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Application of the Dynamic Iterative Learning control to the Heteroplanar Active Magnetic Bearing

IFAC-PapersOnLine 2020年第2期53卷 1511-1516页

作者： Lukasz Hladowski Arkadiusz Mystkowski Krzysztof Galkowski Eric Rogers Bing Chu Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland Faculty of Electrical Engineering Bialystok University of Technology ul.Wiejska 45c 15-351 Bialystok Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

Heteroplanar active magnetic bearings have numerous applications, where one example is a high-temperature gas-cooled reactors. Rotor imbalance, however, may cause problems for critical parts of the system in the form of repetitive periodic vibrations. This is known problem and periodic component extraction is widely used in active magnetic bearing unbalance control laws. More recently, iterative learning control has been considered as an alternative and this paper gives new results on this approach. In particular, a new control law in the 2D systems setting is developed and the results of a simulation based study using the model of a test rig are given, where such a study is an essential step prior to experimental validation.

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Local search algorithms for memetic algorithms: understanding behaviors using biological intelligence

Local search algorithms for memetic algorithms: understandin...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Beatrice Luca Mitica Craus Department of Computer Science Faculty of Automatic Control and Computer Engineering Iasi Romania

Memetic Algorithms (MAs) are a class of stochastic global search heuristics in which Evolutionary Algorithms (EAs) - based approaches are combined usually with heuristic local searches. This hybridization is meant to reach solutions that would otherwise be unreachable by evolution or a local method alone. In this work, we propose three Local Search (LS) algorithms for hybridization with an existing Evolutionary Algorithm with Pareto ranking in order to define biological intelligence using the concepts of useful and utility and therefore to zoom on the basin of attraction of promising realistic solutions. Our experimental results with these memetic algorithms in the game of Checkers show how we can learn the organization of behaviors into paths of behaviors of different lengths and frequencies and then reveal the true nature of these behaviors.

关键词： Correlation Sociology Memetics Optimization Ice

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Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses 3rd

Automated Prediction of Student Participation in Collaborati...

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3rd International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2018

作者： Pasov, Iulia Dascalu, Mihai Nistor, Nicolae Trausan-Matu, Stefan Faculty of Automatic Control and Computer Science University "Politehnica" Bucharest 313 Splaiul Independenţei Bucharest60042 Romania Faculty of Psychology and Educational Sciences Ludwig-Maximilians-Universität Leopoldstr. 13 Munich80802 Germany Academy of Romanian Scientists Splaiul Independentei 54 Bucharest050094 Romania Richard W. Riley College of Education and Leadership Walden University 100 Washington Avenue South Suite 900 MinneapolisMN55401 United States

ISBN: (纸本)9783319920214

The massive student participation in computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students’ participation using two models - a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students’ pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 =. 796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Students

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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

arXiv

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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Microgrid control simulation and testing

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2018年第2期80卷 57-68页

作者： Puianu, Mihaela Flangea, Ramona-Oana Arghira, Nicoleta Iliescu, Sergiu Stelian Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania Dept. of Automatic Control and Industrial Informatics University POLITEHNICA of Bucharest Romania

Microgrid control becomes more and more developed with the application of this concept on a larger scale. This paper defines the modern microgrid concept and evaluates the possible architectures and control hierarchy. A small scale microgrid architecture is proposed. The components of the system are a wind turbine, a photovoltaic panel and a household. The model was implemented in Matlab/Simulink and a decision table control was proposed. The proposed decisions and actions are defined for both on and off grid operation, in order to benefit most of the local resources. The results of this study are promising in terms of microgrid off grid operation for single dwellings, considering that the house is grid supplied for a period of only 4 hours of the day. © 2018 Politechnica University of Bucharest. All rights reserved.

关键词： Decision tables

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Flooded Area Segmentation from UAV Images Based on Generative Adversarial Networks

Flooded Area Segmentation from UAV Images Based on Generativ...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Dan Popescu Loretta Ichim Florin Stoican Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Romania Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest

The detection, localization and evaluation of small flooded areas can contribute to decrease the economical damages of such disasters. The cheapest and most accurate method is to segment the aerial images taken from UAV. In this paper, we propose a new method for detection of regions of interest, like flooding in rural areas, using Generative Adversarial Networks (GAN) and Graphics Processing Units (GPU). The classical GPU is used to create, by parallel calculation of textural features, extracted from the co-occurrence matrix, the supervised mask of flood segmentation in the images from the learning set. Based on these images and their associated real masks, the weights of the generator and discriminator are established. A set of 40 images were used for the learning phase and another set of 60 images were used for method validation. The results demonstrate that the proposed method provide a high accuracy and robustness, comparing with other papers for flooding evaluation. Even if it is a relative long time to learn the GAN, in the operational phase the time for image segmentation process is very short.

