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

2,236 篇 会议
13 册 图书
5 篇 期刊文献

馆藏范围

2,254 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,606 篇 工学
- 1,382 篇 计算机科学与技术...
- 966 篇 软件工程
- 321 篇 控制科学与工程
- 241 篇 信息与通信工程
- 218 篇 电气工程
- 177 篇 生物工程
- 129 篇 生物医学工程（可授...
- 91 篇 机械工程
- 76 篇 交通运输工程
- 57 篇 光学工程
- 50 篇 电子科学与技术（可...
- 43 篇 化学工程与技术
- 42 篇 安全科学与工程
- 35 篇 仪器科学与技术
- 32 篇 土木工程
466 篇 理学
- 237 篇 数学
- 182 篇 生物学
- 101 篇 统计学（可授理学、...
- 81 篇 物理学
- 47 篇 系统科学
- 41 篇 化学
434 篇 管理学
- 287 篇 管理科学与工程(可...
- 162 篇 图书情报与档案管...
- 79 篇 工商管理
170 篇 医学
- 163 篇 临床医学
- 81 篇 基础医学(可授医学...
- 54 篇 药学(可授医学、理...
- 34 篇 公共卫生与预防医...
76 篇 教育学
- 74 篇 教育学
49 篇 法学
- 44 篇 社会学
30 篇 经济学
18 篇 农学
2 篇 军事学

主题

173 篇 machine learning
160 篇 deep learning
135 篇 training
100 篇 data models
78 篇 feature extracti...
76 篇 accuracy
70 篇 data mining
67 篇 neural networks
66 篇 predictive model...
61 篇 reinforcement le...
57 篇 computational mo...
52 篇 support vector m...
46 篇 real-time system...
42 篇 machine learning...
39 篇 training data
39 篇 artificial intel...
36 篇 visualization
35 篇 learning systems
35 篇 adaptation model...
31 篇 big data

机构

18 篇 kazan natl res t...
12 篇 king mongkuts un...
10 篇 institute of inf...
9 篇 school of cyber ...
7 篇 chitkara univers...
7 篇 college of compu...
6 篇 king mongkuts un...
6 篇 department of co...
6 篇 the key laborato...
5 篇 chinese acad sci...
5 篇 department of co...
5 篇 department of co...
4 篇 peng cheng lab
4 篇 univ chinese aca...
4 篇 department of ci...
4 篇 school of electr...
4 篇 department of el...
4 篇 school of inform...
4 篇 univ chinese aca...
4 篇 school of comput...

作者

7 篇 yang qiang
7 篇 piriyasurawong p...
6 篇 ouya samuel
6 篇 akatimagool soms...
5 篇 mendy gervais
5 篇 cortez paulo
5 篇 kraysman natalia...
4 篇 peng shaoliang
4 篇 liu sheng
4 篇 you linlin
4 篇 li han
4 篇 barabanova svetl...
4 篇 wang wei
4 篇 huang kaiqi
4 篇 lei baiying
4 篇 wang jinyan
4 篇 khatsrinova olga...
4 篇 shen dinggang
4 篇 li wei
4 篇 li qianmu

语言

2,155 篇 英文
93 篇 其他
27 篇 中文
1 篇 葡萄牙文

检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2254 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：

Reinforcement learning-Based Optimal Control for Continuous-Time Quantized Systems via Adaptive Dynamic Programming

Reinforcement Learning-Based Optimal Control for Continuous-...

引用

IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Omar Qasem Weinan Gao Omar R. Daoud Electrical and Computer Engineering Department School of Engineering and Computing American International University Al-Jahra Kuwait State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Lianning China

