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arXiv 2024年

作者： Ruszczak, Bogdan Kotowski, Krzysztof Evans, David Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska Str. 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka Str. 16 Gliwice44-100 Poland KP Labs Bojkowska Str. 37J Gliwice44-100 Poland European Space Agency ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT—a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22–23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way. © 2024, CC BY.

关键词： Anomaly detection

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Foreword

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2020年 554卷 v-vi页

作者： Zelinka, Ivan Brandstetter, Pavel Dao, Tran Trong Duy, Vo Hoang Kim, Sang Bong Department of Computer Science Faculty of Electrical Engineering VŠB-TUO Poruba Ostrava Czech Republic Department of Electronics Faculty of Electrical Engineering VŠB-TUO Ostrava Czech Republic International Cooperation Research and Training Institute Ton Duc Thang University Ho Chi Minh Viet Nam Department of Automatic Control Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Mechanical Design Engineering Pukyong National University Busan Korea Republic of

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Hanan Hussein Al-asadi Maria Goga Haider Abdullah Ali Ramona Cristina Popa Awodele Oludele Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest Romania University of Technology-Iraq Baghdad Iraq UTCB Department for Training of Teaching Personnel Technical University of Civil Engineering of Bucharest Bucharest Romania Madenat Alelem University College Baghdad Iraq Faculty of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest Romania computer science department Babcock University Ogun State Nigeria

ISBN: (纸本)9781665460156

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy.

关键词： Smart agriculture Machine learning algorithms Costs Green buildings Soil control systems Agriculture

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Clustering in block Markov chains

arXiv

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arXiv 2017年

作者： Sanders, Jaron Proutière, Alexandre Yun, Se-Young Kth Royal Institute of Technology School of Electrical Engineering Dept. of Automatic Control Osquldasväg 10 Stockholm10044 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Dept. of Quantum and Computer Engineering Mekelweg 4 Delft2628CD Netherlands Korea Advanced Institute of Science and Technology Dept. of Industrial and Systems Engineering 291 Daehak-ro Eoeun-dong Yuseong-gu Korea Republic of

This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper we devise a clustering procedure that accurately, efficiently, and provably detects the clusters. We first derive a fundamental information-theoretical lower bound on the detection error rate satisfied under any clustering algorithm. This bound identifies the parameters of the BMC, and trajectory lengths, for which it is possible to accurately detect the clusters. We next develop two clustering algorithms that can together accurately recover the cluster structure from the shortest possible trajectories, whenever the parameters allow detection. These algorithms thus reach the fundamental detectability limit, and are optimal in that sense. Copyright © 2017, The Authors. All rights reserved.

关键词： Asymptotic analysis

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A lyapunov-based small-gain theorem for infinite networks

arXiv

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arXiv 2019年

作者： Kawan, Christoph Mironchenko, Andrii Swikir, Abdalla Noroozi, Navid Zamani, Majid Institute of Informatics Ludwig Maximilian University of Munich Germany Faculty of Computer Science and Mathematics University of Passau Passau94032 Germany Department of Electrical and Computer Engineering Technical University of Munich Germany Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Magdeburg Germany Computer Science Department University of Colorado BoulderCO80309 United States Computer Science Department Ludwig Maximilian University of Munich Germany

This paper presents a small-gain theorem for networks composed of a countably infinite number of finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. The effectiveness of our result is illustrated through several examples including nonlinear spatially invariant systems with sector nonlinearities and a road traffic *** Codes 37B25, 37L15, 93D05, 93A15 Copyright © 2019, The Authors. All rights reserved.

关键词： Large scale systems

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A review on effects of the atmospheric turbulence on laser beam propagation — An analytic approach

A review on effects of the atmospheric turbulence on laser b...

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International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Mircea Hulea Xuan Tang Zabih Ghassemlooy Sujan Rajbhandari Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania Optical Communications Research Group Northumbria University Newcastle United Kingdom Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fujian China School of Computing Electronics and Mathematics Coventry University United Kingdom

ISBN: (纸本)9781509025275

The main effects of atmospheric turbulence on the laser beams propagation are scintillation, beam wandering, and phase front distortion. The first two can be compensated simply by using aperture averaging, whereas the later, which is of high importance in high data rates free space optics communications, is much more complex. This paper gives a review of principal analytical methods used to simulate and characterize the optical beam propagation through the turbulence channel focussing on phase front distortion. The paper presents simple models suitable for computer simulations of beam wandering and scintillation. This is followed by more complex theoretical approaches in analysing the turbulence induced phase front distortion and the calculation of the mutual coherence function of partially coherent beams propagating through an inhomogeneous medium. The theoretical analysis of beam coherence and phase front distortion due to turbulence represents the first step for elaboration of the mitigation methods.

关键词： Laser beams Optical beams Atmospheric modeling Phase distortion Optical distortion Refractive index

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Learning dynamical systems using local stability priors

arXiv

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arXiv 2020年

作者： Mehrjou, Arash Iannelli, Andrea Schölkopf, Bernhard Empirical Inference Department of Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zürich Faculty of Electrical Engineering Zürich Switzerland Department of Electrical Engineering Automatic Control Lab ETH Zürich8092 Switzerland Empirical Inference Department of Max Planck Institute for Intelligent Systems Tübingen Germany

A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed. The nonlinear identification leverages the local stability information as a prior on the system, effectively endowing the estimate with this important structural property. In addition, the knowledge of the region of attraction plays an experiment design role by informing the selection of initial conditions from which trajectories are generated and by enabling the use of a Lyapunov function of the system as a regularization term. Numerical results show that the proposed method allows efficient sampling and provides an accurate estimate of the dynamics in an inner approximation of its region of attraction. Copyright © 2020, The Authors. All rights reserved.

