As machine learning models continually refine themselves with more data and serves as a testament to modernization and safety. © 2023 ieee.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

8th conference on Foundations of systems Biology in Engineering, FOSBE 2019 - Proceedings

8th Conference on Foundations of Systems Biology in Engineer...

引用

8th conference on Foundations of systems Biology in Engineering, FOSBE 2019

the proceedings contain 47 papers. the topics discussed include: biomolecular signal tracker with fast time response;efficient learning in metabolic pathway designs through optimal assembling;multiobjective optimization of gene circuits for metabolic engineering;leveraging resource competition for part characterization in cell-free extracts;bayesian model selection in synthetic biology: factor levels and observation functions;impact of changing cell-cell communication network in models of epithelial pattern formation;a comparison of methods for quantifying prediction uncertainty in systems biology;literature and data-driven based inference of signaling interactions using time-course data;challenges in the calibration of large-scale ordinary differential equation models;and disturbance response analysis of cell-to-cell communication systems based on spatial frequency decomposition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of Adaptive Training control in Dispatcher Training Simulators 8

Design of Adaptive Training Control in Dispatcher Training S...

引用

8th Asia conference on Power and Electrical Engineering, ACPEE 2023

作者： Lai, Xinyi Chen, Hongfu Dong, Ao Lin, Changnian Jin, Wei Ruan, Mengyu Saeed, Nimrah Han, Zheng Zhejiang University School of Electrical Engineering Hangzhou China State Grid Shanghai Electric Power Company Shanghai China Beijing Kedong Electric Control System Co. Ltd Beijing China Beijing China

ISBN: (纸本)9798350345520

With the expansion of power systems and the development of smart grids, power system operations become increasingly complex and critical, calling for skilled and experienced dispatchers. the dispatcher training simulator (DTS) has been widely used as an effective training tool for decades, providing a realistic simulation of the dispatching environment. Efforts have been made to improve the simulation of DTS, while there is a research gap in improving the training module of DTS. Currently, DTS does not consider trainees' learning states and preferences, and therefore cannot provide customized training services for different trainees, limiting its training effectiveness. Referring to learning analytics and adaptive learning systems, this paper investigates the training process control of DTS based on trainees' learning states. A data-driven adaptive training control framework is proposed, including three stages: learning state monitoring, learning state assessment, and learning process control. the presented adaptive training control framework allows the DTS system to provide personalized and targeted training services to dispatcher trainees, promoting the intelligence and automation of the DTS system. © 2023 ieee.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Triple Hybrid Terresstrial FSO/RF/MMW System with Receiver's Diversity 8

Triple Hybrid Terresstrial FSO/RF/MMW System with Receiver's...

引用

8th International conference on Modern Circuit and System Technologies (MOCAST)

作者： Roumelas, G. D. Nistazakis, H. E. Stassinakis, A. N. Tombras, G. S. Volos, Ch. K. Natl & Kapodistrian Univ Athens Fac Phys Dept Elect Comp Telecommun & Control Athens Greece Aristotle Univ Thessaloniki Dept Phys Thessaloniki Greece

ISBN: (纸本)9781728111841

the continuously expanding demand for faster and more efficient communication, dictates the research and development of more contemporary telecommunication systems, such as the FSO, characterized by their very high data rates and bandwidth. However, the optical beam, propagating through the atmosphere, suffers from phenomena with negative impact on the performance of the overall communication system. A commonly used technique to counterbalance these obstacles is to use a hybrid FSO/RF or FSO/MMW system. In this work, we further develop this idea, by studying a triple hybrid FSO/MMW/RF system, operating over turbulent atmospheric channel with pointing errors, along with Weibull and Rayleigh fading for the MMW and RF links, respectively. the mathematical expression for the outage performance estimation of the triple hybrid system is derived and its efficiency is verified through the numerical results.

