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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Volodymyr Khilenko Andrii Zinchenko Oleksii Stepanov Ivan Kotuliak Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing.

关键词： Analytical models Heuristic algorithms Instruction sets Signal processing algorithms Process control Parallel processing Control systems Smart systems Data models Blockchains

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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Dielectric Cylinder Discrimination with ANN Using Wideband Electromagnetic Response 28

Dielectric Cylinder Discrimination with ANN Using Wideband E...

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28th IEEE International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, DIPED 2023

作者： Drobakhin, Oleg Tabatadze, Vasil Chuchva, Vladyslav Huz, Artem Department of Applied Radiophysics Electronics and Nanomaterials Oles Honchar Dnipro National University Dnipro Ukraine Department of Computer Technologies Oles Honchar Dnipro National University Dnipro Ukraine Informatics Institute Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9798350315332

The application of three-component stacked artificial neural networks (ANN) for discrimination of dielectric cylinders of different diameters by their radio images is considered. Neural networks include two sparse autoencoders and the softmax unit. Neural networks are not tied to the frequency range, unlike many well-known methods based on the resonant properties of objects, and they are a powerful tool for object recognition. Radio images were obtained using the method of auxiliary sources (MAS) for cylinders with the radius of 15 to 35 mm. The possibility of successful recognition was confirmed for the case of the diameter deviation of 1 mm and the presence of additive Gaussian noise with SNR of up to 5 dB. © 2023 IEEE.

关键词： Neural networks

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TEACHING: A Computing Toolkit for Building Efficient Autonomous appliCations Leveraging Humanistic INtelliGence 3

TEACHING: A Computing Toolkit for Building Efficient Autonom...

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3rd Workshop on Flexible Resource and Application Management on the Edge, FRAME 2023, held in conjunction with the HPDC 2023 and the FCRC 2023 Conferences

作者： Bacciu, Davide Tserpes, Konstantinos Coppola, Massimo MacHer, Georg Gallicchio, Claudio Veledar, Omar Anaxagorou, Anna Maria Dazzi, Patrizio University of Pisa Department of Computer Science Pisa Italy Harokopio University Athens Department of Informatics and Telematics Athens Greece National Research Council of Italy Institute of Information Science and Technologies Pisa Italy Tu Graz Department of Informatics and Telematics Graz Austria Avl Graz Austria Information Technology for Market Leadership Athens Greece

ISBN: (纸本)9798400701641

TEACHING proposes a distributed, trustworthy AI integrating continuous human feedback, supporting CPSoS application design and deployment. TEACHING envisions an intelligent environment, empowering humans through cybernetic assistance. It advances autonomous safety-critical systems, improving safety, reliability and acceptability through human-centred design and formal validation crossing paradigms. TEACHING brings humans and AI together, enabling participatory development, optimisation and oversight. © 2023 ACM.

关键词： Cyber Physical System

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Serious games: The Possibilities of Data Mining and Data Analysis through Neuro and Biofeedback

Serious games: The Possibilities of Data Mining and Data Ana...

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International Conference on Emerging eLearning technologies and Applications (ICETA)

作者： M. Hos’ovecký Institute of Computer Technologies and Informatics Faculty of Natural Sciences University of SS. Cyril and Methodius Trnava Slovakia

Serious games have emerged as valuable tools for training in various domains, making them an essential component of training and coaching programs aimed at enhancing skills and knowledge. This study aims to explore the potential of SGs in collecting and analyzing data using neuro and biofeedback techniques. Specifically, we focus on selected methods for data analysis, including eye-tracking, electroencephalography (EEG), heart rate variability (HRV), and electro-dermal activity (EDA). The objective is to investigate how playing SGs can facilitate the collection and analysis of these types of data.

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Machine Learning Techniques for Result Prediction of One Day International (ODI) Cricket Match 5th

Machine Learning Techniques for Result Prediction of One Day...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Haq, Inam ul Kumar, Neerendra Koul, Neha Verma, Chaman Eneacu, Florentina Magda Raboaca, Maria Simona Department of Computer Science and IT Central University of Jammu Jammu and Kashmir India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Department of Electronics Communications and Computers University of Pitesti Pitesti Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm. Valcea Romania

