检索结果-内蒙古大学图书馆

Dynamic Hybrid Models With Active Sampling and Adaptive Selection of Double-Domain Features for the Tuning of Microwave Cavity Filters

引用

ieee TRANSACTIONS ON CYBERNETICS 2024年第8期54卷 4828-4840页

作者： Bi, Leyu Cao, Weihua Shi, Yang Hu, Wenkai Guo, Linwei Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China China Univ Geosci Engn Res Ctr Intelligent Technol Geoexplorat Minist Educ Wuhan 430074 Peoples R China Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada

Microwave cavity filters are essential electromechanical coupling devices in communication systems. Structural-parameter tuning by experienced operators improves the filter performance but is demanding and time-consuming. The automatic tuning method has received extensive research attentions using data-driven modeling approaches. However, two main issues affect the accuracy and efficiency of the model construction: 1) features of tuning processes, as model inputs, have limited adaptability and extraction accuracy to different resonant states and 2) models require plentiful training data and the training process is time-consuming. Thus, dynamic hybrid models are developed in this study with self-selected inputs, self-organized samples, and a self-learning structure. First, spatial features are extracted to flexibly depict the tuning characteristic, and double-domain (spatial or circuital) features are selected adaptively to accommodate distinct resonance states. Second, a trustworthiness-curiosity-driven active sampling method is exploited to attain fewer and better-training data. Third, an improved broad learning systemBLS is developed using new modules of incremental node calculation and weight pruning, characterized by more lightweight and flexible structures. The proposed method is effective and flexible demonstrated by simulations and experiments, and the tuning task of microwave cavity filters is fulfilled in a more accurate and efficient manner.

关键词： Active sampling adaptive feature selection broad learning systems incremental learning intelligent tuning microwave cavity filters

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement learning Approach Combined With Model-Based Paradigms for Multiagent Formation control With Collision Avoidance

引用

ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第7期53卷 4189-4204页

作者： Pu, Zhiqiang Zhang, Tianle Ai, Xiaolin Qiu, Tenghai Yi, Jianqiang Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Generating collision-free formation control strategy for multiagent systems faces huge challenges in collaborative navigation tasks, especially in a highly dynamic and uncertain environment. Two typical methodologies for solving this problem are the conventional model-based paradigm and the data-driven paradigm, particularly the widely used deep reinforcement learning (DRL) method. However, both the model-based and data-driven paradigms encounter inherent drawbacks. In this paper, we present two novel general schemes that combine these two paradigms together in an online mode. Specifically, the two paradigms are combined in a parallel and a serial structure in these two schemes, respectively. In the parallel scheme, the outputs of the model-based and DRL-based controllers are lumped together. In the serial scheme, the output of the model-based controller is fed as an input of the DRL-based controller. The interpretation of the two combined schemes is suggested from a control-oriented perspective, where the parallel DRL controller is viewed as a complementary uncertainty compensator and the serial DRL controller is taken as an inverse dynamics estimator. Finally, comprehensive simulations are conducted to demonstrate the superiority of the proposed schemes, and the effectiveness is further verified by deploying our schemes to a physical experiment platform based on a set of three-wheeled omnidirectional robots.

关键词： Collision avoidance Task analysis Navigation Maintenance engineering Adaptation models Shape Predictive models combined model-based and data-driven deep reinforcement learning (DRL) formation control multiagent system (MAS)

来源：评论

学校读者我要写书评

暂无评论

Online Consensus control of Nonlinear Affine systems From Disturbed data

引用

ieee/CAA Journal of Automatica Sinica 2024年第2期11卷 551-553页

作者： Yifei Li Wenjie Liu Jian Sun Chen Chen Jia Zhang Gang Wang National Key Laborary of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081 Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China

Dear Editor,In this letter, we introduce a novel online distributed data-driven robust control approach for learning controllers of unknown nonlinear multi-agent systems(MASs) using state-dependent representations.

关键词： agent online Online

来源：评论

学校读者我要写书评

暂无评论

Enhancing Industry 4.0 Maturity: Integrating Lean Practices as a Key Feature in Artificial Intelligence-driven Decision Support Model for Companies 28

Enhancing Industry 4.0 Maturity: Integrating Lean Practices ...

