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31st European Safety and Reliability Conference, ESREL 2021

作者： Krini, Abderrahim Börcsök, Josef Engineering of Reliability and Quality Robert Bosch GmbH Germany Department of Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (纸本)9789811820168

In general, failure data is obtained in the automotive industry during the warranty period. If these contain the expression of a service life characteristic for each failure, statements can be made about the reliability and availability of the systems. For this purpose, estimation methods are used to adapt empirical lifetime distributions to theoretical lifetime distributions. By means of the distribution characteristics, a prognosis of the reliability and availability to be expected is also possible beyond the observation time. In addition to the data basis to be investigated, which is available completely in test bench and experimental trials or re-censored in the field, various methods can be used for estimation. In the past, several methods have been used, such as the estimators according to ECKEL [1], Kaplan-Meier [2] or the estimators according to Pauli [3]. In general, a field failure is subject to several stresses, which can be described by expressions of several lifetime characteristics. For this application case, which occurs in the automotive industry, the known estimation methods, [1], [2], [3], cannot be used. In this paper, the necessity of multidimensional estimation methods will be introduced first. Then, a new estimation method for multidimensional metrics is presented. In a further step, mathematical proof is given that the new method can provide realistic results. Finally, two example data sets from bench testing and field are presented. Furthermore, some recommendations for the use of the new method are concretized in this context. © ESREL 2021. Published by Research Publishing, Singapore.

关键词： Reliability

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Collaboration And Versioning Framework – A Systematic Top-Down Approach

Collaboration And Versioning Framework – A Systematic Top-D...

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Conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Jakob Pietron Alexander Raschke Joeri Exelmans Matthias Tichy Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Department of Computer Science University of Antwerp – Flanders Make Antwerp Belgium

CONTEXT: Versioning allows users to efficiently create and evolve artifacts, not only but especially in the domain of model-driven engineering. Due to collaboration tools, the place and time where users jointly work at their artifacts do not play an important role anymore. Objective: We systematically elaborated a classification framework for collaboration and versioning tools. The intention is threefold: First, it should be possible to classify existing approaches. Second, deriving from user goals requirements to be met, and capabilities to be supported by a new tool. Third, given a set of capabilities, highlight which user goals can be achieved. Method: According to Kang and Lee, we systematically elaborated a problem space and a solution space and created mappings between both spaces and their sub-spaces. To demonstrate the applicability, we classified existing VCS like git, Google Docs, and several MDE-specific approaches. Results: The created feature diagram covers 238 features and contains about 87 constraints. Conclusion: The developed framework supports researchers and developers in classifying their tools, revealing new opportunities to improve their tools, and guiding the development of new tools.

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Development of a Navigation System on the University Territory Using Augmented Reality Technology

Development of a Navigation System on the University Territo...

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Olena Chernykh Oleksandr Zakovorotniy Heorhii Hrianyk Leonid Kosmin Yelizaveta Bondarenko Computer Engineering and Programming Department Kharkiv Polytechnic Institute National Technical University Kharkiv Ukraine

AR navigation is one of the interactive ways to use augmented reality. By displaying virtual guides in physical space using a smartphone, users can navigate from point to point more naturally than by comparing the map to their immediate environment. Thanks to this great advantage, AR navigation can help in searching both inside the academic building and on the territory of the institute. In this paper, the authors used 3DUnity and AR Foundation to create a navigation system around the territory of NTU "KhPI" using augmented reality technology. This development will allow you to navigate the terrain of the KhPI campus, find the location of the desired building, and also look the route from building to building on the map. The whole process is displayed on the smartphone screen. Real-time synchronization allows users in real world to feel virtual space, thereby enhancing poignancy and interaction, and making effect more vivid and almost real.

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Functional Model of an Electrocardiological Study

Functional Model of an Electrocardiological Study

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Anna Filatova Anatoliy Povoroznyuk Mohamad Fahs Department of Computer Engineering and Programming Kharkiv Polytechnic Institute National Technical University Kharkiv Ukraine

The purpose of this research is to develop a functional model of the electrocardiological study using the methodology of functional modeling IDEF0. The functional model of the electrocardiological study are developed in the form of a contextual diagram, its decomposition, and decomposition of the activities "To perform registration and analysis of electrocardiograms" and "To perform diagnostics". Using the proposed functional model of the electrocardiological study, the structural diagram of the cardiological decision support system is developed based on the morphological analysis of biomedical signals with locally concentrated features. The modules of the proposed cardiological decision support system, as well as the modes of its operation, are considered. Further research is aimed at developing an information model of the electrocardiological study for the development of an informational structure of the cardiological decision support system based on the morphological analysis of electrocardiograms.

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Development the Numerical Method to Solve the Inverse Initial Value Problem for the Thermal Conductivity Equation of Composite Materials

Development the Numerical Method to Solve the Inverse Initia...

