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International Conference on Communications (COMM)

作者： Eugen Pop Daniela Gî fu Automatics and Industrial Informatics Department Faculty of Automatic Control and Computers University &#x201C Politehnica&#x201D of Bucharest R&#x0026 D Institute for Automation-IPA SA Bucharest Romania Institute of Computer Science of Romanian Academy - Iasi Branch Faculty of Computer Science of Alexandru Ioan Cuza University of Iasi Iasi Romania

E-business offers to economic agents the opportunity to increase the efficiency of their activity, promote and offer their products and services on a large and diversified market. Often a single service, separately, does not meet all the needs of various categories and groups of customers. In such a situation, service composition can provide more technical, commercial, informational facilities than each separate component service can offer. There are many web-based frameworks used for e-business, but still with limitations. The paper presents a Services Composition Framework and a wise algorithm for e-business models, based on REST and SOAP web services. It is intended to be used for the development of e-business models, comprising stock market exchange, e-commerce, e-banking, etc. The customers can access the framework services, through Smartphones, on mobile and wireless communication channels. Within a Quality-of-Service performance analysis of the e-business Service Composition Framework, the response time and throughput for multiuser access simultaneous requests were evaluated. A stock market exchange service is considered, as an e-business service example, while bucharest Stock Exchange information is the digital content source for data transactions.

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Set-theoretic fault-diagnosis for a nonlinear real-word fluidic benchmark

Set-theoretic fault-diagnosis for a nonlinear real-word flui...

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European control Conference (ECC)

作者： Florin Stoican Janetta Culiţă Sorin Olaru Faculty of Automation Control and Computer Science University Politehnica of Bucharest Romania L2S lab of CentraleSupelec Gif sur Yvette France

Fault diagnosis is essential in managing abnormal changes in system characteristics which affect its functioning and control abilities. The manuscript provides a set-based fault diagnosis for a fluidic benchmark. Invariant sets are employed to characterize the steady-state behavior of the open-loop stable dynamics for sensor faults. It is shown that the set-based fault diagnosis theory integrates this model and is able to handle efficiently persistent fault events.

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Development of an Executive Design of a Station for Measuring the Curvature of Metallurgical Charge for Rolling 21st

Development of an Executive Design of a Station for Measurin...

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21st Polish control Conference, PCC 2023

作者： Placzek, Marek Krauze, Piotr Ryfa, Arkadiusz Jablonka, Tomasz Balys, Karol Helmich, Magdalena Badora, Dawid Florkiewicz, Bartosz Polak, Kamil Slonka, Piotr Trybulowski, Mateusz Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Energy and Environmental Engineering Silesian University of Technology Konarskiego 18 Gliwice44-100 Poland MOSTOSTAL ZABRZE Biprohut S.A Dubois 16 Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The work is a report on the implementation of interdisciplinary group project classes conducted in accordance with the Project Based Learning approach. The aim of the project was to develop an automated station for measuring the curvature of metallurgical charge and to create its technical documentation. The station is intended to work in cooperation with devices of an automated line for spreading and transporting metallurgical charges, which is a part of the technological line for hot rolling. The task of the station is to identify the curvature value, so that a charge with exceeded parameters does not proceed to the next stage of the process (the goal is significant in terms of eliminating defects). The final outcome of the project was to be a complete technical documentation of the designed station along with the measurement algorithm. Additionally, a laboratory test station and a system design for use in real industrial conditions were developed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hot rolling

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A Quantitative Research to Determine User's Requirements for the Mind-controlled Prosthesis Arm Intelligent System 13

A Quantitative Research to Determine User's Requirements for...

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13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Ali, Haider Abdullah Goga, Nicolae Vasilateanu, Andrei Ali, Liwa Abdullah Abd-Almuhsen, Ghufran Saady Naji, Hasan K. University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania Al-Emamain Al Kadhimain Medical City Department of Neurosurgery Baghdad Iraq Madenat Alelem University College Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9781665425346

Brain-computer Interface (BCI) identifies user thinking through different electrophysiological signals. These signals are detected through electrodes placed on the scalp, and the neuronal activity of the cortical is collected by electrodes implanted inside the brain. Regardless of reasons, limbs losses affect a large percentage of the world's people yearly. The necessity for solutions for those having these conditions can be related to health issues and research. Myoelectric and other types of prostheses are currently in use, but it has some drawbacks like the high-price, heaviness, and the dependency on the intact nerves. These drawbacks can be overcome using electroencephalography (EEG) mind-controlled prosthesis arm. The mind-controlled prosthesis arm uses EEG non-invasive technique which provides powerful help for disabled persons in their daily activities, particularly to help arm amputees move their prosthesis arm easily. In this paper, we present a quantitative questionnaire to identify and validate the user's functional and non-functional requirements and to know how users want to interact with the system. We aim to build inexpensive, lightweight, and smart functional prosthesis arm according to functional and non-functional requirements to meet users' goals and requirements. The participants' interests and recommendations are considered while designing and building a smart functional prosthesis arm system. This is one of the very few surveys that handle the user's requirements of a mind-controlled prosthesis arm according to the amputees and specialists' opinions. © 2021 IEEE.