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Ahstract- This paper presents a comprehensive investigation into the stability analysis and design conditions for quantizers to ensure the closed-loop stability of continuous-time linear quantized systems, under the conditions derived by the small-gain theorem. The study begins by deriving explicit bounds on the quantizer parameters required for maintaining system stability. Building on this foundation, an optimal controller is designed using the linear quadratic regulator (LQR) framework, providing an efficient data-driven control strategy. To further enhance the system's performance, an adaptive dynamic programming (ADP) algorithm, referred to as the hybrid iteration (HI) method, is developed. This algorithm effectively learns the optimal control policy by leveraging the trajectories of the quantized states and inputs, thereby addressing the challenges posed by quantization constraints. The proposed HI approach combines the advantages of adaptive learning and optimization, making it well-suited for continuous-time systems with limited information. The simulation results confirm that the ADP approach with the provided conditions not only stabilizes the quantized system but also achieves optimal control performance under the specified quantization conditions. This study offers valuable insights and a robust methodological framework for addressing stability and control challenges, with insights to be expanded to continuous-time nonlinear quantized systems, with potential applications in various engineering domains, such as networked systems, robotics and autonomous systems.

关键词： Quantization (signal) Adaptive systems Regulators Simulation Heuristic algorithms Optimal control Stability analysis Dynamic programming Trajectory Thermal stability

来源：评论

学校读者我要写书评

暂无评论

Smart Pipes: An intelligent Robotic Solution for Pipeline Condition Assessment and Inspection

Smart Pipes: An Intelligent Robotic Solution for Pipeline Co...

引用

international Workshop on Research and Education in Mechatronics (REM)

作者： Mohammad Alkhedher Mohammed A. Ghazal Hasan Tariq Hamdan Abdeen Ahmed Osman Abdulrahman Zakaria Awad Omar Lutfi Sidahmed Shahad Faisal Aljaberi Mechanical and Industrial Engineering Department Abu Dhabi University Abu Dhabi UAE Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

Maintaining and inspecting drill pipes remains a significant challenge in the oil and gas industry due to economic and engineering unfeasibility. Therefore, designing smart and effective inspection tools that address these challenges is imperative. This study introduces an intelligent in-pipe inspection robot that integrates an adjustable size mechanism, artificial intelligence (AI), and simultaneous localization and mapping (SLAM) for defect analysis in drill pipes. Structural simulations on the adjustable size mechanism identified an ultimate von Mises stress below the material's yield strength, with a satisfactory factor of safety. Furthermore, the torque demanded by the stepper motor to drive the mechanism yielded maximum values as the wheel platform was close to the robot's body (shrink position). Finally, extensive testing verified the robot's performance. The adjustable diameter system navigated pipelines of various widths, displaying flexibility and robustness after multiple expansion and contraction cycles. The depth camera integration was also confirmed, resulting in clear, real-time visual data and improved inspection capabilities. Real-time visualization of data is supported by AI crack and corrosion detection. Integrating an adjustable size mechanism, AI detection, and SLAM mapping offers effective human-technology interaction and system adaptability during operation.

关键词： Visualization Simultaneous localization and mapping Service robots Corrosion Pipelines Robot vision systems Inspection Cameras Real-time systems Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

intelligent Bearing Fault Diagnosis Using Adaptive Time-Frequency Convolutional Features with Multi-task learning

Intelligent Bearing Fault Diagnosis Using Adaptive Time-Freq...

引用

international conference on Communication Technology (ICCT)

作者： Simin Li Youjiang Liu Yongtao Qiu Shengli Wang China Academy of Engineering Physics Institute of Electronic Engineering Mianyang China Department of Engineering Physics Tsinghua University Beijing China

Deep learning based fault diagnosis methods can extract representative and distinguish features automatically from raw signals and have achieved promising results in recent years. However, deep models rely heavily on massive labeled data and do not generalize well under varying load conditions. Besides, both temporal features and frequency features of raw vibration signals provide useful information in fault diagnosis and should be fully exploit to acquire better performance. To address these issue, a dual-input model based on a convolutional neural network with attention mechanism and multi-task learning strategy is proposed in this paper. The model uses both temporal and frequency adaptive features to achieve end-to-end fault diagnosis. First, a two-branch one-dimensional CNN is designed to learn rich and complementary features from both temporal and frequency data in parallel. Moreover, attention mechanism is adopted in the two-branch CNN to further increase the adaptability of the proposed model under different working loads. Finally, to obtain robust and discriminative features as well as enhance recognition accuracy, a multi-task learning strategy is also employed to better optimize the whole network. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset demonstrate the superiority of our proposed model and can achieve a high fault recognition rate under variable load conditions with fewer training samples.

