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IEEE Conference on Decision and control

作者： Junpeng Zhang Yue Ju Bo Wahlberg Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Instituite of Technology Stockholm Sweden Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity $\mathcal{O}(N^{3})$ , where $N$ is the number of input-output data measurements. This becomes particularly costly when $N$ is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity $\mathcal{O}(N)$ and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

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Multi-Loss Function for Distance-to-collision Estimation

Multi-Loss Function for Distance-to-collision Estimation

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Zhang, Xiangzhu Zhang, Lijia Xu, DIng Pei, Hailong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Min. of Educ. Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong Guangzhou China Guangdong University of Technology School of Computer Science and Technology Guangzhou China

ISBN: (纸本)9781665402453

Estimation of the drone's distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results. © 2021 IEEE.

关键词： Regression analysis

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NFV-Enabled Service Recovery in Space-Air-Ground Integrated Networks: A Matching Game Based Approach

arXiv

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arXiv 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Li, Guangxia Zhou, Fuhui Wu, Qihui Han, Zhu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of ISN Xidian University Xian710071 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

To achieve ubiquitous connectivity of the sixth generation communication, the space-air-ground integrated network (SAGIN) is a popular topic. However, the dynamic nodes in SAGIN such as satellites and unmanned aerial vehicles, may be fragile and out of operation, which can potentially cause service failure. Therefore, the research on service recovery in SAGIN under situations of resource failure is critical. In order to facilitate the flexible resource utilization of SAGIN, the network function virtualization technology (NFV) is proposed to be employed. Firstly, the task management is transformed into the deployment of service function chains (SFCs). Then, we design an NFV-based SFC recovery model in SAGIN in the face of resource failure, so that tasks can quickly select alternative resources to complete deployments. Moreover, the problem of SFC recovery is formulated to minimize the total time consumption for all completed SFCs. Since it is an NP-hard integer linear programming problem, we propose the efficient recovery algorithm based on the matching game. Finally, via various simulations, the effectiveness of the proposed algorithm and its advantages are verified, where the total time consumption is optimized by about 25%, compared with other benchmark methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

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Pilot Study on Using Hybrid–Cascade Filtering on Brain Signals for the control Purposes

Pilot Study on Using Hybrid–Cascade Filtering on Brain Sign...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mariusz Pelc Dariusz Mikołajewski Edward Jacek Gorzelańczyk Anna Wieczorek Henryk Racheniuk Adam Sudoł Kayhan Latifzadeh Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

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automatic removal algorithm of electrooculographic artifacts in non-invasive braincomputer interface based on independent component analysis

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Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical engineering 2022年第6期39卷 1074-1081页

作者： Song, Hao Xu, Song Liu, Guoming Liu, Jing Xiong, Peng Key Laboratory of Digital Medical Engineering of Hebei Province Hebei Baoding071002 China School of Quality and Technical Supervision Hebei University Hebei Baoding071002 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Aerospace Intelligent Control Technology Beijing100854 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China College of Electronic and Information Engineering Hebei University Hebei Baoding071002 China

The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI. © 2022 West China Hospital, Sichuan Institute of Biomedical engineering. All rights reserved.

关键词： Electroencephalography

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An ontology-supported inquiry learning technique

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 16-28页

作者： Xue, Xingsi Zhang, Jie Yang, Chaofan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou Fujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin City Guanxi Province537000 China School of Computer Science and Mathematics Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guilin Guangxi541004 China

Although Inquiry-based Learning (IBL) offers compelling opportunities for the engineering education, one of the challenges to the implementation of it is that the learner always lacks of the background knowledge. To provide him with the necessary knowledge when making the inquiry, this paper proposes an Ontology Supported Inquiry Learning technique (OSIL), which works on the basis of two ontologies, i.e. curriculum ontology and the learner’s ontology. The experimental results show that the constructed curriculum ontology and learner ontology is of high-quality, and the answers recommended by OSIL almost perfectly match with the true answers. From the experimental results, we can see that it is a feasible way of using the software engineering and prototype evolution method to construct the curriculum and learner ontology, and the individual inquiry based on two ontologies is able to provide the learners with high-quality personalized services. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Curricula

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Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers

arXiv

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arXiv 2023年

作者： Cao, John Niazi, M. Umar B. Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information & Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis- Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor meassurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators. © 2023, CC BY.

关键词： Nonlinear systems

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Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification 16th

Multi-feature Fusion Based Deep Forest for Hyperspectral Ima...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Liu, Xiaobo Mohammad, Mostofa Zaman Zhang, Chaochao Gong, Xin Cai, Zhihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789811912528

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Spectroscopy

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A Distributed control framework for the optimal operation of DC microgrids

arXiv

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arXiv 2022年

作者： Fu, Zao Cucuzzella, Michele Cenedese, Carlo Yu, Wenwu Scherpen, Jacquelien M.A. School of Cyber Science and Engineering Southeast University Nanjing211189 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Physikstrasse 3 Zürich8092 Switzerland

In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on the dual theory, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC mcirogrid and the designed distributed control system in a power preserving way, we steer the DC microgrid's state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on the singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework. © 2022, CC BY-NC-SA.

关键词： Perturbation techniques

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Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1029-1042页

作者： Di Cao Weihao Hu Junbo Zhao Guozhou Zhang Bin Zhang Zhou Liu Zhe Chen Frede Blaabjerg the Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina the Department of Electrical and Computer Engineering Mississippi State UniversityStarkvilleMississippiUSA the Department of Energy Technology Aalborg UniversityAalborgDenmark

With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy *** brings great challenges to the operation and ***,with the deployment of advanced sensor and smart meters,a large number of data are generated,which brings opportunities for novel data-driven methods to deal with complicated operation and control *** them,reinforcement learning(RL)is one of the most widely promoted methods for control and optimization *** paper provides a comprehensive literature review of RL in terms of basic ideas,various types of algorithms,and their applications in power and energy *** challenges and further works are also discussed.

关键词： Reinforcement learning deep reinforcement learning power system operation and control optimization

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