关键词： Electroencephalography

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Mini-batch stochastic subgradient for functional constrained optimization

arXiv

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arXiv 2024年

作者： Singh, Nitesh Kumar Necoara, Ion Kungurtsev, Vyacheslav Automatic Control and Systems Engineering Department University Politehnica Bucharest Spl. Independentei 313 Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania Computer Science Department Czech Technical University Karlovo Namesti 13 Prague12135 Czech Republic

In this paper we consider finite sum composite convex optimization problems with many functional constraints. The objective function is expressed as a finite sum of two terms, one of which admits easy computation of (sub)gradients while the other is amenable to proximal evaluations. We assume a generalized bounded gradient condition on the objective which allows us to simultaneously tackle both smooth and nonsmooth problems. We also consider the cases of both with and without a strong convexity property. Further, we assume that each constraint set is given as the level set of a convex but not necessarily differentiable function. We reformulate the constrained finite sum problem into a stochastic optimization problem for which the stochastic subgradient projection method from [17] specializes to a collection of mini-batch variants, with different mini-batch sizes for the objective function and functional constraints, respectively. More specifically, at each iteration, our algorithm takes a mini-batch stochastic proximal subgradient step aimed at minimizing the objective function and then a subsequent mini-batch subgradient projection step minimizing the feasibility violation. By specializing different mini-batching strategies, we derive exact expressions for the stepsizes as a function of the mini-batch size and in some cases we also derive insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime. We also prove sublinear convergence rates for the mini-batch subgradient projection algorithm which depend explicitly on the mini-batch sizes and on the properties of the objective function. Numerical results also show a better performance of our mini-batch scheme over its single-batch counterpart. © 2024, CC BY.

关键词： Constrained optimization

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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2023 Progress in Applied Electrical Engineering, PAEE 2023

作者： Pelc, Mariusz Mikolajewski, Dariusz Ruotsalo, Tuukka Leiva, Luis A. Sudol, Adam Gorzelanczyk, Edward Jacek Lysiak, Adam Kawala-Sterniuk, Aleksandra Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Faulty of Natural and Technical Sciences University of Opole Opole Poland University of Opole Faculty of Natural and Technical Sciences Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence. © 2023 IEEE.

关键词： Machine learning

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Community Clustering: A Smart Social Group-Based Mobile Cloud Computation Offloading Solution

SSRN

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SSRN 2023年

作者： Toader, Sergiu-Cristian Ciobanu, Radu-Ioan Dobre, Ciprian Marin, Radu-Corneliu Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest 313 Splaiul Independent ¸ei sector 6 Bucharest060042 Romania National Institute for Research and Development in Informatics 8-10 Maresal Averescu Avenue sector 1 Bucharest011455 Romania

The proliferation of Internet-connected mobile devices has been a driving force in the evolution of mobile computing. Battery life and computational limitations inherent to these devices have steered mobile applications towards the cloud computing paradigm. However, this shift has exacerbated issues such as data congestion, slow transfer speeds, and subpar user experiences due to the increased volume of data transferred over the Internet. To alleviate the dependence of mobile devices on the cloud, various alternative network technologies and collaborative computing algorithms have been proposed. This paper introduces one such algorithm, entitled Community Clustering, grounded in dynamic short-term network establishment. It serves as an alternative mobile computing solution, emphasizing collaborative task computation across all devices. Tasks are intelligently routed to devices with lower workloads, employing optimal battery-preserving transfer technologies. Within this paper, we present a practical scenario of this algorithm, and, through an empirical evaluation through simulations, we show that collaborative computing can reduce cloud usage by 67%, while still ensuring a satisfactory user experience. © 2023, The Authors. All rights reserved.

关键词： Mobile computing

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Methods and tools for automatic or semi-automatic recognition of selected emotions using machine learning algorithms

Methods and tools for automatic or semi-automatic recognitio...

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Progress in Applied Electrical Engineering (PAEE)

作者： Piotr Schneider Dariusz Mikołajewski Anna Bryniarska Magdalena Igras-Cybulska Artur Cybulski Walery Marcinowicz Maciej Janiszewski Aleksandra Kawala-Sterniuk Department of Neuroinformatics and Biomedical Engineering Institute of Computer Science Maria Curie-Sklodowska University Lublin Poland Hishoo Sp. z o.o Gdynia Poland Faculty of Computer Science Kazimierz Wielki University Bydgoszcz Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

automatic or semi-automatic recognition of selected emotions using Machine Learning (ML) algorithms requires practical, fast and accurate implementation of the steps involved in acquiring the actual signals (e.g. speech, facial expression, posture and body movements), recording them in the form of an optimal feature vector/matrix, correctly interpreting the above data and reproducing and generalising this process within the ML system. Depending on the accuracy and speed required and the degree of its ‘invasiveness’ (e.g. by attaching sensors to the user’s body), single or multi-modal interpretations of biometric signals associated with different groups of emotions are possible. This group of studies, despite their relative non-invasiveness, requires the caution prescribed by the Artificial Intelligence (AI) Act and RODO Act and the possibility of being replaced (if necessary) by alternatives. This article aims to analyse the opportunities and risks associated with this group of technologies, with a particular focus on directions for further development.

关键词： Emotion recognition Machine learning algorithms Accuracy Video on demand Speech recognition Machine learning Feature extraction Recording Convolutional neural networks Web sites

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Machine Learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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RuleKit 2: Faster and simpler rule learning

arXiv

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arXiv 2025年

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

Rules offer an invaluable combination of predictive and descriptive capabilities. Our package for rule-based data analysis, RuleKit, has proven its effectiveness in classification, regression, and survival problems. Here we present its second version. New algorithms and optimized implementations of those previously included, significantly improved the computational performance of our suite, reducing the analysis time of some data sets by two orders of magnitude. The usability of RuleKit 2 is provided by two new components: Python package and browser application with a graphical user interface. The former complies with scikit-learn, the most popular data mining library for Python, allowing RuleKit 2 to be straightforwardly integrated into existing data analysis pipelines. RuleKit 2 is available at GitHub under GNU AGPL 3 license (https://***/adaa-polsl/RuleKit). © 2025, CC BY-NC-ND.

关键词： Graphical user interfaces

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