关键词： Fault diagnosis Training Vibrations Adaptation models Time-frequency analysis Adaptive systems Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Modeling Crowdedness of Emergency Departments Leveraging Crowdsensing Mobility data 19

Modeling Crowdedness of Emergency Departments Leveraging Cro...

引用

2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shou, Tieqi Wang, Zhiyuan Shi, Shang Yang, Dingqi Zhou, Binbin Wang, Cheng Chen, Longbiao Xiamen University SpAtial Sensing and Computing Lab Xiamen China University of Virginia School of Engineering and Applied Science Charlottesville United States Imperial College London Department of Mathematics London United Kingdom University of Macau State Key Laboratory of Internet of Things for Smart City China Zhejiang University City College Department of Computer Science and Computing Hangzhou China

ISBN: (纸本)9798350346558

Hospital emergency departments (EDs) are crucial medical facilities providing emergency healthcare. Understanding and measuring the patient flow in EDs plays a key role in maximizing the utility of scarce hospital resources. Existing approaches either require manual reporting by staff or collect sensitive data about patients via camera or hospital registration system, causing potential privacy breaches. Against this background, we propose a data-driven modeling strategy of ED crowdedness leveraging multi-source urban crowdsensing data, which automatically provides fine-grained and timely information about the crowdedness of EDs. Specifically, our model can not only accurately extract the emergency visit demand from noisy human mobility data with minimum expert knowledge using active learning and co-training techniques, but also estimate ED crowdedness by modeling the emergency service process by integrating three queueing models for general practice, internal medicine, and surgical clinics, respectively. We evaluate our method leveraging large-scale taxi and hire vehicle trajectory datasets and hospital information system datasets from the government open data portal. Results show that our approach effectively extracts the emergency visit demand and models the emergency service process to assess the crowdedness of EDs. We have deployed the system in Xiamen City by collaborating with the municipal government to provide services for citizens and health providers. © 2022 IEEE.

关键词： Emergency services

来源：评论

学校读者我要写书评

暂无评论

data Mining Based Artificial intelligent Technique for Identifying Abnormalities from Brain Signal data 22nd

Data Mining Based Artificial Intelligent Technique for Ident...

引用

22nd international conference on Web Information Systems engineering (WISE)

作者： Tawhid, Md. Nurul Ahad Siuly, Siuly Wang, Kate Wang, Hua Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Australia RMIT Univ Sch Hlth & Biomed Sci Melbourne Australia

ISBN: (纸本)9783030908881;9783030908874

Analysis of brain signal data like Electroencephalography (EEG) plays an important role in efficient diagnosis of neurological disorders and treatment. EEG records electrical activity of the brain and contains huge volume of multi-channel time-series data that are visually analyzed by neurologists to identify abnormalities within the brain, which is time-consuming, error-prone, and subject to fatigue. Therefore, an automatic data mining system is always in need to detect abnormality from those large volume of data. To meet the requirements, in this study, a time-frequency spectrogram image-based classification framework is developed using texture feature extractor and machine learning (ML) based classifiers. At first, signals are filtered to remove noises and artifacts and normalized. Then signals are segmented into small chunks and spectrogram images are generated from those segments using short-time Fourier transform. After that, histogram based textural features are extracted and significant features are selected using principal component analysis. Finally, those features are fed into three ML based classifiers for categorizing the signals into different groups. The proposed system is tested on EEG brain signal data and have obtained promising results in identifying different abnormality groups, which indicates that the proposed system can be used for mining large volume of brain signal data.

关键词： Brain signal data EEG Time-Frequency spectrogram image Machine learning data mining

来源：评论

学校读者我要写书评

暂无评论

Multi-zone Residential HVAC Control with Satisfying Occupants' Thermal Comfort Requirements and Saving Energy via Reinforcement learning 22nd

Multi-zone Residential HVAC Control with Satisfying Occupant...

