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arXiv 2023年

作者： Barta, Botond Lakatos, Dorina Nagy, Attila Nyist, Milán Konor Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Eötvös Loránd Research Network Hungary

We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1.14M news articles. The dataset is built by collecting, cleaning and deduplicating data from 9 major Hungarian news sites through CommonCrawl. Using this dataset, we build abstractive summarizer models based on huBERT and mT5. We demonstrate the value of the created dataset by performing a quantitative and qualitative analysis on the models' results. The HunSum-1 dataset, all models used in our experiments and our code are available open source. © 2023, CC BY.

关键词： Open systems

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Internet Computing 1

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2020年

作者： Ali Sunyaev

ISBN: (数字)9783030349578

ISBN: (纸本)9783030349561

Intended as a textbook for upper undergraduate and graduate classes, it features a wealth of examples, learning goals and summaries for every chapter, numerous recommendations for further reading, and questions for checking students’ comprehension. A dedicated author website offers additional teaching material and more elaborate examples. Accordingly, the book enables students and young professionals in IT-related fields to familiarize themselves with the Internet’s basic mechanisms, and with the most promising Internet-based technologies of our time.

关键词： Computer and Information Systems Applications Business IT Infrastructure Computer Communication Networks Software Engineering

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Best Selection of Generative Adversarial Networks Hyper-Parameters Using Genetic Algorithm

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SN Computer Science 2021年第4期2卷 283页

作者： Alarsan, Fajr Ibrahem Younes, Mamoon Informatics and Decision Supporting Systems Higher Institute for Applied Sciences and Technology Damascus Syrian Arab Republic Faculty of Computer and Automation Engineering Damascus University Damascus Syrian Arab Republic

Generative adversarial networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other. In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digit numbers 1–9 with generator and train discriminator to decide whereas the generated images are fake or true. Genetic algorithm (GA) technique was used to adapt GAN hyper-parameters, the resulted algorithm is named GANGA: generative adversarial network with genetic algorithm. The resulted algorithm has achieved high performance;it was able to get zero value of loss function for the generator and discriminator separately. Anaconda environment with tensorflow library facilitates was used;python as programming language was adapted with needed libraries. The implementation was done using MNIST dataset to validate the work. The proposed method is to let genetic algorithm choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function used to find best values of learning rate, batch normalization, number of neurons and a parameter of dropout layer. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Discriminator Generative adversarial networks (GAN) Generator Genetic algorithm Image synthesis MNIST dataset

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems Engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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SafeSmart 6G: The Future of Emergency Vehicle Traffic Light Preemption

SafeSmart 6G: The Future of Emergency Vehicle Traffic Light ...

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6G Networking (6GNet), International Conference on

作者： Eduardo Kochenborger Duarte Mikael Erneberg Edison Pignaton de Freitas Boris Bellalta Alexey Vinel School of Information Technology (ITE) Halmstad University Halmstad Sweden H&E Solutions AB Stockholm Sweden Institute of Informatics (INF) Federal University of Rio Grande do Sul Porto Alegre Brazil Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

This paper delves into the utilization of Vehicular Ad-hoc Networks (VANETs) in emergency vehicle warning systems in the era of 6G. The proposed system, named SafeSmart 6G, will leverage VANET-based vehicle-to-infrastructure Communication powered by 6G to exchange data between traffic lights and emergency vehicles, enhancing safety and reducing response times. SafeSmart 6G will predict the arrival time of emergency vehicles at intersections using historical data and AI-driven analytics, requesting signal preemption for the chosen route. The paper discusses the potential benefits and challenges that might arise from the use of 6G in emergency scenarios.

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Applying evolutionary algorithms successfully a guide gained from real-world applications

arXiv

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arXiv 2021年

作者： Jakob, Wilfried Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization. This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated. © 2021, CC BY-SA.

关键词： Global optimization

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Concepts for automated machine learning in smart grid applications

arXiv

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arXiv 2021年

作者： Meisenbacher, Stefan Pinter, Janik Martin, Tim Hagenmeyer, Veit Mikut, Ralf Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

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A novel criterion for global incremental stability of dynamical systems

arXiv

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arXiv 2022年

作者： Vrabel, Robert Slovak University of Technology in Bratislava Institute of Applied Informatics Automation and Mechatronics Bottova 25 Trnava917 01 Slovakia

In this paper, we establish the sufficient conditions guaranteeing global uniform exponential stability, or at least global asymptotic stability, of all solutions for nonlinear dynamical systems, also known as global incremental stability (GIS) of the systems. We provide here an alternative approach for assessment of GIS in terms of logarithmic norm under which the stability becomes a topological notion and also generalize both horizontally and vertically the well-known Demidovich criterion for GIS of dynamical systems. Convergence of all solutions to the origin x = 0, which is not assumed to be an equilibrium state of system, is also analyzed. Theory is illustrated by a simulation *** Codes 93C10, 34D23 © 2022, CC BY.

关键词： Nonlinear dynamical systems

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Adaptive test case selection for DNN-based perception functions

Adaptive test case selection for DNN-based perception functi...

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IEEE International Symposium on Systems Engineering (ISSE)

作者： Johannes Bernhard Thomas Schulik Mark Schutera Eric Sax Institute for Information Processing Technologies Karlsruhe Institue of Technology Germany Research and Development ZF Friedrichshafen AG Institute for Automation and Applied Informatics Karlsruhe Institue of Technology Germany

The progress in deep learning methods has bolstered the development of automated vehicles during the last decade. However, the deployment of deep learning methods in safety-critical applications raised questions on their safety. Like other vehicle components, a testing process has to prove the reliability of perception systems. Scalability issues arise when using real-world data to validate perception algorithms due to the immense amount of sensor data that needs to be tested. Simulation tools can complement this testing process, as they can fabricate synthetic data based on variable specifications of test cases. While simulative tools can produce a vast amount of test data, at some point, the testing process is limited by the available computing resources. Identifying test specifications that pose risks to the perception algorithms is crucial for efficiently utilizing these computing resources and estimating functional reliability. We present a pipeline for adaptive test case selection to expose the faults of a deep learning system using synthetic image data generated by a simulation framework. We apply our concept to a state-of-the-art object detector and implement multiple adaptive sampling strategies to demonstrate their ability of early fault detection. Our experiments show that our pipeline can achieve a 95% coverage of system faults while reducing the number of test executions by 90%.

关键词： Deep learning Adaptive systems Computational modeling Scalability Pipelines Tools Data models

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Sliding Mode Control Strategy for State-of-Charge (SoC) Balancing in Lithium-ion Battery Packs*

Sliding Mode Control Strategy for State-of-Charge (SoC) Bala...

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International Conference on Control, automation and Information Sciences (ICCAIS)

作者： Yong Feng Fengling Han Rebecca Yang Yanmin Wang Fan Bai Nguyen-Vu Truong School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Science RMIT University Melbourne Australia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh City Vietnam

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

This paper proposes a sliding mode control strategy for balancing the State of Charge (SoC) in lithium-ion battery packs. By thoroughly analyzing the battery balancing circuit topology, a bidirectional Cuk circuit balancing topology for a series-connected battery pack is established, and its mathematical model is developed. Utilizing sliding mode control technology, a balancing strategy is designed to equalize the SoC of all cells in a battery pack composed of multiple series-connected battery cells. The effectiveness of the proposed method is verified through experiments, demonstrating that the developed battery balancing system can successfully balance the SoC of each cell in the battery pack.

关键词： Lithium-ion batteries Circuit topology automation Mathematical models Topology State of charge Sliding mode control

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