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Active Disturbance Rejection Control for 3D Crane Systems

Procedia computer Science 2024年 242卷 976-983页

作者： Raul-Cristian Roman Radu-Emil Precup Emil M. Petriu Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 300223 Timişoara Romania Center for Fundamental and Advanced Tech. Res. Romanian Academy – Timisoara Br. Bd. Mihai Viteazu 24 300223 Timisoara Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard Ottawa ON K1N 6N5 Canada

Active Disturbance Rejection Control (ADRC) is a data-driven algorithm that offers a promising approach for robust control design. This paper investigates the effectiveness of first-order and second-order ADRC for 3D cranes. To compare the results fairly and reduce the heuristics in parameter tuning, the ADRC parameters were optimally computed via the African Vultures Optimization Algorithm. Both controllers based on ADRC are validated using experiments on a 3D crane laboratory system and their design and performance evaluation are done by minimizing the sum of squared errors. The results demonstrate the robustness of the ADRCs. This comparative analysis highlights the advantages and limitations of both control strategies, providing valuable insights for selecting the appropriate ADRC order for specific applications.

关键词： 3D crane systems ADRC data-driven control optimization

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GWO-based Modeling of an Unstable Transport System 9th

GWO-based Modeling of an Unstable Transport System

引用

9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Gale-Cazan, Cristiana-Bogdana Bojan-Dragos, Claudia-Adina Precup, Radu-Emil Roman, Raul-Cristian Petriu, Emil M Szedlak-Stinean, Alexandra-Iulia Politehnica University of Timisoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 Timisoara300223 Romania Center for Fundamental and Advanced Technical Research Romanian Academy Timisoara Branch Bd. Mihai Viteazu 24 Timisoara300223 Romania University of Ottawa School of Electrical Engineering and Computer Science 800 King Eduard OttawaONK1N 6N5 Canada

The goal of this paper is to obtain optimal models of an unstable transport system, which is a nonlinear process represented by the two-wheeled unstable transport system. An optimization problem is defined in order to ideally minimize and practically reduce the differences of the outputs of the real-time laboratory equipment with respect to the outputs of the nonlinear model. The parameters of the nonlinear models are optimally tuned using a recent metaheuristic optimization algorithm, namely the Grey Wolf Optimizer, which solves three optimization problems. A comparison of the responses of the real-time laboratory equipment and the derived optimal nonlinear models is carried out in various simulation scenarios, which are discussed and analyzed. © 2022 The Authors. Published by Elsevier B.V.

关键词： Optimization

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A Secure and Trustworthy Biometric Data Ecosystem for Cross-border Suspect Identification

A Secure and Trustworthy Biometric Data Ecosystem for Cross-...

引用

IEEE International Conference on Big Data

作者： Katerina Kyriakou Apostolos Apostolaras Polychronis Velentzas Georgios Benos Konstantinos Koutsoukos Chrysostomos Symvoulidis Kaitai Liang Zeshun Shi Asterios Leonidis Kyriaki Miniadou Eleni Veroni Spyridon Evangelatos Georgios Th. Papadopoulos Thanasis Korakis Department of Electrical and Computer Engineering University of Thessaly Volos Greece CERTH The Centre for Research & Technology Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft The Netherlands Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Crete Greece Research & Innovation Development Netcompany-Intrasoft S.A. Luxembourg Luxembourg Dept. of Electronic Engineering Hellenic Mediterranean University Crete Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

关键词： Biometrics Law enforcement Space technology Terrorism Ecosystems Regulation Blockchains Resource management Spectrogram Indexing

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An ISM-Band RFID Module for Simultaneous Wireless Information and Power Transfer with High Gain Antenna in Multi-Layer Configuration

An ISM-Band RFID Module for Simultaneous Wireless Informatio...

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European Conference on Antennas and Propagation, EuCAP

作者： David Chatzichristodoulou Giacomo Paolini Photos Vryonides Dimitra Psychogiou Diego Masotti Alessandra Costanzo Symeon Nikolaou Frederick Research Center Nicosia Cyprus RF and Microwave Solutions LTD Cyprus DEI "Guglielmo Marconi" University of Bologna Bologna Italy Dept. Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus School of Engineering University College Cork Cork Ireland Tyndall National Institute Cork Ireland DEI "Guglielmo Marconi" University of Bologna Cesena Italy

This paper presents an implementation of an ISM RFID module capable of simultaneous wireless information and power transfer, that is implemented in a three-layer configuration. The top two layers of the RFID module are used for the low profile, high gain (8 dBi), circular polarization antenna implementation. The proposed antenna consists of four radiating L-shaped patches on the top layer, which are fed from a circular power divider. The energy harvesting (EH) system and the communication module are implemented on the bottom third layer. A four-port coupler is used to direct 85-90% of the received power from the antenna to the EH system and the remaining to the communication module. TI CC2530 IC is used, and it is powered from the PMU of the EH system. With -13 dBm input power, at the coupler’s port 3, the communication module can support TX/RX communication cycle lasting up to 3.5 ms.