引用

22nd international conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)

作者： Ding, Zhengkai Fu, Qiming Chen, Jianping Wu, Hongjie Lu, You Hu, Fuyuan SuZhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China Suzhou Univ Sci & Technol Jiangsu Prov Key Lab Intelligent Bldg Energy Effi Suzhou 215009 Jiangsu Peoples R China SuZhou Univ Sci & Technol Sch Archirecture & Urban Planning Suzhou 215009 Jiangsu Peoples R China Chongqing Ind Big Data Innovat Ctr Co Ltd Chongqing 400707 Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Residential HVAC system control has been focused on thermal comfort and energy consumption. Due to the complexity of the dynamic building thermal model, weather conditions and human activities, traditional methods such as rule-based control (RBC) and model predictive control (MPC) are difficult to learn a strategy that can save energy while satisfying occupants' thermal comfort requirements. To solve the above problem, we propose a method combining a thermal comfort prediction model and reinforcement learning to optimize residential multi-zone HVAC control. In this paper, we first design a hybrid model of Support Vector Regression and a Deep Neural Network (SVR-DNN) to predict thermal comfort value, which is taken as a part of the state and reward in reinforcement learning. Then we apply reinforcement learning algorithms (Q-learning, Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG)) to respectively generate an optimal HVAC control strategy to maintain the stability of thermal comfort and minimize energy consumption. The experimental results show that our SVR-DNN model can improve thermal comfort prediction performance by 20.5% compared with the deep neural network (DNN);compared with rule-based control, DDPG, DQN and Q-learning based on SVR-DNN can reduce energy consumption by 11.89%, 8.41%, 6.51% and reduce thermal comfort violation by 91.8%, 43.2%, 25.4%.

关键词： Multi-zone HVAC control Reinforcement learning Energy conservation Thermal comfort

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Federated learning with Fog Nodes: Enhancing Efficiency in Smart City Networks 22

Hierarchical Federated Learning with Fog Nodes: Enhancing Ef...

引用

22nd OITS international conference on Information Technology, OCIT 2024

作者： Manju, A.B. Pavan Kumar, C.S. Jegan, J. Jagadeeshan, D. Nunna, Satya Krishna School of Technology Apollo University Chittoor India Dept.of Information Technology VR Siddhartha Engineering College Vijayawada India Department of CSE Apollo University Chittoor India Department of CSE SRM University-AP Amaravati India

ISBN: (纸本)9798331510404

Federated learning (FL) is decentralized machine learning, which preserves data confidentiality and security but suffers challenges such as high communication overhead, latency, and scalability issues in large scale smart city networks. We propose a Hierarchical Federated learning (HFL) framework which takes advantage of fog nodes to address these problems. HFL framework ensures that the cost of communication is cut back through the introduction of the multilevel aggregation strategy where local models are aggregated first at fog nodes before they are combined at a central server. Communication costs are reduced while latency is improved and scalability is enhanced by this HFL framework through simulations on real-world smart city datasets. On edge devices, our simulation results with real-world datasets from smart cities show that compared to traditional FL it reduces communication overhead by up to 50%, achieves faster model convergence with similar accuracy, and leads to lower energy consumption. This framework represents a big step forward towards deployment of FL in smart cities making it efficient and scalable in resource-constrained environments. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Approach for Question-Level Knowledge Tracing 22nd

An Ensemble Approach for Question-Level Knowledge Tracing

引用

22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Zia, Aayesha Nouri, Jalal Afzaal, Muhammad Wu, Yongchao Li, Xiu Weegar, Rebecka Stockholm Univ Stockholm Sweden

ISBN: (纸本)9783030782702;9783030782696

Knowledge tracing-where a machine models the students' knowledge as they interact with coursework-is a well-established area in the field of Artificial Intelligence in Education. In this paper, an ensemble approach is proposed that addresses existing limitations in questioncentric knowledge tracing and achieves the goal of predicting future question correctness. The proposed approach consists of two models;one is Light Gradient Boosting Machine (LightGBM) built by incorporating all relevant key features engineered from the data. The second model is a Multiheaded-Self-Attention Knowledge Tracing model (MSAKT) that extracts historical student knowledge of future question by calculating their contextual similarity with previously attempted questions. The proposed model's effectiveness is evaluated by conducting experiments on a big Kaggle dataset achieving an Area Under ROC Curve (AUC) score of 0.84 with 84% accuracy using 10fold cross-validation.

