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Mathematical Models to Determine Temperature Fields in Heterogeneous Elements of Digital Devices with Thermal Sensitivity Taken into Account 12

Mathematical Models to Determine Temperature Fields in Heter...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Havrysh, Vasyl Kochan, Volodymyr Lviv Polytechnic National University Lviv79013 Ukraine West Ukrainian National University Ternopil46020 Ukraine Research Institute of Intelligent Computer Systems of Western Ukrainian National University Ternopil46020 Ukraine

ISBN: (纸本)9798350358056

Linear and nonlinear mathematical models for determining a temperature field and subsequently analysis of temperature distribution in isotropic spatial media with semi-through foreign inclusions subjected to internal and external thermal loads are considered. For this purpose, the heat transfer coefficient for such structures is considered as a single unit using asymmetric unit functions, which allows considering the boundary value problems of heat transfer with one linear and nonlinear differential equations of heat transfer with discontinuous and singular coefficients as well as linear and nonlinear boundary conditions on the boundary surfaces of the media. The analytic expressions in the form of quadratic equations were obtained for determining the temperature distribution in a thermally sensitive layer with a foreign semi-through inclusion under external heating by a thermal flux. A numerical analysis of the temperature as a function of spatial coordinates for certain geometric and thermophysical parameters is performed. The theoretical studies are applied for BK94-I ceramic with the silver inclusion. © 2023 ieee.

关键词： convective heat transfer half-through foreign inclusion isotropic spatial inhomogeneous medium local internal and external heating perfect thermal contact temperature field thermal conductivity thermal flux thermal sensitivity

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A Model for Building a Wireless Terahertz Network for 5G NR 12

A Model for Building a Wireless Terahertz Network for 5G NR

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Saiko, Volodymyr Odarchenko, Roman Zhurakovskyi, Bogdan Yevdokymenko, Maryna Fesenko, Vladyslav Tkachova, Olena Taras Shevchenko National University of Kyiv Kyiv Ukraine Bundleslab Kft Budapest Hungary National Technical University of Ukraine 'Ihor Sikorsky Kyiv Polytechnic Institute' Kyiv Ukraine Kharkiv National University of Radio Electronics Kharkiv Ukraine National Aviation University Kyiv Ukraine Turnkey Lender Enterprise Austin United States

ISBN: (纸本)9798350358056

A new approach to implementing 5G and next-generation networks by using the resource of a local cluster of the network infrastructure of the terahertz range with integrated reconfigurable intelligent surfaces (hereafter RIS) is proposed in the paper. The approach allows for ensuring communication reliability. A solution for using a distributed register for implementing a preventive handover mechanism when blocking in the terahertz range is presented. The novelty of the proposed method is an activating of the serial interaction mechanism and transmission of signals between several mobility and session management objects during the handover of user equipment in modern wireless networks. The main benefit proposed solution from the existing is that solution allows for eliminating the need for sequential processing of requests by multiple managed objects, especially when the network function must be selected from multiple candidates, and accordingly ensures the implementation of various services with ultra-low delays. © 2023 ieee.

关键词： 5G distributed registry mapping method model RIS terahertz communication systems

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Using data Analysis Methods for Predicting the Concentration of Toxic Elements in Soil 12

Using Data Analysis Methods for Predicting the Concentration...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Naizabayeva, Lyazat Zakirova, Gulnara International Information Technology University Almaty050000 Kazakhstan

ISBN: (纸本)9798350358056

This article is aimed at the selection of machine learning algorithms for the perception of elements in the composition of the soil. In our study, machine learning aims to track changes in the toxicity of contaminated soils in order to predict the level of human health hazard of a contaminated soil condition. For this, the following tasks were set: 1) collection and analysis of data on the types of soil toxicity;2) study of a mathematical model of toxicity to assess the level of resistance;3) testing the concentration of toxic elements in soils using four machine learning algorithms. Four machine learning algorithms were tested in the article to predict the content of heavy metals in the soil. The results show that the use of machine learning algorithms allows achieving a high degree of prediction of harmful elements concentrations in soils, which can be useful for making decisions on managing pollution sites. Further improvement of data accuracy and selection of the model and algorithm can also increase the accuracy of prediction. As a result of forecasting using the K-Nearest Neighbors algorithm, the accuracy of the model was achieved at the level of 68%, and using the Decision Tree - 69%. The results of the experiment make it possible to predict the measures of danger to human health from the state of contaminated soil. This will help reduce the risks of the relationship between soil pollution and human health. The obtained results can be useful for specialists in environmental protection, land resource management, and decision-making. © 2023 ieee.

