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23rd International Conference on control Systems and Computer Science Technologies, CSCS 2021

作者： Pastravanu, Octavian Matcovschi, Mihaela-Hanako Technical University 'Gheorghe Asachi' of Iasi Department of Automatic Control and Applied Informatics Iasi700050 Romania

ISBN: (纸本)9781665439398

The paper proves a new sufficient condition for the stability of switched linear systems with mode-dependent average dwell-time (MDADT). This condition is formulated in general terms of matrix-measure inequalities that are correlated with the properties of the switching signals. We show that our result is able to incorporate, as particular cases, several approaches separately reported by the literature of the previous years. The mode-independent average dwell-time (ADT) can also be addressed as a context derived from our main result. A numerical example illustrates the theoretical development proposed in the first part, including some studies on stability analysis for MDADT and ADT, respectively. © 2021 IEEE.

关键词： Linear systems

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Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language

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Procedia Computer Science 2024年 246卷 3447-3456页

作者： Elena-Beatrice Nicola Dumitru-Clementin Cercel Florin Pop Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics (ICI) Bucharest Romania Academy of Romanian Scientists Bucharest Romania

Offensive language detection is a crucial task in today’s digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, building robust offensive language detection models requires large amounts of labeled data, which can be expensive and time-consuming to obtain. Semi-supervised learning ofers a feasible solution by utilizing labeled and unlabeled data to create more accurate and robust models. In this paper, we explore a few different semi-supervised methods, as well as data augmentation techniques. Concretely, we implemented eight semi-supervised methods and ran experiments for them using only the available data in the RO-Offense dataset and applying five augmentation techniques before feeding the data to the models. Experimental results demonstrate that some of them benefit more from augmentations than others.

关键词： Semi-Supervised Learning Romanian Language Offensive Language Data Augmentation

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Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups

Investigating Large Language Models for Complex Word Identif...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Smădu, Răzvan-Alexandru Ion, David-Gabriel Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia National University of Science and Technology POLITEHNICA Bucharest Faculty of Automatic Control and Computers Bucharest Romania National Institute for Research & Development in Informatics - ICI Bucharest Bucharest Romania Academy of Romanian Scientists Bucharest Romania Paris 1 Panthéon-Sorbonne University Paris France University of Bucharest Bucharest Romania

ISBN: (纸本)9798891761643

Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5turbo and GPT-4o, in the CWI, LCP, and MWE settings. We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods. In addition, we provide some views on meta-learning combined with prompt learning. In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Constrained Predictive Current control in dq Frame for a Permanent Magnet Synchronous Machine

Constrained Predictive Current Control in dq Frame for a Per...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Madalin Costin Corneliu Lazar Dept.of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iasi Iasi Romania Dept. of Automatic Control and Electrical Engineering "Dunarea de Jos" University of Galati Galati Romania

In the paper, the design and Simulink implementation of a predictive current control structure in dq frame for a permanent magnet synchronous machine (PMSM) are presented. Starting from the multivariable model of the currents in the dq reference frame, first it is decoupled and through the rejection of the disturbance introduced by the back-EMF, two single-input single-output (SISO) systems result whose dynamics are generated by R-L circuits. For these SISO systems, the model predictive control (MPC) algorithms are designed that allow the consideration of physical limitations through constraints The performances of the current control structure with MPC algorithms are compared with those obtained with the conventional PI controllers.

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Analysis of message flow transmissions for an inter-vehicle communication scenario 14

Analysis of message flow transmissions for an inter-vehicle ...

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14th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2022

作者： Militaru, Andreea V. Lazar, Razvan G. Caruntu, Constantin F. Comsa, Ciprian R. Bogdan, Ion Gheorghe Asachi Technical University of Iasi Department of Telecommunications and Information Technologies Iasi Romania Gheorghe Asachi Technical University of Iasi Department of Automatic Control and Applied Informatics Iasi Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

Mobility in urban areas evolved massively in recent decades, leading in a continuous manner to traffic difficulties and significantly increasing the number of accidents, especially in crowded intersections. While much attention has been paid to controlling individual vehicles, recently the wireless communication networks were integrated as main ingredients for cooperation between vehicles with the other traffic participants and smart infrastructure. Thus, the fifth-generation (5G) communication technology promises an increase of the capacity for the existent cellular network, a faster connection, an ultra-low latency, and higher reliability. Moreover, device-to-device (D2D) communication is an essential feature when a large amount of devices have to be connected. The main advantage of D2D communication is the direct exchange of information between devices, without having to send the signal through the base station. Hence, this paper uses the Simu5G simulator to explore the D2D communication in a vehicle communication scenario by analysing the quality of the network. For this, the network performance was evaluated by monitoring the Channel Quality Indicator (CQI) and the message flow during the movement of the cars. The obtained results are in accordance with the real-time operation of the 5G technology. © 2022 IEEE.