关键词： Image segmentation Gallium nitride Floods Instruction sets Graphics processing units Generative adversarial networks Generators

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Fog-Based Solution for Real-Time Monitoring and Data Processing in Manufacturing

Fog-Based Solution for Real-Time Monitoring and Data Process...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Stefan Mocanu Giorgiana Geampalia Oana Chenaru Radu Dobrescu Faculty of Automatic Control and Computer Science Politehnica University Bucharest Romania

Today's industry is facing an increased need for implementing intelligent manufacturing solutions, capable of integrating existing machinery with new technologies. Current solutions do not provide support for analyzing vast amounts of data. Also, implementation of cloud architectures proved inappropriate for real-time or near real-time processing and control because of the network latency. Under these considerations, the new paradigm of fog computing provides promising characteristics enabling greater scalability, fast reaction time and increasing security through a local private processing cloud structure. This paper evaluates the integration capabilities between existing technologies with new devices for seamless integration of the fog computing paradigm and provides an architecture solution for this upgrade.

关键词： Cloud computing Real-time systems computer architecture Manufacturing processes Process control Wireless sensor networks

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CNN based on LBP for Evaluating Natural Disasters

CNN based on LBP for Evaluating Natural Disasters

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Andrada Livia Cirneanu Dan Popescu Loretta Ichim Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Romania Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest

This paper presents a novel evaluation method of areas affected by natural disasters with the purpose of managing these crisis situations. Since it is necessary to have a real overview of a specific area in the shortest time, our methodology proposes a neural network with backpropagation approach for flood detection from UAV images. For this, the Local Binary Pattern (LBP) texture operator is used for areas classification. The LBP operator labels each pixel of the analyzed image by comparing it with its neighbors, which ends with the computation of a binary number that it is converted to decimal format named LBP code. Thus, based on the generated LBP codes, a histogram type feature is computed and used in both training and testing phases of the proposed neural network. Over 50 images obtained with the aid of UAV technology were tested with the proposed neural network and good results in terms of accuracy for flood areas detection were obtained.

关键词： Training Biological neural networks Histograms Backpropagation Testing Training data

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Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study ⁎

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IFAC-PapersOnLine 2019年第1期52卷 772-777页

作者： Anna Stief James R. Ottewill Jerzy Baranowski ABB Corporate Research Center ul. Starowislna 13a 31-038 Krakow Poland Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 30-059 Krakow Poland

Feature design and selection is one of the first steps towards successful fault detection and diagnosis. Data from different sources can contain complimentary information about a monitored system. Hence methods which fuse features from multiple sources can often detect and diagnose a greater number of fault modes with higher confidence. However, solutions that require data from multiple sensors as inputs can be susceptible to failure if one or more of those sensors cease to function. Optimally a solution will fuse data from a sufficient number of sensors so that the advantages of sensor fusion are realized, while the robustness of the system is retained. In this paper the authors investigate how the best subset of features might differ for fault detection and fault severity diagnosis in a multiphase flow facility case study. ReliefF, which is a K-nearest neighbors-based feature selection filter, is used to rank the features for different problems. The dataset used for the analysis contains data from various operating conditions and induced faults with various severities. It was found that the optimal subset of features varied for different monitoring problems. It was also shown that including features that are ranked as being uninformative into a fault classifier can also impact the robustness of the classifier to sensor failures.

关键词： Signal processing Feature extraction Feature selection Process monitoring

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Performance Analysis of Dual-Hop Mixed FSO/mmWave Systems

Performance Analysis of Dual-Hop Mixed FSO/mmWave Systems

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IEEE International Conference on Communications Workshops, ICC

作者： Yan Zhang Jiayi Zhang Liang Yang Dinh-Thuan Do Bo Ai School of Electronic and Information Engineering Beijing Jiaotong University Beijing China College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

The combination of free-space optical (FSO)-based backhaul links and millimeter wave (mmWave)-based access links is considered as a key enabling technique to provide enhanced mobile broadband in ultra dense networks. In this paper, the generalized end-to-end performance of a dual-hop mixed FSO/mmWave relaying system is evaluated. The FSO link is assumed to follow the Gamma-Gamma fading with pointing error impairments, while the mmWave link experiences the fluctuating two-ray fading. Both heterodyne detection and indirect modulation/direct detection are accounted for in the FSO link. Using both amplify-and-forward and decode-and-forward relaying schemes, novel closed-form expressions for the outage probability and average bit error probability (BER) are derived in terms of bivariate Fox's H-functions. Furthermore, an asymptotic analysis for the outage probability and the average BER is provided to show the diversity order and other important engineering insights. Finally, the accuracy of our derived results is validated through Monte-Carlo simulations.

关键词： Radio frequency Fading channels Signal to noise ratio Relays Optical transmitters Power system reliability Probability

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