关键词： data structure intellectual analysis predicting soil toxic elements

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Convolutional Neural Network Assessment of Image Quality Based on the TID2013 database 12

Convolutional Neural Network Assessment of Image Quality Bas...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Talun, Arkadiusz Drozda, Pawel Yelmanov, Sergei Romanyshyn, Yuriy Tehlivets, Oles Emplocity Ltd Warsaw Poland University of Warmia and Mazury in Olsztyn Olsztyn Poland Special Design Office of Television Systems Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798350358056

The problem of automatic neural network assessment of image quality based on the widely used TID2013 image database is considered in the paper. Image quality assessment without the use of basic reference images, as one of the preliminary stages of their processing, is of great importance from the point of view of the information capacity of the images, the necessity and possibilities of enhancement of the image quality when solving the next tasks of segmentation, classification, recognition, etc. A preliminary analysis of the used image quality assessment metrics and, as an alternative, the use of neural networks, primarily deep neural networks, was conducted. The structures of used neural networks and variants of input data for training neural networks in the problem of image quality assessment are presented. The first variant is a regression problem, and the other one is a classification task. Keras software using TensorFlow for tensor operations is used to build and study neural networks. © 2023 ieee.

关键词： activation function deep neural network image quality assessment Keras neural network training quality metrics TensorFlow TID2013 database visual expert assessment

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Development of Verification Model for Intelligence of Multiagent systems 12

Development of Verification Model for Intelligence of Multia...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Martynyuk, Oleksandr Drozd, Oleksandr Sachenko, Anatoliy Martynyuk, Dmitry Mileiko, Igor Kopytchuk, Igor Hladiy, Grygoriy National University 'Odesskaya Polytechnica' Odessa650044 Ukraine West Ukrainian National University Ternopil46009 Ukraine Kazimierz Pulaski University of Technology and Humanities Radom26-600 Poland Join Venture 'Nippon Auto' Odessa65000 Ukraine

ISBN: (纸本)9798350358056

The development of distributed information systems essentially relies on the complex application of Web services technologies, cloud, 'green', GRID, multi-agent and many others. In many of these technologies, intellectualization is increasingly manifested, which is clearly inherent, for example, in semantic and intelligent Web services, multi-agent systems. Intelligence, often defined as a set of properties of knowledge and mechanisms for their formation, requires its own ways of formalization and implementation, which is also reflected in the problems of analysis and synthesis of intelligent systems, including the problems of their identification and verification. This paper consider the fuzzy and component development of a model for verifying the intelligence of multi-agent systems based on the previously proposed model for verifying the intelligence of multi-agent systems, which, in turn, is based on behavioral experiments for extended Petri nets. The features of the proposed model for verifying intelligence are the presentation of fuzzy production models of all components of intelligence for parallel, associative-evolutionary search for known identifying primitives and recognition of fragments of system behavior based on them, the construction of new identifying primitives as part of a three-level model of new knowledge, as well as a fuzzy inference mechanism, as the basis for obtaining new knowledge, and for identifying and verifying the behavioral properties of the system. The intelligence verification model makes it possible to reduce the computational complexity of the behavioral verification of multi-agent systems and increase its flexibility. © 2023 ieee.

关键词： associative-evolutionary search behavior identifying behavior verification distributed information system intelligence verification model multiagent system

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Tsunami Visualization Tool Using Numerical Simulation Utilizing 3D City Model 13

Tsunami Visualization Tool Using Numerical Simulation Utiliz...

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Joint 13th international conference on Soft computing and intelligent systems and 25th international Symposium on advanced intelligent systems, SCIS and ISIS 2024

作者： Oshida, Ryutaro Sasaki, Kano Ota, Takao Emoto, Hisao Fukui, Nobuki Graduate School of Sustainability Science Tottori University Tottori Japan Tottori University Faculty of Engineering Department of Social Systems and Civil Engineering Tottori Japan

ISBN: (纸本)9798350373332

After the Noto Peninsula earthquake on January 1, 2024, the importance for residents on the Sea of Japan coast to have a correct understanding of the tsunami hazard is growing. One of the training tools for enhancing tsunami awareness is a tsunami visualization using Virtual Reality (VR). In this study, we create a VR space around Asahi-machi, Sakai-minato City using 3D City Model of PLATEAU, and develop a tsunami evacuation training tool by creating inundation scenarios in the VR space using the results of tsunami inundation simulations with the JAGURS tsunami model. © 2024 ieee.

关键词： Digital elevation model

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Towards Evidence-Based Cybersecurity Assessment of Programmable systems to Ensure the Protection of Critical IT Infrastructure 12

Towards Evidence-Based Cybersecurity Assessment of Programma...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Illiashenko, Oleg Kharchenko, Vyacheslav Odarushchenko, Oleg National Aerospace University 'KhAI' Department of Computer Systems and Networks Kharkiv61070 Ukraine Institute of Information Science and Technologies 'Alessandro Faedo' - Isti Cnr Software Engineering & Dependable Computing Laboratory Pisa56124 Italy Research and Production Enterprise Rpc Radics Llc Kropyvnytskyi25009 Ukraine

ISBN: (纸本)9798350358056

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature - physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems. © 2023 ieee.