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An Efficient Localization for Indoor Environment using Classification Algorithms

An Efficient Localization for Indoor Environment using Class...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Mulahuwaish, Aos Towne, Wyatt Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Saginaw Valley State University Dept. of Computer Science and Information Systems Michigan United States Salahaddin University-Erbil Dep. of Software Engineering and Informatics Iraq Koya University Faculty of Engineering Dept. of Software Engineering Kurdistan Region-F.R. Iraq

ISBN: (纸本)9781728176246

The positioning accuracy of various kinds of devices is often calculated by using Wi-Fi fingerprints as a data source of Received Signal Strength (RSS). Through different matching algorithms, these RSS values are related to corresponding positioning coordinates for positioning estimation. In this paper, we compare two well known Machine Learning (ML) structures for calculating positioning accuracy: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) and a Decision Tree (DT). Both matching algorithms run through a series of datasets in two parts. In the first part, the datasets contain raw, non-normalized RSS values. In the second part, the RSS values are all normalized to remove noise in the datasets. After extensive testing and simulations, the tests showed that as long as RSS values are first normalized, a Decision Tree offers better average positioning accuracy. © 2020 IEEE.

关键词： Decision trees

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Spatial photonic reservoir computing based on non-linear phase-to-amplitude conversion in micro-ring resonators

arXiv

引用

arXiv 2021年

作者： Mesaritakis, Charis Sozos, Kostas Dermanis, Dimitris Bogris, Adonis University of the Aegean Dept. of Information and Communication Systems Engineering Palama 2 Karlovassi Samos83200 Greece University of West Attica Dept. of Informatics and Computer Engineering. Aghiou Spiridonos Egaleo Athens12243 Greece

We present a photonic reservoir computing, relying on a non-linear phase-to-amplitude mapping process, able to classify in real-time multi-Gbaud time traces subject to transmission effects. This approach delivers an a... 详细信息

关键词： Optical resonators

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A Dynamic Fuzzy Neural System for Time Series Classification

A Dynamic Fuzzy Neural System for Time Series Classification

引用

International IEEE Conference on Intelligent Systems, IS

作者： George Kandilogiannakis Paris Mastorocostas Costas Hilas Dept. of Informatics and Computer Engineering University of West Attica Athens Greece Dept. of Computer Informatics & Telecommunications Engineering International Hellenic University Serres Greece

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

A dynamic fuzzy neural system is proposed, for time series anomaly detection. The model is entitled BFuzzTiD (Block-diagonal Fuzzy Time-series Detector) and consists of fuzzy rules whose consequent parts are three-layer small recurrent neural networks. The hidden layer of each network has blocks of neurons that feed back to each other. BFuzzTid is trained by the Dynamic Resilient Propagation algorithm. The model learns the dynamics of the time series such that it can classify them by detecting the anomaly points. A comparative analysis is conducted with a series of time series anomaly detection models, in order to investigate the capabilities of the proposed detector.

关键词： Time series analysis Anomaly detection Neurons Computational modeling Detectors Recurrent neural networks Training

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

引用

Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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Coherent VCSEL Network Computing 27

Coherent VCSEL Network Computing

引用

27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Chen, Zaijun Sludds, Alexander Davis, Ronald Christen, Ian Ateshian, Lamia Bernstein, Liane Heuser, Tobias Heermeier, Niels Lott, James Reitzenstein, Stephan Hamerly, Ryan Englund, Dirk Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Fakultät Ii Institut für Festkörperphysik Sekretariat Technische Universität Berlin Berlin10623 Germany Ntt Research Inc. Physics & Informatics Laboratories SunnyvaleCA94085 United States

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Deep learning

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Estimating leverage scores via rank revealing methods and randomization

arXiv

引用

arXiv 2021年

作者： Sobczyk, Aleksandros Gallopoulos, Efstratios IBM Research Zurich Saümerstrasse 4 Rüschlikon Switzerland HPCLab Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece

We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms. We first develop a set of fast novel algorithms for rank estimation, column subset selection and least squares preconditioning. We then describe the design and implementation of leverage score estimators based on these primitives. These estimators are also effective for rank deficient input, which is frequently the case in data analytics applications. We provide detailed complexity analyses for all algorithms as well as meaningful approximation bounds and comparisons with the state-of-the-art. We conduct extensive numerical experiments to evaluate our algorithms and to illustrate their properties and performance using synthetic and real world data sets. © 2021, CC BY-NC-ND.

关键词： Data Analytics

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