引用

28th ieee International conference on Intelligent Engineering systems (INES)

作者： Oussama, Ben Ali Fehmi, H'Mida Sondes, Hammami Natl Engn Sch Tunis ENIT Mech Prod & Energy Lab LMPE LR18ES01 ENSIT Tunis Tunisia Natl Engn Sch Carthage ENICarthage Mech Prod & Energy Lab LMPE LR18ES01 ENSIT Tunis Tunisia Natl Engn Sch Carthage ENICarthage Syst Anal Design & Control Lab LR ACS ENIT Tunis Tunisia

ISBN: (纸本)9798350367607;9798350367591

This paper presents an Artificial intelligence (AI)-driven approach designed to provide a sophisticated decision support tool for companies. The main goal of this machine learning (ML) model is to aid companies in elevating their Industry 4.0 maturity level by guiding strategic decision-making processes, drawing insights from successful companies that have attained high maturity levels in similar contexts.

关键词： Artificial intelligence Industry 4.0 decision-support tool machine learning smart maturity level

来源：评论

学校读者我要写书评

暂无评论

Robust Stability and Stabilization of Switched Parametric Uncertain Linear systems with Infinite Subsystems 13

Robust Stability and Stabilization of Switched Parametric Un...

引用

13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhang, Jing Liang, Kaili Zhu, Liying Jinan250353 China

ISBN: (纸本)9798350361674

This paper investigates the robust stability issues of switched parametric uncertain linear systems with infinite subsystems and their constrained dwell times. Firstly, a switching Lyapunov function is obtained by solving a family of differential Lyapunov equations and algebraic Lyapunov equations. Secondly, based on the obtained switching Lyapunov function, this paper proposes some sufficient conditions for robust globally asymptotic stability of such kind of switched systems. Thirdly, by means of the proposed robust stability results, robust globally asymptotically stabilized state feedback controllers are designed for switched parameter uncertain linear systems with infinite subsystems. Finally, an illustrative example and its numerical simulations demonstrate the effectiveness of the new results given in this paper. © 2024 ieee.

关键词： State feedback

来源：评论

学校读者我要写书评

暂无评论

Robust Adaptive control Barrier Functions for Input-Affine systems: Application to Uncertain Manipulator Safety Constraints

引用

ieee control systems LETTERS 2024年 8卷 279-284页

作者： Zeng, Danping Jiang, Yiming Wang, Yaonan Zhang, Hui Feng, Yun Hunan Univ Sch Robot Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

Most existing control barrier functions-based control strategies for manipulator systems require a perfect knowledge of the model or consider the worst-case uncertainties. To solve this problem, a composite learning-enhanced adaptive optimal control approach is first proposed for manipulator systems, which achieves constraint satisfactions of joint positions and velocities in the presence of model uncertainties, and leveraging historical data online reduces uncertainties in estimated parameters. Technically, to ensure constraint satisfactions, a series of zeroing control barrier functions are designed, based on which the conditions that guarantee the forward invariance of the constraint-admissible set are derived. Then, a data-driven approach is utilized to reduce the conservatism of the robust adaptive control barrier functions by tightening the bounds of the unknown parameters. A manipulator system illustrates the effectiveness of the proposed method.

关键词： control barrier functions manipulator systems robust adaptive control adaptive optimal control

来源：评论

学校读者我要写书评

暂无评论

A data-driven Method for Online Gain Scheduling of Distributed Secondary controller in Time-Delayed Microgrids

引用

ieee TRANSACTIONS ON POWER systems 2024年第3期39卷 5036-5049页

作者： Xia, Yang Xu, Yan Wang, Yu Yao, Weitao Mondal, Suman Dasgupta, Souvik Gupta, Amit K. Gupta, Gaurav M. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China Nanyang Technol Univ Interdisciplinary Grad Sch ERI N Singapore 639798 Singapore Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China Rolls Royce Singapore Pte Ltd Singapore 638673 Singapore Rolls Royce Solut GmbH D-88045 Friedrichshafen Germany

