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arXiv 2021年

作者： Rusu, Cristian The Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Romania Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest Romania

In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Singular value decomposition

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Fuzzy-logic adaptive control of traffic in an urban junction

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

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2019年第2期81卷 61-74页

作者： Nae, Andrei C. Dumitrache, Ioan Faculty of Automatic Control and Computer Science University POLITEHNICA Bucharest Romania Faculty of Automatic Control and Computer Science University POLITEHNICA Bucharest Romania

Traffic light system plays a critical role in solving congestion in current urban traffic environment, where moving from pre-programed traffic lights systems to traffic adapted systems is a natural development. In this paper we present a new traffic control concept using fuzzy logic adaptive control for traffic light timing strategies to minimize the time vehicles spend in the intersection, as an optimization criteria in an Intelligent Transportation System. We also propose simulation tools to enable in-detail analysis for adaptive traffic light system based on road sensing and several strategies for pre-programed and adaptive stroke concepts. © 2019, Politechnica University of bucharest. All rights reserved.

关键词： Fuzzy logic

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Multiple-image super-resolution reconstruction using deep learning: A Sentinel-2 case study

Multiple-image super-resolution reconstruction using deep le...

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IAF Earth Observation Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Kawulok, Michal Tarasiewicz, Tomasz Ziaja, Maciej Tyrna, Diana Kostrzewa, Daniel Nalepa, Jakub KP Labs Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

ISBN: (纸本)9781713843016

Super-resolution reconstruction embraces a variety of techniques aimed at generating a high-resolution image from a low-resolution input. The goals of super-resolution range from hallucination that consists in producing a visually-attractive high-resolution image to reconstructing the real high-resolution information that is commonly required in Earth observation scenarios. The latter can be achieved by fusing a number of images with the same georeference, captured at a different time. Such multi-image super-resolution solutions underpinned with deep learning have been recently proposed for enhancing images acquired with the Proba-V sensor. This satellite captures images of 100 m and 300 m ground sampling distance, which makes it relatively easy to collect sufficient amounts of real-world data that can be used for training deep convolutional neural networks. As such data are unavailable for most of other satellites, alternative ways for training the networks must be adopted. In this paper, we demonstrate our solution for super-resolving multispectral Sentinel-2 images, and we present its most important components that may help implement multi-image super-resolution for other satellites. Our main focus is on the data that are used for training, as well as on the low-resolution images that are presented for reconstruction. We expect that the reported techniques will allow for increasing the capabilities of using Sentinel-2 images in a variety of practical Earth observation scenarios, but even more importantly, the presented methodology may be helpful in exploiting multiple-image SR for enhancing images captured with other satellites. Copyright © 2021 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Satellites

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Closed loop systems

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Analysis of selected dielectric parameters of insulating liquids

Analysis of selected dielectric parameters of insulating liq...

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International Scientific Conference on Electric Power Engineering (EPE)

作者： Aleksandra Płużek Łukasz Nagi Michał Kozioł Małgorzata Zygarlicka Department of Electrical Power and Renewable Energy Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

During the last few years, there has been a growing interest in the topic of using natural or synthetic esters as an alternative to mineral oils in oil transformers due to the easier way to obtain them and their ability to exhibit better electrical properties. In addition, more and more researchers are describing in their work that doping esters with nanoparticles is capable of further improving these properties. Therefore, in the current work, it was decided to see what effect the addition of a hydrophobic carbon shell iron nanoparticle to ORLEN mineral oil and Midel 7131 synthetic ester would have on selected electrical parameters of the insulating fluid, i.e. breakdown voltage and electrical strength.

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On the Impact of Noisy Labels on Supervised Classification Models 23rd

On the Impact of Noisy Labels on Supervised Classificatio...

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23rd International Conference on Computational science, ICCS 2023

作者： Dubel, Rafal Wijata, Agata M. Nalepa, Jakub Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Biomedical Engineering Silesian University of Technology Roosevelta 40 Zabrze41-800 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland

ISBN: (纸本)9783031360206

The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. They benefit from annotated training sets which are fed to the training routine which elaborates a model that is further deployed for a specific task. The process of capturing real-world data may lead to acquring noisy observations, ultimately affecting the models trained from such data. The impact of the label noise is, however, under-researched, and the robustness of classic learners against such noise remains unclear. We tackle this research gap and not only thoroughly investigate the classification capabilities of an array of widely-adopted machine learning models over a variety of contamination scenarios, but also suggest new metrics that could be utilized to quantify such models’ robustness. Our extensive computational experiments shed more light on the impact of training set contamination on the operational behavior of supervised learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Active disturbance rejection control for unmanned tracked vehicles in leader-follower scenarios: discrete-time implementation and field test validation

arXiv

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arXiv 2023年

作者： Amokrane, Salem-Bilal Laidouni, Mohammed Zouaoui Adli, Touati Madonski, Rafal Stanković, Momir Military Academy University of Defence Belgrade Serbia Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

This paper presents a systematic design of an active disturbance rejection control (ADRC) system for unmanned tracked vehicles (UTVs) in leaderfollow formation. Two ADRC controllers are designed for the lateral and the longitudinal channels of the UTV based on control errors in the cross-track and the along-track directions. Through simulations, the proposed ADRC approach is first shown to outperform the conventional PI/PID controllers in scenarios involving sudden changes in the leader motion dynamics, slippage disturbances, and measurement noise. Then, a comprehensive experimental validation of the proposed leader-follower control is performed using a laboratory UTV equipped with a camera and laser sensors (to enable the calculation of error signals). In order to provide more effective interaction between the human (leader) and the UTV (follower) during the leader-follower task, a camera-based subsystem for human pose recognition is developed and deployed. Finally, the experimental results obtained outdoors demonstrate that the proposed ADRC-based leader-follower UTV control system achieves high tracking capabilities, robustness against slippage disturbances, and adaptability to changing environmental conditions. © 2023, CC BY-NC-ND.

关键词： controllers

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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On the use of dictionary learning in time series imputation 28

On the use of dictionary learning in time series imputation

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Zheng, Xiaomeng Dumitrescu, Bogdan Liu, Jiamou Giurcaneanu, Ciprian Doru Department of Statistics University of Auckland Auckland New Zealand Department of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9789082797053

In this work, we show how dictionary learning (DL) can be employed in the imputation of univariate and multivariate time series. In the multivariate case, we propose to use a structured dictionary. The size of the dictionary and the sparsity level are selected by information theoretic criteria. We also evaluate the effect of removing the trend/seasonality before applying DL. We conduct an extensive experimental study on real-life data. The positions of the missing data are simulated by applying two strategies: (i) sampling without replacement, which leads to isolated occurrences of the missing data, and (ii) sampling via Polya urn model that is likely to produce long sequences of missing data. In all scenarios, the novel DL-based methods compare favorably with the state-of-the-art. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Time series

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