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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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Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems

arXiv

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arXiv 2021年

作者： Verhoek, Chris Tóth, Roland Haesaert, Sofie Koch, Anne Control Systems Group Eindhoven University of Technology Netherlands Institute for Computer Science and Control Hungary Institute for Systems Theory and Automatic Control University of Stuttgart Germany

Based on the Fundamental Lemma by Willems et al., the entire behaviour of a Linear Time-Invariant (LTI) system can be characterised by a single data sequence of the system as long the input is persistently exciting. This is an essential result for data-driven analysis and control. In this work, we aim to generalise this LTI result to Linear Parameter-Varying (LPV) systems. Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems’. Based on an LPV representation, i.e., embedding, of nonlinear systems, this allows the application of the Fundamental Lemma for systems beyond the linear class. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear systems

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Learning to control Linear Systems can be Hard

arXiv

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arXiv 2022年

作者： Tsiamis, Anastasios Ziemann, Ingvar Morari, Manfred Matni, Nikolai Pappas, George J. Automatic Control Laboratory ETH Zurich Switzerland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Electrical and Systems Engineering University of Pennsylvania United States

In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior results state that the statistical difficulty for both benchmarks scales polynomially with the system state dimension up to system-theoretic quantities. However, this does not reveal the whole picture. By utilizing minimax lower bounds for both benchmarks, we prove that there exist non-trivial classes of systems for which learning complexity scales dramatically, i.e. exponentially, with the system dimension. This situation arises in the case of underactuated systems, i.e. systems with fewer inputs than states. Such systems are structurally difficult to control and their system theoretic quantities can scale exponentially with the system dimension dominating learning complexity. Under some additional structural assumptions (bounding systems away from uncontrollability), we provide qualitatively matching upper bounds. We prove that learning complexity can be at most exponential with the controllability index of the system, that is the degree of underactuation. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

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Fixed-time convergent control barrier functions for coupled multi-agent systems under STL tasks

arXiv

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arXiv 2021年

作者： Sharifi, Maryam Dimarogonas, Dimos V. Department of Automatic Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper presents a control strategy based on a new notion of time-varying fixed-time convergent control barrier functions (TFCBFs) for a class of coupled multi-agent systems under signal temporal logic (STL) tasks. In this framework, each agent is assigned a local STL task regradless of the tasks of other agents. Each task may be dependent on the behavior of other agents which may cause conflicts on the satisfaction of all tasks. Our approach finds a robust solution to guarantee the fixed-time satisfaction of STL tasks in a least violating way and independent of the agents’ initial condition in the presence of undesired violation effects of the neighbor agents. Particularly, the robust performance of the task satisfactions can be adjusted in a user-specified way. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Intelligent fault diagnosis based on multi-resolution and one-dimension convolutional neural networks

Intelligent fault diagnosis based on multi-resolution and on...

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2021 International Conference on System science and Engineering, ICSSE 2021

作者： Liu, Po-Yi Chen, Chih-Cheng Liong, Sze-Teng Tsai, Ming-Han Hsieh, Ping-Cheng Wang, Kun-Ching Feng Chia University Department of Mechanical and Computer-Aided Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung Taiwan

ISBN: (纸本)9781665448482

Intelligent fault diagnosis (IFD) plays an important role to increase the safety and reliability of rotating machinery. In recent years, there is a large number of deep-learning-based algorithms applied to IFD. Many studies have shown that the one-dimension convolutional neural network (1D-CNN) performs well in fault diagnosis on original vibration signals. However, the original vibration signals are very complicated and difficult to analyze directly. Some critical information is often hidden in different frequency subbands, such as some fault state is only related to specific subbands. In order to enhance the learning progression well organized for the 1D-CNN model, the proposed approach is based on wavelet packet decomposition (WPD) and 1D-CNN. First, decompose the original vibration signal into different frequency subbands with WPD to make the signals more concise. Next, to be compatible with the 1D-CNN model, we proposed a novel 1D-CNN with multi-resolution (CNN-MR) and the experiment result according to Case Western Reserve University (CWRU) Bearing Data Center. Through CNN-MR, it achieves high accuracy for fault diagnosis. © 2021 IEEE.

关键词： Failure analysis

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Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems

Fundamental Lemma for Data-Driven Analysis of Linear Paramet...

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IEEE Conference on Decision and control

作者： Chris Verhoek Roland Tóth Sofie Haesaert Anne Koch Control Systems Group Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Hungary Institute for Systems Theory and Automatic Control University of Stuttgart Germany

ISBN: (纸本)9781665436601

关键词： Linear systems Conferences Nonlinear systems

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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

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International Conference on computer Vision and Graphics, ICCVG 2020

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Do We Run Large-scale Multi-Robot Systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling

arXiv

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arXiv 2023年

作者： Kuckling, Jonas Luckey, Robin Avrutin, Viktor Vardy, Andrew Reina, Andreagiovanni Hamann, Heiko The Department of Computer and Information Science University of Konstanz Konstanz78457 Germany The Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany The Institute of Computer Engineering University of Lübeck Lübeck Germany The Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany The Department of Computer Science The Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John's Canada IRIDIA Université Libre de Bruxelles Brussels Belgium

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning. © 2023, CC BY.

关键词： Multipurpose robots

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Adaptive Identification of Linear Systems with a Mix of Static and Time-Varying Parameters

SSRN

引用

SSRN 2022年

作者： Niedźwiecki, Maciej Gańcza, Artur Shen, Lu Zakharov, Yuriy the Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Gdá nsk University of Technology Gdańsk80-233 Poland Department of Electronic Engineering University of York United Kingdom

We consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics, for instance, in applications requiring identification of the acoustic channel, such as underwater acoustic communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identifying time-varying systems. In this paper, we further improve the performance of the fLBF algorithm by exploiting properties of the system. Specifically, we propose an adaptive time-invariance test to identify whether a particular system tap is static or time-varying and exploit this knowledge for choosing the number of basis functions. We also propose a regularization scheme that exploits the system sparsity and an adaptive technique for estimating the regularization parameter. Finally, a debiasing technique is proposed to reduce an inherent bias of fLBF estimates. The high performance of the fLBF algorithm with the proposed techniques is demonstrated in scenarios of underwater acoustic communications, using numerical and real experiments. © 2022, The Authors. All rights reserved.

关键词： Continuous time systems

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