检索结果-内蒙古大学图书馆

Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

System Level Synthesis for Affine control Policies: Model Based and Data-Driven Settings

arXiv

引用

arXiv 2025年

作者： Schüepp, Lukas De Pasquale, Giulia Dörfler, Florian Alonso, Carmen Amo Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Computer Science Department of Stanford University Palo AltoCA United States Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612AP Netherlands

There is an increasing need for effective control of systems with complex dynamics, particularly through data-driven approaches. System Level Synthesis (SLS) has emerged as a powerful framework that facilitates the control of large-scale systems while accounting for model uncertainties. SLS approaches are currently limited to linear systems and time-varying linear control policies, thus limiting the class of achievable control strategies. We introduce a novel closed-loop parameterization for time-varying affine control policies, extending the SLS framework to a broader class of systems and policies. We show that the closed-loop behavior under affine policies can be equivalently characterized using past system trajectories, enabling a fully data-driven formulation. This parameterization seamlessly integrates affine policies into optimal control problems, allowing for a closed-loop formulation of general Model Predictive control (MPC) problems. To the best of our knowledge, this is the first work to extend SLS to affine policies in both model-based and data-driven settings, enabling an equivalent formulation of MPC problems using closed-loop maps. We validate our approach through numerical experiments, demonstrating that our model-based and data-driven affine SLS formulations achieve performance on par with traditional model-based MPC. © 2025, CC BY.

关键词： Distributed parameter control systems

来源：评论

学校读者我要写书评

暂无评论

A meta-reinforcement learning method for adaptive payload transportation with variations

引用

Neurocomputing 2025年 638卷

作者： Chen, Jingyu Ma, Ruidong Xu, Meng Candan, Fethi Mihaylova, Lyudmila Oyekan, John Institute of Software Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Information Technology & Management University of International Business and Economics Beijing China Department of Computer Science University of York York United Kingdom

The safe transport of cable-suspended payloads by a group of Unmanned Aerial Vehicles (UAVs) depends on their capacity to effectively respond to fluctuations in the dynamics caused by external variations, such as wind gusts. For group transportation with obstacles, internal variations, such as changes in formation, can also alter the space occupancy of the system related to collision detection. However, traditional adaptive learning methods are challenging to adapt to these two variations. In this paper, we present a learning-based method for collision-free dual-UAV–payload transportation in the presence of varied wind force and formation change. It consists of an adaptive trajectory tracking controller based on meta-model-based reinforcement learning with online adaptation and a novel correction policy, and a path planner that can sample collision-free goal states of the system for the controller based on the meta-collision predictor. The simulation results demonstrate that the proposed trajectory tracking controller outperforms state-of-the-art model-free, model-based, and variational inference methods in terms of payload tracking error reduction and robustness when dealing with the variations mentioned above. Specifically, the proposed controller reduces the average payload tracking error to less than 0.1 metres in most tasks without obstacles. Furthermore, by following the adapted paths generated by the path planner, the trajectory tracking controller can effectively track the payload while ensuring collision-free safety of the dual-UAV–payload system during navigation among obstacles. The success rate of the proposed method is more than 80% in all scenarios with obstacles. Our project website can be seen at https://***/view/meta-payload-fly/ and the source code is available at https://***/wawachen/Meta-load-fly. © 2025

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Observer-based finite-time control of conformable Takagi–Sugeno fuzzy systems with time delay

引用

Asian Journal of control 2025年

作者： Kahouli, Omar Gassara, Hamdi Aloui, Ali Elleuch, Zied Bouteraa, Yassine Department of Electronics Engineering Community College University of Ha'il Ha'il Saudi Arabia Laboratory of Science and Technology of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Department of Computer Science Community College University of Ha'il Ha'il Saudi Arabia Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

This paper examines finite-time (F-T) observer-based control of fractional-order nonlinear systems with time delay, employing the Takagi–Sugeno fuzzy (T-SF) approach. This study utilizes the conformable fractional derivative (CFD) as its foundational framework. Sufficient conditions are formulated to guarantee that the augmented vector, consisting of the state estimates and the state estimation errors, stays within a ball centered at the origin. This applies not only to specified initial conditions but also to predefined disturbances. These conditions are then reformulated as linear matrix inequalities (LMIs) conditions, which can be efficiently solved using the LMI toolbox. An illustrative example is given to illustrate the proposed result. © 2025 Chinese automatic control Society and John Wiley & Sons Australia, Ltd.

