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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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High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift control for Battery Charger of Electric Vehicles

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Energies 2024年第2期17卷 354页

作者： Kuo, Shih-Hao Chiu, Huang-Jen Chiang, Che-Wei Huang, Ta-Wei Chang, Yu-Chen Bachman, Serafin Piasecki, Szymon Jasinski, Marek Turzyński, Marek Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Institute of Control and Industrial Electronics Warsaw University of Technology Warsaw00-662 Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk80-233 Poland

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal modulation scheme, ensuring the circuit’s efficiency over the entire range in the realm of a high-power and high-efficiency battery charger for electric vehicles. The convergent approach to TPS mode selection also reduces the numerous cases for small-signal analysis through general average modeling. After verifying the small-signal models under various voltage transfer ratios and load conditions to verify the stability, a converter prototype with a rated power of 15 kW is built and tested. Thus, a peak efficiency of 97.7% can be achieved. © 2024 by the authors.

关键词： Charging (batteries)

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BigFeat: Scalable and Interpretable Automated Feature Engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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IEEE International Conference on Big Data

作者： Hassan Eldeeb Shota Amashukeli Radwa ElShawi Institute of Computer Science University of Tartu Tartu Estonia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt.

ISBN: (纸本)9781665480468

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.

关键词： Measurement Industries Scalability Pipelines Buildings Machine learning Big Data

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Design and Realization of Unmanned Vehicle Autonomous Navigation System Based on Binocular Camera

Design and Realization of Unmanned Vehicle Autonomous Naviga...

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computer science, Electronic Information Engineering and Intelligent control Technology (CEI), IEEE International Conference on

作者： Tianxing Guo Lianlei Lin Huanyu Liu Junbao Li Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Aiming at the navigation problem of unmanned vehicles in extreme environments such as communication interference and limited GPS signals, this study proposes an autonomous navigation method based on binocular cameras. The method enables the unmanned vehicle to complete the task of localization and map building using only binocular images, without the need for other sensors or signal sources. Meanwhile, this study also proposes a local path planning obstacle avoidance method based on depth map, which, when combined with global localization information, can take into account both local obstacle avoidance and global path navigation, and can cope with random environmental changes. The system uses a single sensor for autonomous obstacle avoidance and navigation, which reduces the computational requirements and ensures the low cost of the navigation system. In order to verify the reliability and effectiveness of the system under signal-constrained conditions, the system is evaluated in a simulation environment and a real field scenario, respectively. The experimental results show that the system is able to achieve reliable localization and path planning under signal-constrained conditions.

关键词： Location awareness Navigation Reliability theory Path planning Sensor systems Sensors Collision avoidance

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MATLAB Toolbox for fractional-variable-order closed loop systems with a digital controller

MATLAB Toolbox for fractional-variable-order closed loop sys...

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

作者： Piotr Oziablo Dorota Mozyrska Mariusz Matusiak Faculty of Computer Science Bialystok University of Technology Wiejska 45A Poland Faculty of Electrical Electronics Computer Science and Control Engineering Lodz University of Technology Lodz Poland

ISBN: (纸本)9781665476881

In the paper, there is presented a MATLAB Toolbox that allows simulation and parameter tuning of closed-loop systems with fractional-variable-order digital PID (FVOPID) controllers. The proposed toolbox provides MATLAB Simulink blocks of FVOPID based on two different implementations of Grünwald-Letnikov fractional-variable-order operator. An additional part of the toolbox is an application with a GUI interface which primary purpose is to simulate and find the optimal tuning parameters of FVOPID for a given closed-loop system. The tuning process, in this case, can be performed employing different optimization methods set to minimize an objective function according to the selected integral criterion. The paper describes the internal architecture (design) of the toolbox and its functionalities.

关键词： Software packages Optimization methods Programming Linear programming Calculus Closed loop systems Tuning

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Identification of Fast Time-varying Communication Channels Using the Preestimation Technique

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IFAC-PapersOnLine 2021年第7期54卷 351-356页

作者： Maciej Niedźwiecki Artur Gańcza Piotr Kaczmarek Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Gdańsk University of Technology ul. Narutowicza 11/12 Gdańsk Poland

Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application - identification of fast time-varying acoustic channels used in underwater communication.