关键词： Lyapunov functions

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Retrospective Vision of a Long Term Innovative Experience

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Procedia - Social and Behavioral Sciences 2014年 141卷 15-19页

作者： Jose Manuel Lopez-Guede Manuel Graña Fernando Oterino Jesus Maria Larrañaga Department of Systems Engineering and Automatic Control University College of Engineering of Vitoria Basque Country University (UPV/EHU) Vitoria 01006 Spain Department of Computer Science and Artificial Intelligence Faculty of Informatics Basque Country University (UPV/EHU) San Sebastian 20018 Spain Department of Electronics University College of Engineering of Vitoria Basque Country University (UPV/EHU) Vitoria 01006 Spain Department of Business Management University College of Engineering of Vitoria Basque Country University (UPV/EHU) Vitoria 01006 Spain

The purpose of this paper is to look back on a long term informal innovative experience carried out during the last nine years until the present one, and try to discern, with a retrospective and a long time vision, whether it has been as beneficial as expected once it has concluded. This experience has taken place in the University College of engineering of Vitoria (Basque Country University, Spain) since the year 2.004, and it has been accomplished in the subject Company Networks of Technical engineering in Management Computing. After the analysis of a number of data collected along these years, we found that we have reached the initial objective of increasing the enrolment in the subject that has driven the project, and other unexpected ones that are more important from a long time point of view. Once that nine years have lapsed and the project is going to be finished, we conclude that it is a very positive experience and we recommend doing the same with the new degrees of the European Higher Education Area (EHEA), as we plan.

关键词： Company Networks Active Learning Educational Innovation Project Cooperative Learning

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Semi-supervised active transfer learning for fetal ECG arrhythmia detection

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computer Methods and Programs in Biomedicine Update 2023年 3卷

作者： Mohebbian, Mohammad Reza Marateb, Hamid Reza Wahid, Khan A. Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon S7N 5A9 SK Canada Biomedical Engineering Department Engineering Faculty University of Isfahan Isfahan Iran Biomedical Engineering Research Centre (CREB) Automatic Control Department (ESAII) Universitat Politècnica de Catalunya-Barcelona Tech (UPC) Barcelona Spain

Deep learning has demonstrated excellent results for ECG anomaly detection, wherein most approaches used supervised learning. The requirement of thousands of manually annotated samples is a concern for state-of-the-art anomaly detection systems, especially for fetal ECG (FECG), and currently, there is not a publicly available FECG dataset annotated for each FECG beat. In this paper, we offer a modified active learning technique based on transfer learning, calibration probability, and autoencoder-based sampling to reduce number of samples requires to annotate. In this regard, we used 25,000 s of recording from 47 patients from the MIT-BIH Arrhythmia Database to train a deep learning model to detect anomalies in non-fetus subjects. Then we used the unlabeled Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) of 26 subjects to fine-tune the trained model to fine-tune the trained model based on active learning to detect anomalies in binary form for fetal. A variational autoencoder is trained on all data (adult and fetal ECG), and clustering is applied to latent features extracted from data after dimension reduction. Then, the sampling process of active learning selected samples from different clusters with low confidence to cover all data distribution. Moreover, a probability calibration based on mc-dropout and isotonic regression is used to calibrate confidences, helping to select reliable low-confidence samples. Various ablation studies were performed to show the influence of autoencoder-based sampling, calibration, and transfer learning, which showed that the proposed method could achieve 92% accuracy using 399 training samples. In contrast, other methods required more training samples to reach the same level of accuracy without calibration or an autoencoder and clustering approach or training without active learning. The study also found that transfer learning significantly impacted faster convergence and that the proposed active learning approach was more effect

关键词： Active learning Anomaly Arrhythmia FECG Transfer learning

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Educational Innovation Project in the Field of Industrial Informatics

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Procedia - Social and Behavioral Sciences 2014年 141卷 20-24页

作者： Jose Manuel Lopez-Guede Manuel Graña Jesus Maria Larrañaga Fernando Oterino Department of Systems Engineering and Automatic Control University College of Engineering of Vitoria Basque Country University (UPV/EHU) Vitoria 01006 Spain Department of Computer Science and Artificial Intelligence Faculty of Informatics Basque Country University (UPV/EHU) San Sebastian 20018 Spain Department of Business Management University College of Engineering of Vitoria Basque Country University (UPV/EHU) Vitoria 01006 Spain Department of Electronics University College of Engineering of Vitoria Basque Country University (UPV/EHU) Vitoria 01006 Spain

The objective of this paper is to describe an ongoing work that we are carrying out in the frame of an educational innovation project supported by the Education Advisory Service of the Vicerectorship for Teaching Quality and Innovation (University of the Basque Country, Spain). The project is driven by means of the subject of Industrial Informatics of the degree of Industrial Electronic engineering and automatics (University College of engineering of Vitoria, Basque Country University, Spain). The design of the project is based on the development of a collaborative work distributed among all the students of the subject, grouped in several teams or working groups. The work developed by each of those groups is complementary, and when all these parts of work are finished, all of them will fit together. The conclusion reached until this moment through the continuous tracking of the students is that there is an increased and more active attendance. Besides, the results reached by the students in different evaluations have improved those reached by means of the traditional paradigm of learning.

关键词： Industrial Informatics Active Learning Educational Innovation Project Cooperative Learning

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