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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic Science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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A Machine-Learning Approach for the Development of a FOWT Model Integrated with Four OWCs 26

A Machine-Learning Approach for the Development of a FOWT Mo...

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26th International Conference on Circuits, systems, Communications and computers, CSCC 2022

作者： Ahmad, Irfan Mzoughi, Fares Aboutalebi, Payam Garrido, Izaskun Garrido, Aitor Automatic Control Group-ACG Department of Automatic Control and Systems Engineering University of Basque Country- Upviehu Bilbao Spain

ISBN: (纸本)9781665481861

The wind-wave excitations cause structural vibrations on the Floating Offshore Wind Turbines (FOWT) pressing the power generation efficiency and reducing the life expectancy. In particular, tower-top displacement and barge-type platform pitch dynamics are extremely sensitive to wind speed and wave elevation to the point that may even lead to structural instability in extreme conditions. Having into account that computational techniques such as Artificial Neural Networks (ANNs) are widely used in artificial intelligence because of their strong predicting and forecasting capabilities, the aim of this article is to create a deep-layer ANN model that incorporates Oscillating Water Columns (OWCs) into the barge platform. This ANN model enables to address stability issues of the hybrid floating offshore wind platform. The proposed control-oriented model has been successfully validated to achieve adequate dynamic behavior and structural performance using FAST. © 2022 IEEE.

关键词： Wind

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DNN-based Skeleton Independent Motion Retargeting 27

DNN-based Skeleton Independent Motion Retargeting

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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A Review on Defense Mechanism against the Denial of Service and False Data Injection in Cyber-Physical Power systems 6

A Review on Defense Mechanism against the Denial of Service ...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Zhou, Zhuoran Zhang, Jinning Zhang, Xin University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom School of Engineering University of Leicester Leicester United Kingdom

ISBN: (纸本)9798350346671

Cyber-physical power system (CPPS), with its bi-directional power and information flows, is considered as the next generation of widely distributed and automated electrical power network. However, CPPS is vulnerable to malicious cyberattacks that can cause significant operational risk and financial loss to stakeholders, such as electricity system operators. The complexity of vulnerabilities, which arises from the heterogeneity and interdependency of physical and cyber domains, will present substantial challenges for the detection and prevention of cyberattacks. This paper provides a comprehensive review of the defense mechanisms in CPPS against coordinated and widespread cyberattacks. The most prevalent cyberattacks in the CPPS are denial-of-service (DoS) and false data injection (FDI) attacks. This paper examines the state-of-the-art prevention, detection, and mitigation technologies for these major mainstreams and coordinated cyberattacks. In particular, the advantages and disadvantages of popular cyberattack prevention sciences such as Game Theory, Optimization, control Theory, and Data-driven method are discussed. It reveals that cyberattack detection techniques have evolved from traditional state estimation to artificial intelligence-based methods. Finally, the challenges and opportunities for future research on cybersecurity in CPPS are identified. © 2023 IEEE.

关键词： Game theory

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Vehicle string using spacing strategies for cooperative adaptive cruise control system

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

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2021年第1期83卷 91-106页

作者： Luu, Duc Lich Lupu, Ciprian Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

The Co-operative Adaptive Cruise control (CACC) is one of the key subsystems of Connected and Automated Vehicles (CAVs) that have the potential to increase the road traffic capacity and improve traffic safety with shorter inter-vehicle spacing between vehicles. The spacing strategies are the core of all CACC control algorithms which are chosen based on design purposes for the system. This work presents a comparative research to evaluate the effectiveness of two spacing strategies, consisting of the constant spacing strategy and, constant time headway strategy, which is suitable for information flow topologies. The main focus of this research analyzes the benefits of operating mechanisms as well as the characteristics of each spacing strategy. The results of the simulations using software Matlab/Simulink have demonstrated the performances and applicability of each spacing policy. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Adaptive cruise control

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Computing the explicit MPC solution in the constrained zonotope case 63

Computing the explicit MPC solution in the constrained zonot...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Stoican, Florin Mihai, Stefan S. Monnigmann, Martin Ciubotaru, Bogdan D. University 'Politehnica' of Bucharest Department of Automatic Control and Systems Engineering 313 Splaiul Independenţei Sector 6 Bucharest060042 Romania Ruhr University Bochum Martin Mönnigmann Is with Automatic Control and System Theory Bochum44801 Germany

ISBN: (纸本)9798350316339

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem's constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution. © 2024 IEEE.

关键词： State space methods

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Holistic Self-Distillation with the Squeeze and Excitation Network for Fine-grained Plant Pathology Classification 26

Holistic Self-Distillation with the Squeeze and Excitation N...

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26th International Conference on Information Fusion, FUSION 2023

作者： Su, Jingxuan Anderson, Sean Mihaylova, Lyudmila University of Sheffield Department of Automatic Control & Systems Engineering S1 3JD United Kingdom

ISBN: (纸本)9798890344854

Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy. © 2023 International Society of Information Fusion.

关键词： Distillation

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