关键词： identification of time-varying systems underwater acoustic communication channels local basis function approach regularization leave-one-out cross-validation

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The OPS-SAT benchmark for detecting anomalies in satellite telemetry

arXiv

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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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Horizontal scaling method for a hyperconverged network

Horizontal scaling method for a hyperconverged network

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2021 International Conference on Information and Digital Technologies, IDT 2021

作者： Kovalenko, Andriy Kuchuk, Heorhii Kuchuk, Nina Kostolny, Jozef Kharkiv National University of Radio Electronics Faculty of Computer Engineering and Control Kharkiv Ukraine National Technical University KhPI Faculty of Computer and Information Technologies Kharkiv Ukraine Faculty of Management Science and Informatics Univeraity of Zilina Zilina Slovakia

ISBN: (纸本)9781665436922

Today cloud platforms are gradually being replaced by hyperconverged. With a hyperconverged infrastructure, the servers, networks, storage and computing power are combined. This is done through specific software. The main advantage of hyperconverged network is to reduce operating costs. However, system performance can be reduced due to the standardization of hardware modules and centralization of control, and, consequently, QoS deteriorates. At the same time, companies that operate computer systems are attracted by the ability to quick horizontal scaling and lower operating costs. Hyperconverged networks often use addition of a new hardware modules to meet QoS requirements using a simple horizontal scaling implementation. The purpose of the paper is to develop such a horizontal scaling method, which allows decreasing transaction processing time while total cost of data transmission does not exceed the specified values. The method also allows determining the optimal characteristics of additional hardware. © 2021 IEEE.

关键词： Resource allocation

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Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data

arXiv

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arXiv 2022年

作者： Gudyś, Adam Sikora, Marek Wróbel, Lukasz Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Institute of Innovative Technologies EMAG Leopolda 31 Katowice40-189 Poland

Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on separate and conquer - a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme provide contrast sets describing same examples with different attributes, distinguishing presented approach from the standard separate and conquer. The algorithm was also generalized for regression and survival data allowing identification of contrast sets whose label attribute/survival prognosis is consistent with the label/prognosis for the predefined contrast groups. This feature, not provided by the existing approaches, further extends the usability of RuleKit-CS. Experiments on over 130 data sets from various areas and detailed analysis of selected cases confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. The algorithm was implemented as a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://***/adaa-polsl/RuleKit). © 2022, CC BY-NC-ND.

关键词： Diagnosis

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Parallelised Hybrid Heuristics in Numerical Linear Algebra for Data science

SSRN

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SSRN 2023年

作者： Ulmeanu, Vlad Adrian Ulmeanu, Anatoli Paul Faculty of Automatic Control and Computer Science Polytechnic University of Bucharest Splaiul Independentei 313 060042 Romania Polytechnic University of Bucharest EG-ELa-EH Buildings 313 Splaiul Independentei Bucharest060042 Romania

Although the numerical linear algebra algorithms are very popular in many applied areas, the quantification of their time complexities and the convergence analysis have not been widely covered in the literature. We illustrate by different types of numerical methods selected on relevant criteria, such as hybrid or entropy variants, that are identified, presented, and implemented as efficient algorithms on parallel computers. This paper reveals how the time and complexity of the algorithms are defined and quantified and shows the usefulness of these concepts for the design of efficient large linear solvers by illustrating them in dense matrix computations. One of the aims of this study is the observation of parallel tasks, which are of numerical nature and can arise in many interdisciplinary engineering approaches and consequently in many programming problems, as well as finding and analyzing general algorithms in linear algebra, which are useful in many solutions involving the big data paradigm. We also demonstrate how to evaluate the worth of such methods, how to prove their competitiveness, and how they can be mutated to allow parallel computations, as well as challenging and beating methods used for benchmarking in this aspect. © 2023, The Authors. All rights reserved.

关键词： Convergence of numerical methods

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