关键词： Adaptive learning Knowledge tracing Question-level prediction Artificial Intelligence intelligent education

来源：评论

学校读者我要写书评

暂无评论

Classifying Speech Disorders Using Voice Signals and Machine learning

Classifying Speech Disorders Using Voice Signals and Machine...

引用

learning and Technology conference (L&T)

作者： Durid Al Masri Amro Yousef Layan Turkistani Tariq Tadmori Enfel Barkat Narjisse Kabbaj Electrical and Computer Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

This study explores the application of machine learning (ML) and advanced voice signal analysis to classify speech disorders, including vocal tremors, dysarthria, and stuttering. These disorders pose significant challenges to communication and quality of life, requiring precise and reliable diagnostic tools. To address the inherent challenges of limited data availability in this domain, the study employs synthetic voice data generated using mathematical models to augment real-world recordings. This hybrid approach ensures a more comprehensive dataset, enabling robust training and evaluation of machine learning *** machine learning algorithms such as Support Vector Machines (SVMs), Random Forest, and Gradient Boosting are utilized to extract and analyze a wide range of acoustic features from speech samples. These methods are selected for their ability to handle complex, nonlinear patterns and to identify subtle distinctions between normal and disordered speech. By leveraging these techniques, the study investigates their performance in accurately classifying speech disorders, emphasizing their potential in improving diagnostic *** findings highlight the transformative potential of machine learning in the field of speech pathology. ML models demonstrated notable improvements in the precision, efficiency, and consistency of diagnosing speech disorders compared to traditional methods. This breakthrough not only enhances diagnostic reliability but also equips clinicians with objective tools that reduce subjectivity in assessments. Moreover, the integration of these technologies promotes earlier detection of disorders, enabling timely and tailored therapeutic *** facilitating more accurate and accessible diagnostic practices, this work paves the way for significant advancements in patient care. The use of ML-powered tools can bridge the gap between clinical expertise and technological innovation, empowering healthcare professionals to offer pe

关键词： Training Support vector machines Pathology Accuracy Machine learning algorithms Boosting Real-time systems Mathematical models Random forests Synthetic data

来源：评论

学校读者我要写书评

暂无评论

Recyclable Waste Categorization with Transfer learning

Recyclable Waste Categorization with Transfer Learning

引用

international Workshop on Research and Education in Mechatronics (REM)

作者： Abdelrazzaq Ahmad Abuhejleh Moath Zaid Alafeshat Natheer Almtireen Hisham Elmoaqet Mutaz Ryalat Mohammad M. AlAjlouni Mechatronics Engineering German Jordanian University Amman Jordan Jordan Design and Development Bureau (JODDB) Amman Jordan

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

Environmental sustainability has become a critical concern globally, with waste management being a significant challenge. Traditional methods of waste segregation often rely heavily on manual labor, leading to inefficiencies, inaccuracies, and increased costs. As the volume of waste continues to grow, there is an urgent need to adopt innovative technologies to enhance waste management practices. This study presents an AI algorithm designed to improve the efficiency and accuracy of waste segregation, thereby enhancing sustain ability efforts. The algorithm utilizes advanced Convolutional Neural Network (CNN) techniques, specifically utilizing a pre-trained DenseNet169 model and employing transfer learning to classify waste materials into specific categories: paper, plastic, and metal. By employing CNNs, the system achieves high precision in identifying various types of waste based on visual data, thereby reducing human error and labor costs associated with manual sorting.

关键词： Waste management Waste materials Visualization Costs Transfer learning Manuals Classification algorithms Convolutional neural networks Plastics Sorting

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226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：