ISBN: (纸本)9789819906000

Cricket is the most popular sport, and most watched now a day. Test matches, One Day Internationals (ODI), and Twenty20 Internationals are the three forms in which it is played. Until the last ball of the last over, no one can predict who would win the match. Machine learning is a new field that uses existing data to predict future results. The goal of this study is to build a model that will predict the winner of a One Day International Match before it begins. Machine learning techniques will be used on testing and training datasets to predict the winner of ODI match that will be based on the specified features. The data for model will be collected from Kaggle, and some will be collected from the different cricket Web sites because the data obtained from Kaggle have only matches up until July 2021. After that prediction will be done, and the model will provide advantages to team management in terms of improving team performance and increasing the chance of winning the game. This model will be used to predict the outcomes of the next Cricket World Cup 2023, which will be, the 13th, edition of the men’s ODI Cricket World Cup and will be hosted by India in 2023. Also, this work will serve as a guidance work, as there is much to be done in the field of sports. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sports

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Contemporary Approach of Additive Manufacturing for Sustainable Products in the Era of Circular Economy 8th

Contemporary Approach of Additive Manufacturing for Sustaina...

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8th EAI International Conference on Management of Manufacturing Systems, MMS 2023

作者： Knapčíková, Lucia Behúnová, Annamária Tauberová, Rebeka Kaščak, Jakub Husár, Jozef The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia Technical University of Kosice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Kosice Slovakia The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Computer Aided Manufacturing Technology Prešov Slovakia

ISBN: (纸本)9783031531606

Considering the need for mass customization in Industry 4.0, it is necessary to develop non-traditional production methods. Therefore, additive manufacturing can become a key technology for the production of customized products thanks to its ability to create irregular objects or shapes. In addition to enabling new design possibilities, the additive manufacturing method holds promise for increasing social, economic, and environmental sustainability. It also has the potential to reduce energy consumption and lower polymer material consumption. Additive manufacturing contrasts with conventional processes, as it consists in applying materials layer by layer. Conventional manufacturing operations produce polymer waste that is difficult to reintroduce into manufacturing processes. The aim of the contribution is the possibility of using additive manufacturing in the design and processing of new effective use of the product with regard to the environmental aspect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 3D printing

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Reducing the Percentage of Defective Castings Using the Neural Network Modeling Method

Reducing the Percentage of Defective Castings Using the Neur...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Yasnitsky, Leonid N. Rabchevsky, Andrey N. Martynenko, Sergey V. Perm State University Department of Applied Mathematics and Informatics United States National Research University 'Higher School of Economics' Department of Information Technologies Perm Russia Computer Science and Technology Institute Perm State University Perm Russia Ural Federal University Named After. B.N YeItsin Branch in Nizhny Tagil Department of Metallurgical Technologies Nizhny Tagil Russia

ISBN: (纸本)9798350395044

The article is devoted to the current problem of reducing the percentage of defective products manufactured at serial steelmaking plants. To study the patterns of defect formation, a neural network was created that predicts the formation of defects such as 'Cracks' in castings produced by the open-hearth method. To train the neural network, statistical data taken under the conditions of the current production process was used, which resulted in a high degree of information noise. Outliers of statistical information were detected and removed with the original author's neural network technique. By conducting virtual computer experiments on a neural network model, some important dependences of the probability of the defect formation on the content of manganese, phosphorus, silicon, chromium, and sulfur were identified. Based on that, practical recommendations have been developed to reduce defects by adjusting the chemical composition of the metal. Despite the relatively low accuracy of the created neural network model, the application of the practical recommendations made it possible to reduce the percentage of defective products manufactured at a serial plant by half. © 2024 IEEE.

关键词： Statistics

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The Simulation of Internet of Things System on the Base of Neural Network Approach 8

The Simulation of Internet of Things System on the Base of N...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Lvovich, Igor Lvovich, Yakov Preobrazhenskiy, Andrey Preobrazhenskiy, Yuriy Sakharov, Yuriy Voronezh Institute of High Technologies Faculty of Informatics Voronezh Russia Voronezh State Technical University Faculty of Information Technology and Computer Security Voronezh Russia Dubna International University Dubna Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper deals with the problem associated with the description of the Internet of Things system. The purpose of the paper is to ensure the required efficiency of this system. In the course of describing the problem, it was proposed to use neural networks to divide objects in the Internet of Things system into groups. The illustration of the ART network operation model is given. An appropriate algorithm for dividing objects into groups was developed. The results of modeling on the basis of such an algorithm are given. The dependence of efficiency parameters from variants of system of Internet of Things is obtained. © 2022 IEEE.

关键词： Efficiency

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