关键词： Delay-sensitive applications

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Reachability Analysis for Piecewise Affine System

arXiv

引用

arXiv 2025年

作者： Xie, Peng Betz, Johannes Raimondo, Davide M. Alanwar, Amr TUM School of Computation Information and Technology Department of Computer Engineering Technical University of Munich Heilbronn74076 Germany TUM School of Engineering and Design Department of Autonomous Vehicles Systems Technical University of Munich Garching85748 Germany School of Automatic Control Department of Engineering and Architecture University of Trieste Via Valerio 10 Trieste34127 Italy

Hybrid systems play a crucial role in modeling real-world applications where discrete and continuous dynamics interact, including autonomous vehicles, power systems, and traffic networks. Safety verification for these systems requires determining whether system states can enter unsafe regions under given initial conditions and uncertainties—a question directly addressed by reachability analysis. However, hybrid systems present unique difficulties because their state space is divided into multiple regions with distinct dynamic models, causing traditional data-driven methods to produce inadequate over-approximations of reachable sets at region boundaries where dynamics change abruptly. This paper introduces a novel approach using hybrid zonotopes for data-driven reachability analysis of piecewise affine systems. Our method addresses the boundary transition problem by developing computational algorithms that calculate the family of set models guaranteed to contain the true system trajectories. Additionally, we extend and evaluate three methods for set-based estimation that account for input-output data with measurement noise. © 2025, CC BY.

关键词： Convergence of numerical methods

来源：评论

学校读者我要写书评

暂无评论

Editorial: Biomedical signals and artificial intelligence towards smart robots control strategies

引用

Frontiers in Neurorobotics 2025年 19卷 1570870页

作者： Seddik, Hassene Fourati, Hassen Ben Jabeur, Chiraz Khraief, Nahla Department of Computer and Embedded Systems Higher National Engineering School of Tunis Tunis Tunisia Department of Automatic Control Université Grenoble Alpes Auvergne-Rhone-Alpes Saint Martin d'Héres France Ariana Tunisia Tunis Tunisia

来源：评论

学校读者我要写书评

暂无评论

Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

引用

58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michał Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Paweł Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： acoustics mel-frequency cepstral coefficient mfcc sound spectrogram vehicle type detection vehicle type recognition

来源：评论

学校读者我要写书评

暂无评论

H observer-based control for fuzzy Hadamard fractional-order systems

引用

Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligent controller design for the ship steering problem

引用

IEE PROCEEDINGS-control THEORY AND APPLICATIONS 1996年第1期143卷 17-24页

作者： Enab, YM Faculty of Engineering Automatic Control & Computer Engineering Department El-Mansoura University El-Mansoura Egypt

The reason for the present upsurge of interest in intelligent control is that the present generation of control systems are incapable, to a greater or lesser extent, of dealing with problems of a certain complexity. Fortunately, the human operator (HO) is often expert in keeping the complex control systems on the right track. In this paper a method for controller design has been investigated based on a concept of developing a mathematical model for HO behaviour. The method treats HO behaviour as a dynamic process by itself, transformed from the unknown dynamics of the ship to be controlled. The method of controller design entails observing the human controller, constructing a model of the human controller, and using that model as a basis for an automatic control system. During the observation phase the HO controls the ship and the data representing the state of the ship and human actions are registered. During the modelling phase, a human operator behaviour model (HOBM) is constructed as a neural network and its parameters are estimated. During the testing phase, the HOBM controls the ship and its performance is evaluated under the observation phase conditions and completely different conditions. The method has been successfully applied using computer simulation for the ship steering problem.

关键词： man-machine systems intelligent control neural networks learning systems

来源：评论

学校读者我要写书评

暂无评论

LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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

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

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：