Secondary control of a microgrid is to restore the frequency/voltage and share power among different units. However, due to time-delay issues in secondary control loops, system instability may happen. To solve this problem, a data-driven approach is proposed for scheduling the control gains to ensure the stability of the microgrid. By utilizing real-time measured current as input, the proposed method can appropriately adjust the control gain setting for the distributed secondary control to achieve a stable operation even under various time-delay scenarios. First, a time-delayed small-signal model is developed for microgrid stability analysis. Based on the damping ratio calculated from the small-signal model, a constrained soft actor-critic (SAC) algorithm is designed to learn an optimal policy of gain scheduling, which can improve the safety and efficiency of learning. Finally, case studies are carried out to validate that the proposed method can provide an optimal gain scheduling policy, which enhances the stability of microgrids during real-time operation.

关键词： Microgrid secondary control time-delayed small-signal model data-driven gain scheduling constrained soft actor-critic

来源：评论

学校读者我要写书评

暂无评论

A Bidirectional Attention-Based LSTM Model for Multi-Step Ahead Prediction in Dynamic Soft Sensors 13

A Bidirectional Attention-Based LSTM Model for Multi-Step Ah...

引用

13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Huang, Yi Wang, Longyan Ye, Lingjian Yang, Zeyu School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou313000 China

ISBN: (纸本)9798350361674

In the field of dynamic soft sensor developments, multi-step ahead prediction of key quality variables is becoming increasingly important, which can provide a long-term data changing trends in the future time period. However, multi-step ahead prediction is currently facing various issues such as data uncertainty and unstable forecasts. In order to improve these issues, this paper proposes a bidirectional spatiotemporal attention-based LSTM network model (Bi-STA-LSTM), in which spatiotemporal attention extracts the relevant features from historical time series, and the bidirectional structure captures more historical and future trends. It can continue to provide useful information when the prediction step increases. By comparing different methods on the debutanizer dataset, the superiority of the Bi-STA-LSTM is verified. © 2024 ieee.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Finding an Optimal Geometric Configuration for TDoA Location systems with ADWP-GWO Algorithm 13

Finding an Optimal Geometric Configuration for TDoA Location...

引用

13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Li, Jingyuan Wang, Huiwei Shi, Yawei Chen, Menggang Li, Huaqing Li, Chuandong College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9798350361674

Anchor deployment is a necessary prerequisite for achieving high-accuracy indoor localization. In order to enhance the comprehensive positioning performance, a criterion for minimizing Dilution of Precision (DoP) is proposed to measure the positioning performance. Based on this, we model the anchor deployment as an optimization problem and propose an Adaptive Dynamic Weight Position Update Gray Wolf Optimization(ADWP-GWO) algorithm to solve the multivariate dimensionality problem. Two basic anchor deployment strategies are designed and validated by real-world experiments with the TDoA localization system. The experimental results indicate that the RMSE achieved on the basis of the ADWP-GWO algorithm deployment scheme is 0.3766m is better than the triangular deployment of 0.5447m and the rectangular deployment scheme of 0.5963m. This is considered as the best deployment which realizes high-precision positioning. © 2024 ieee.

关键词： Time difference of arrival

来源：评论

学校读者我要写书评

暂无评论

data Extraction, Visualization, and Prediction through Natural Language Processing

Data Extraction, Visualization, and Prediction through Natur...

引用

ieee International conference on Omni-Layer Intelligent systems (ieee COINS)

作者： Alvarado, Carlos Velasquez, Gabriel Mauricio, David Univ Peruana Ciencias Aplicadas Fac Ingn Lima Peru

ISBN: (纸本)9798350349603;9798350349597

This study presents datalyzer, a system designed for data extraction, visualization, and prediction in the mining sector using advanced NLP and machine learning, specifically GPT-3.5 Turbo. The system enhances operational efficiency through rigorous data preprocessing and specialized fine-tuning, validated on a simulated mining dataset. Results show significant improvements: data extraction time reduced by 94% and visualization time by 97.6%. These improvements indicate a transformation in efficiency, usability, and user satisfaction. Despite limitations in data variability and complexity, this pioneering approach highlights the potential of NLP and machine learning in modernizing the mining industry and supporting data-driven decision-making.

关键词： NLP data Visualization Predictive Analytics Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：