关键词： FSO communication systems hybrid FSO/MMW/RF negative exponential turbulence pointing errors Rayleigh fading Weibull fading selection combining outage probability

来源：评论

学校读者我要写书评

暂无评论

Exploring Alzheimer's Disease Risk Prediction: A data-driven Approach with RandomForestClassifier 8

Exploring Alzheimer's Disease Risk Prediction: A Data-Driven...

引用

8th International conference on Inventive systems and control, ICISC 202

作者： Jampani, Pallavi Javvadi, Varun Raj Nandi, Varun Sai Tanuku, Sai Varun Amarendra, K. Benarji, Bejjam S. N. Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350386578

In this work, we employ a sophisticated machine learning technique based on the RandomForestClassifier model to examine Alzheimer's disease risk prediction. the '***' and '***' full datasets, which comprise both cross-sectional and longitudinal data, serve as the basis for our study. Our research project's major purpose is to create and assess a prediction model that may identify a person's chance of getting Alzheimer's disease based on clinical and demographic characteristics. Our method entails substantial data preparation, including feature engineering, imputation of missing values, and normalization. Feature engineering is a discrete stage. Stratified cross-validation approaches are used to train and modify the RandomForestClassifier model, establishing the validity of the performance metrics. Several important results from our work include evaluations of model correctness, a thorough assessment of prediction abilities, and insights into critical components that contribute to the risk assessment of Alzheimer's disease. In this chapter, we underline how machine learning may increase Alzheimer's disease research and clinical practice, and we analyze the implications of our results for early detection and therapeutic possibilities. this paper contributes a large quantity of information to the growing corpus in the realm of Alzheimer's disease prediction. It illustrates the advantages of data-driven methods and the importance of applying powerful machine learning algorithms in order to enhance healthcare results. © 2024 ieee.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Forecasting the Condition Severity of COVID-19 in Terms of Prevalence in Iran 8

Forecasting the Condition Severity of COVID-19 in Terms of P...

引用

8th International conference on control, Instrumentation and Automation, ICCIA 2022

作者： Hajji, Morteza Rafiee, Ahmad Ali Safavi, Ali Akbar Farhang, Mahmoud Shiraz University Department of Power and Control Engineering Shiraz Iran Shiraz University Department of Electronics and Telecommunications Engineering Shiraz Iran

ISBN: (纸本)9781665495691

Since the advent of the Coronavirus disease 2019 (COVID-19) pandemic, one of the most significant attempts toward coping with this disease was predicting the severity condition of this pandemic in terms of prevalence. In this way, the authorities can make better and more informative decisions. Machine learning has great potential to solve such data-driven problems. In this article, the issue of predicting the future COVID-19 prevalence severity as a classification task has been investigated. We employed four state-of-the-art predictive models, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Self-Attention mechanism, in order to predict the COVID-19 prevalence severity in Iran using the gathered dataset by Johns Hopkins University. the main challenge we have faced was data scarcity because of the limited duration of the pandemic. Besides, as we are considering a classification task, data imbalance was an issue due to imposing a conventional criterion to divide each day into three classes regarding the state of severity. the final results suggest that although the performance of all models was acceptable, Self-Attention with an accuracy of 95% was the superb architecture to deal with this classification task. © 2022 ieee.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

data-driven Model for Detection, Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft 34

Data-Driven Model for Detection, Isolation and Accommodation...

引用

34th Chinese control and Decision conference (CCDC)

作者： Mersha, Bemnet Wondimagegnehu Ma, Hongbin Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

