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作者： Azab, Ahmed M. Ahmadi, Hamed Mihaylova, Lyudmila Arvaneh, Mahnaz Department of Automatic Control and System Engineering Sheffield University Sheffield United Kingdom School of Computer Science and Electronic Engineering University of Essex Essex United Kingdom

Objective. Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. Approach. The proposed framework combines the subject-specific covariance matrix (Σ55) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix (ΣDTW) estimated using previous subjects trials. In the proposed ΣDTW, the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials. Main results. The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. Significance. Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class. © 2020 IOP Publishing Ltd.

关键词： Covariance matrix

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Multistability Analysis and Adaptive Feedback control on a New Financial Risk system

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International Journal of Applied and Computational Mathematics 2023年第5期9卷 1-16页

作者： Johansyah, Muhamad Deni Sambas, Aceng Vaidyanathan, Sundarapandian Benkouider, Khaled Sulaiman, Ibrahim Mohammed Department of Mathematics Universitas Padjadjaran Jatinangor Sumedang 45363 Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu Besut 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Center for Control System Vel Tech University Avaddi Chennai 60062 India Department of Non-Destructive Automatic Testing Laboratory Jijel University Rue Rouikha Mokhta Jijel 1800 Algeria School of Quantitative Science University Utara Malaysia Sintok Kedah Bukit Kayu Hitam 06010 Malaysia

This study presents the findings of a new financial risk system with one absolute function and two quadratic nonlinearities. The rich dynamic behaviors of the new financial risk system are discussed analytically and numerically through stability analysis, phase portrait, bifurcation diagram, Lyapunov exponent, multistability and coexisting attractors. In addition, a new financial risk system with two quadratic nonlinearities and one absolute function shows multistability and coexistence of chaotic attractors. Using adaptive feedback control, we established a new control law for fully synchronising the proposed chaotic financial systems with itself. To conclude, some numerical simulation was presented using MATLAB to illustrate the efficiency of the designed method. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Backstepping control Chaos Financial chaotic system Synchronisation

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Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework

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IFAC-PapersOnLine 2022年第23期55卷 92-97页

作者： Sebastián Espinel-Ríos Nicolas Huber Edgar Alberto Alcalá-Orozco Bruno Morabito Thomas F.T. Rexer Udo Reichl Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Cell-free biosynthesis uses the machinery of cells, such as the metabolic reactions, to carry out conversion processes in vitro. This can be more beneficial than in vivo approaches like fermentations. Some advantages of these synthetic biology processes include higher product yields, rates and titers, and more flexibility in pathway design. Cell-free biosynthesis is still in early stages and, unlike in vivo production, there are very few examples of model-based optimization. Moreover, we encounter static optimizations in most cases, neglecting the dynamic nature of the processes. We present an optimal control framework to maximize the efficiency of cell-free biosynthesis. We focus on fed-batch setups as they allow enhancing the reaction rates via the feeding, extending production processes for longer times, and minimizing the potential negative effects of enzyme kinetics with substrate inhibition. Our framework can in principle handle several cost functions and exploit both static and dynamic degrees of freedom. An aspect that can hinder model-based optimization is model uncertainty, which can arise due to uncertain parameters, oversimplified model assumptions or unknown reaction mechanisms. To counteract this, we propose the use of model predictive control during the process operation. In addition, we outline the use of moving horizon estimation as an observer in the case of unmeasured states. We consider the de novo cell-free synthesis of uridine diphosphate-N-acetylglucosamine as a biomedical relevant case study, where we were able to maximize the volumetric productivity in simulations, and indirectly also the titer and enzyme efficiency use.

关键词： Cell-free fed-batch optimization control state estimation UDP-GlcNAc

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Abnormal Test Data Diagnosis and Prognosis Based on Least Squares Support Vector Machine

Abnormal Test Data Diagnosis and Prognosis Based on Least Sq...