关键词： 5G mobile communication systems

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Pneumonia Detection Using Federated Learning

Pneumonia Detection Using Federated Learning

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International Conference on control Systems and Computer Science (CSCS)

作者： Camelia-Raluca Farkaş Radu-Ioan Ciobanu Ciprian Dobre Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics Bucharest Romania

Pneumonia is a major cause of illness and death among older adults, and hospitalization rates for pneumonia in patients aged 65 and older have been increasing. However, detecting pneumonia automatically is challenging because it requires large amounts of data from many patients, making traditional machine learning solutions impractical in real life, despite the good results they can achieve. To address this issue, our paper focuses on using federated learning, a new method for applying machine learning techniques in a distributed environment while maintaining patients’ privacy and reducing communication and storage overhead at the processing servers. We propose a federated learning system that can analyze X-rays of patients with and without pneumonia, present our approach, and demonstrate promising results.

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A Computation Offloading Framework for Android Devices in a Mobile Cloud

A Computation Offloading Framework for Android Devices in a ...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Vlad-Florin Ilie Mihail-Andrei Bănică Radu-Ioan Ciobanu Ciprian Dobre Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics Bucharest Romania

Mobile devices have become a widespread commodity with an increasing number of applications. The resources needed by those applications have also been increasing disproportionately with the mobile phones’ battery capacity and computation power. The solution for this problem is to offload computation to both neighboring mobile devices and to remote servers. The Drop Computing paradigm proposes a few ways of implementing such a solution. The objective of this paper is thus to implement the Drop Computing paradigm as an Android framework that can then be used by third-party developers in their applications in order to leverage computation offloading. The experimental results performed on multiple Android devices show that the framework fairs well with computation problems that have small-sized or constant inputs.

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Ensuring Data Privacy in AC Optimal Power Flow with a Distributed Co-Simulation Framework

arXiv

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arXiv 2024年

作者： Dai, Xinliang Kocher, Alexander Kovačević, Jovana Dindar, Burak Jiang, Yuning Jones, Colin Çakmak, Hüseyin Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory École Polytechnique Fédérale de Lausanne Switzerland

During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, often lacks numerical convergence guarantees or is limited to single-machine numerical simulation. To address this, we present a distributed approach for solving AC Optimal Power Flow (OPF) problems within a geographically distributed environment. This involves integrating the energy system Co-Simulation (eCoSim) module in the eASiMOV framework with the convergence-guaranteed distributed optimization algorithm, i.e., the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). Comprehensive evaluations across multiple system scenarios reveal a marginal performance slowdown compared to the centralized approach and the distributed approach executed on single machines—a justified trade-off for enhanced data privacy. This investigation serves as empirical validation of the successful execution of distributed AC OPF within a geographically distributed environment, highlighting potential directions for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Improvement of Random Undersampling to Avoid Excessive Removal of Points from a Given Area of the Majority Class 21st

Improvement of Random Undersampling to Avoid Excessive Remov...

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21st International Conference on Computational Science, ICCS 2021

作者： Bach, Malgorzata Werner, Aleksandra Department of Applied Informatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783030779665

In this paper we focus on class imbalance issue which often leads to sub-optimal performance of classifiers. Despite many attempts to solve this problem, there is still a need to look for better ones, which can overcome the limitations of known methods. For this reason we developed a new algorithm that in contrast to traditional random undersampling removes maximum k nearest neighbors of the samples which belong to the majority class. In such a way, there has been achieved not only the effect of reduction in size of the majority set but also the excessive removal of too many points from the given area has been successfully prevented. The conducted experiments are provided for eighteen imbalanced datasets, and confirm the usefulness of the proposed method to improve the results of the classification task, as compared to other undersampling methods. Non-parametric statistical tests show that these differences are usually statistically significant. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Multivariable control in General Anesthesia: A Comparative Study of Model Predictive and Fractional-Order Strategies for Effect-Site Concentration and Hemodynamic Regulation

Multivariable Control in General Anesthesia: A Comparative S...

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International Conference on Computational Cybernetics (ICCC)

作者： Erwin T. Hegedüs Anca Maxim Marcian D. Mihai Isabela R. Birs Nicoleta Badau Cristina I. Muresan Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

Managing anesthesia and hemodynamic regulation during surgical procedures poses significant challenges due to patient-specific variability and the complex interactions of administered drugs. While advanced monitoring systems and Target-controlled Infusion (TCI) devices enhance precision in drug delivery, their reliance on manual adjustments limits real-time adaptability. This study investigates two advanced control strategies–centralized Model Predictive control (MPC) and decentralized fractional-order control–within a multivariable framework designed to regulate hypnosis, analgesia, and hemodynamic stabilization. Virtual patient simulations were used to evaluate these approaches under diverse anesthetic and, notably, hemodynamic conditions. The findings reveal that MPC delivers superior precision in maintaining the Bispectral Index (BIS) but tends to apply more aggressive adjustments to hemodynamic variables. In contrast, fractional-order control ensures smoother responses, though its ability to handle disturbances is less robust in certain scenarios. This study highlights the trade-offs between these strategies and advocates for further exploration of hybrid solutions to enhance patient safety and surgical outcomes.

关键词： Stability criteria Surgery Anesthesia Regulation Real-time systems Hemodynamics Safety Reliability Transient analysis Predictive control

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