ISBN: (纸本)9781665478960

Ethiopian Airlines' Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10, 2019, at Ejere Town, south of Addis Ababa. A faulty angle of attack (AOA) sensor was the cause of the crash. Many airplane accidents have been linked to faulty AOA sensors in the past. the majority of the AOA sensor fault detection, isolation, and accommodation (SFDIA) literature relied on linear model-driven techniques, which are not suitable when the system's model is uncertain, complex, or nonlinear. Traditional multilayer perceptron (MLP) models have been employed in data-driven models in the literature and the effectiveness of deep learning-based data-driven models has not been investigated. In this work, a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is proposed. the proposed model uses a deep learning-based recurrent neural network (RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement, faulty total velocity measurement, and faulty pitch rate measurement. Conventional residual analysis with a fixed threshold is used to detect and isolate faulty AOA sensors. the proposed and benchmark models are trained with the adaptive momentum estimation (Adam) algorithm. We show that the proposed model effectively detects, isolates, and accommodates faulty AOA measurements when compared to other data-driven benchmark models. the method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： data-driven modeling Intelligent transportation systems Faulty angle of attack measurement Sensor fault Detection Isolation Accommodation (SFDIA) and Recurrent neural network

来源：评论

学校读者我要写书评

暂无评论

Artificial Neural Network Based Automatic Voltage Regulator for a Stand-Alone Synchronous Generator 8

Artificial Neural Network Based Automatic Voltage Regulator ...

引用

International conference on Renewable Energy Research and Applications

作者： Bal, Gungor Kaplan, Orhan Yalcin, Suleyman Samet Gazi Univ Fac Technol Ankara Turkey

ISBN: (纸本)9781728135878

In this study, an automatic voltage regulator (AVR) based on artificial neural network (ANN) is presented for a standalone synchronous generator. the AVR maintains the constant output voltage independent of load by altering its excitation current and brings the output voltage to the desired level. Generally, AVR systems are designed by using proportional-integral-derivative (PID) controller. In this paper ANN based AVR system is proposed to get the constant output voltage. An experimental setup was prepared to acquire data to be used for training ANN. the experimental setup consists of synchronous generator, asynchronous motor as actuation machine, frequency inverter and load groups. ANN is trained with the experimental data by using 3 different learning methods defined on MATLAB. then, the ANN, which is trained by each learning method respectively, is tested separately on MATLAB and the results are compared.

关键词： Automatic voltage regulator artifical neural network synchronous generator automatic voltage control

来源：评论

学校读者我要写书评

暂无评论

AI-driven Defect Detection in Glass Bangles Manufacturing Using Transfer-learning with MobileNet Model 8

AI-Driven Defect Detection in Glass Bangles Manufacturing Us...

引用

8th International conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2024

作者： Sasongko, Ananto Tri Khofiyah, Nida An Hadikristanto, Wahyu Teknik Informatika Universitas Pelita Bangsa Bekasi Indonesia Teknik Industri Universitas Pelita Bangsa Bekasi Indonesia

ISBN: (纸本)9798350368970

Defect detection in glass bangles is a critical quality control challenge in the manufacturing industry. Traditional inspection methods, reliant on human expertise, are time-consuming and prone to error, leading to inefficiencies and increased production costs. this study introduces an AI-driven approach leveraging MobileNet and transfer learning to automate and enhance the defect detection process. MobileNet, a lightweight convolutional neural network, is fine-tuned using transfer learning on a dataset of labeled bangle images to identify defects such as cracks and broken. the methodology involves data collection and augmentation, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. the trained model demonstrates high accuracy and robustness, significantly outperforming traditional inspection methods. Its lightweight nature ensures feasibility for deployment on edge devices, facilitating real-time defect detection in manufacturing environments. Key findings reveal that the AI-driven system improves defect detection accuracy. this study applied an AI-driven approach leveraging MobileNet and transfer learning to automate the defect detection process in glass bangles manufacturing. the proposed model achieved a validation accuracy of 93%, with a precision of 92%, recall of 94%, and an F1-score of 93%. these results indicate a significant improvement over traditional inspection methods, demonstrating the potential of AI in enhancing quality control in manufacturing. the conclusion underscores the transformative impact of AI in manufacturing, suggesting broader applications across various industries. © 2024 ieee.

关键词： Convolutional neural networks

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34页 << < 25 26 27 28 29 30 31 32 33 34 > >>

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：