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2020 IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2020

作者： Han, Huilian Cui, Zhaojing National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

To figure out the shortage of traditional fault diagnosis method, the paper carried out fault diagnosis and forecast by detecting abnormal data based on LS-SVM algorithm. SVM has the optimal solution and good generalization ability in small samples and can avoid falling into local minimum. The paper introduced the principle of SVM, made up the model of non-linear least squares support vector regression solve various breakdowns coexist of actual circumstance, then identified and prognosis abnormal data by using high- dimension feature vector space as the input of LS-SVM. Examples showed that abnormal data could be detected effectively. The results proved that the model of fault diagnosis and prediction effective and has higher rate of correct judgments. © 2020 IEEE.

关键词： Data mining

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Soft sensor for monitoring dynamic changes in cell composition

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IFAC-PapersOnLine 2022年第23期55卷 98-103页

作者： Sebastián Espinel-Ríos Bruno Morabito Katja Bettenbrock Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia co li’ s metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens.

关键词： Cell composition soft sensor full information estimation resource balance analysis dynamic enzyme-cost flux balance analysis

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Quick unsupervised hyperspectral dimensionality reduction for earth observation: A comparison

arXiv

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arXiv 2024年

作者： Lupu, Daniela Garrett, Joseph L. Johansen, Tor Arne Orlandic, Milica Necoara, Ion Automatic Control and System Engineering Department National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Center for Autonomous Marine Operations and Systems Norwegian University of Science and Technology Trondheim Norway Department of Engineering Cybernetics Norway Department of Electronic Systems Norway G. Mihoc-C. Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. Seven unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection. The experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline. © 2024, CC BY.

关键词： Remote sensing

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Design and Implementation of Dynamic Reconfiguration Based on Heterogeneous Arrow control Platform

Design and Implementation of Dynamic Reconfiguration Based o...

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International Conference on Electronic Measurement and Instruments, ICEMI

作者： Ran Tao Gang Xiang Yue Luo Kun Tian Hui Li Zhaolei Wang Beijing Aerospace Automatic Control Research Institute Beijing China Beijing Inst. Of Electronic System Engineering Beijing China

ISBN: (纸本)9781665444927

Traditional rocket control computing platform use hardware redundancy more ways to promote the reliability, the same hardware redundancy of common cause failure, unable to cope with complex environment against the shortcomings, is proposed based on heterogeneous architecture arrows control platform, by using SOPC, FPGA and DSP three core components to build a new generation of control computing platform, has high reliability, strong adaptability to environment, etc. With SOPC as the core, the heterogeneous platform completes data transmission through high-speed serial transmission, supports dynamic logical reconstruction, and utilizes dynamic reconstruction technology to realize self-recovery under abnormal state. In this paper, the platform architecture, data transmission and synchronization methods, dynamic reconstruction strategy and control methods are described in detail. Finally, the correctness of the platform design is verified by experiments and simulations.

关键词： Rockets Instruments Redundancy Computer architecture Hardware Reliability Data communication

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Model Predictive Purge control for PEM Fuel Cell systems with Anode Recirculation

Model Predictive Purge Control for PEM Fuel Cell Systems wit...

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IEEE Conference on Decision and control

作者： Michael Hauck Felix Petzke Stefan Streif Faculty for Electrical Engineering and Information Technology Automatic Control & System Dynamics Lab at Technische Universität Chemnitz Chemnitz Germany

ISBN: (纸本)9781665436601

Fuel cell systems that utilize anode recirculation generally require a purge process to remove accumulated gaseous impurities from the anode recirculation system. Especially the accumulation of nitrogen leads to a decrease of the cell voltage and therefore a reduced stack efficiency. However, unconsumed hydrogen is lost during the purge process, resulting in a decrease of hydrogen utilization. Therefore, an optimal purge control can help to maximize the overall system *** order to determine and predict the influence of the purge valve opening on the system efficiency with respect to the hydrogen utilization and the stack efficiency we develop a control-oriented model of a PEMFC anode recirculation system. We then set up a model predictive purge controller and compare its performance to two standard purge strategies using the NEDC vehicle test cycle.

关键词： Impurities Conferences Hydrogen Fuel cells Predictive models Valves Nitrogen

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MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Hekai Yao Xuetao Zhang Gang Sun Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China College of Artificial Intelligence The Institute of Robotics and Automatic Information System (IRAIS) Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

关键词： Laser radar Accuracy Uncertainty Probabilistic logic Distortion Robustness Sensors Odometry Distortion measurement State estimation

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

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