关键词： critical IT infrastructure cybersecurity FPGA functional safety programmable systems of critical applications

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A Comparative Analysis of Deep Learning-Based Object Detectors for Embedded systems 12

A Comparative Analysis of Deep Learning-Based Object Detecto...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Boltov, Yehor Skarga-Bandurova, Inna Derkach, Maryna G.E. Pukhov Institute for Modelling in Energy Engineering Kyiv03164 Ukraine Oxford Brookes University OxfordOX33 1HX United Kingdom Ternopil Ivan Puluj National Technical University Ternopil46001 Ukraine

ISBN: (纸本)9798350358056

The paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. The objective was to identify the optimal algorithm that can efficiently operate within limited computational resources and memory constraints of embedded systems while minimizing energy consumption. We tested a set of popular detectors, such as Faster R-CNN, YOLO, and SSD MobileNet and evaluated their performance on the testing platform with CUDA cores. A faster variant of bounding box suppression, blazing fast Non-Maximum Suppression (NMS), was employed to enhance efficiency. Additionally, the generalized intersection over union (GIoU) metric was adopted as the optimal testing method, allowing for more accurate object localization and detection. Results demonstrate that the SSD MobileNet-V2 and Tiny YOLO detectors exhibit promising real-time performance on embedded platforms, although their accuracy is diminished compared to larger models. © 2023 ieee.

关键词： blazing fast NMS COCO CUDA embedded system Fast R-CNN GIoU object detector real-time SSD MobileNet YOLO

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Design and Research of intelligent Robot 3D Recognition Processing System 7

Design and Research of Intelligent Robot 3D Recognition Proc...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： Wang, Liang Zhejiang Business Technology Institute Ning Bo315100 China

ISBN: (纸本)9798331529314

With the continuous advancement of artificial intelligence technology, intelligent robots are increasingly and extensively utilized in industrial production, daily life, as well as detection and security. This paper presents the design and research of a 3D recognition and processing system for intelligent robots, with the aim of enhancing the autonomous cognition and decision-making ability of robots in complex environments. Firstly, the system is based on advanced 3D sensing technology, through the combination of multi-line LiDAR for assisted acquisition of point cloud data and stereo vision. Subsequently, a deep learning network architecture of global features and local scale features is designed for the feature extraction and classification of point cloud data. Finally, through training and optimizing the model, accurate 3D object recognition is achieved. The experimental results demonstrate that the classification accuracy of the system on the training set and the test set has reached a high level, and it can well recognize different categories of three-dimensional objects. © 2024 ieee.

关键词： Adversarial machine learning

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Implement of Edge computing for Cyber-Physical System in the Smart Grids Quasi-Real Time Control Context 12

Implement of Edge Computing for Cyber-Physical System in the...

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12th ieee international conference on intelligent data acquisition and advanced computing systems: Technology and Applications, IDAACS 2023

作者： Dombrovskyi, Mykhailo Dombrovskyi, Zbyshek Pashchuk, Ivan West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350358056

An increase in the number of power consumers leads to scale up a power supply grids, and the introduction of Smart Grid (SG) for connecting components and subsystems of distributed generation, increases the complexity of the interaction configuration and, accordingly, grids control. At the same time, with peak loads cases, when there is a lack of power, the probability of big-scale fluctuations increases, which turns the energy system from deterministic to stochastic. Unpredictable manifestations of big-scale fluctuations spread in the form of a cascade of failures through the grid, which lead to the loss of reliability of the entire power grid. To simplify the controllability of complex big-scale power grids and prevent cascading failures in the supply of electric power, they are divided into a big number of microgrids. To increase observability and controllability, big-scale SGs are implementing innovative intelligent products and cyber-physical systems (CPS) that connect the virtual world with the world of things. CPSs allow to carry out instant monitoring and display of the states of power system components in large geographical areas, however, due to the concentration of control in the center, it does not ensure control efficiency in critical peak load times. Thus, it is important to increase the energy efficiency of smart grids, in particular by reducing power dispersion and increasing the flexibility, safety and reliability of energy supply. Such requirements lead to rethinking the structure of both the physical and cybernetic subsystems of the CPS, with which users and suppliers of energy resources interact. To meet these requirements, it is proposed to develop the structure of the CPS and improve computation based on the processing of 'instant data' with the use of edge computations in quasi-real time, in order to ensure an increase in the efficiency of a big-scale complex power networks control. © 2023 ieee.

关键词： big-scale power grid cyber-physical systems edge computing instant data quasi-real time control smart grid

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