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2020 Ibn Al-Haitham International Conference for Pure and Applied Sciences, IHICPS 2020

作者： Al-Mahdawi, H.K. Department of System Programming School of Electronic Engineering and Computer Science South Ural State University Lenina 76 Chelyabinsk454080 Russia

In this paper, the heat conduction equation for composite materials posed and solved. This problem is known as an inverse initial value problem for the heat conduction equation. In order to solve and formulate this inverse problem, the function spaces must be defined and represented. By studying and solving the direct problem for the heat equation in composite materials, it is possible to determine the function spaces and solve the inverse initial value problem. Scientific methods used: the separation of variables method used to solve the direct problem for the heat equation. It found that method separation of variables does not completely lead to the solution of the inverse initial value problem, since this method leads to a divergent series of solutions and rather massive errors. The heat conduction problem can be formulated as Fredholm integral first kind equations. The discretization algorithm applied to reformulated the problem as linear operator problem as matrix and vectors form. Then, Tikhonov's regularization inversion method has been used to find an approximation solution. Finally, as shown in the numerical example the regularized approximate solutions obtained. © Published under licence by IOP Publishing Ltd.

关键词： Initial value problems

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Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic

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Materials Today: Proceedings 2022年 56卷 2213-2216页

作者： Boddu, Raja Sarath Kumar Karmakar, Partha Bhaumik, Ankan Nassa, Vinay Kumar Vandana Bhattacharya, Sumanta Department of CSE Lenora College of Engineering Andhra Pradesh Rampachodavaram India Government of West Bengal Bikash Bhawan Salt Lake W.B. Kolkata India Dept. of Applied Mathematics With Oceanology and Computer Programming Vidyasagar University Midnapore India Department of Computer Science Engineering South Point Group of Institutions Sonepat Haryana India Department of Mathematics Dasmesh Khalsa College Zirakpur India Department of Science and Technology and Biotechnology MAKAUT India

Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic

关键词： COVID-19

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Method of Detection of Intrusions Based on Stacking Meta-Algorithm

Method of Detection of Intrusions Based on Stacking Meta-Alg...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Vladislav Zozulia Nataliia Khatsko Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models.

关键词： Training Support vector machines Computational modeling Stacking Training data Intrusion detection Predictive models Nearest neighbor methods Data models Random forests

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A blockchain-oriented covert communication technology with controlled security level based on addressing confusion ciphertext

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Frontiers of computer Science 2025年第2期19卷 133-135页

作者： Lejun ZHANG Bo ZHANG Ran GUO Zhujun WANG Guopeng WANG Jing QIU Shen SU Yuan LIU Guangxia XU Zhihong TIAN Sergey GATAULLIN Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China Research and Development Center for E-Learning Ministry of EducationBeijing 100039China College of Information Engineering Yangzhou UniversityYangzhou 225009China School Math and Computer Science Quanzhou Normal UniversityQuanzhou 362000China Pazhou Lab Guangzhou 510330China School of Physics and Materials Science Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of SciencesMoscow 117418Russia Industrial Programming Department MIREA-Russian Technological UniversityMoscow 119454Russia

1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods also exist,such as peer-to-peer networks and distributed hash *** technology,which is decentralized,anonymous,non-tamperable and anti-attack,can be applied to covert communication to solve the problems of traditional methods and improve the quality of communication.

关键词： communication tables networks

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Creating a Dynamic Online Learning Environment with AI Integration

Creating a Dynamic Online Learning Environment with AI Integ...

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International computer Sciences and Information Technologies (CSIT)

作者： Oleksandra Bulgakova Zarina Viniarska Viacheslav Zosimov P.M Platonov Educational and Scientific Institute of Computer Engineering Automation Robotics and Computer Programming Odesa National University of Technology Odesa Ukraine Department of Applied information systems Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs by integrating advanced AI capabilities for personalized support and adaptive learning. The platform supports user registration and authentication, course and lesson management, and offers interactive features like AI-driven chat support. In this study, we show the importance of creating user-friendly online learning platforms with the implementation of modern AI trends. The development and testing of this platform is ongoing to determine the adaptation of the system to different learning styles.

关键词： Adaptive learning Electronic learning Authentication Passwords Market research Real-time systems Artificial intelligence Information technology Standards Testing

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IoT Cyberattack Detection Approach Based on Energy Consumption Analysis

IoT Cyberattack Detection Approach Based on Energy Consumpti...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Kira Bobrovnikova Oleg Savenko Sergii Lysenko Ivan Hurman Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine

ISBN: (纸本)9798350333053

Today, the Internet of Things has been widely used and is becoming an increasingly vital part in various areas of human activity, which helps to effectively solve various real-life tasks and problems. The infrastructure of the Internet of Things includes a lot of heterogeneous devices with limited capacities, which makes it difficult to control them in order to detect cyberattacks. The increase in the number of attacks on the infrastructure of the Internet of Things indicates the relevance of developing new solutions for detecting attacks on the networks of the Internet of Things. One of the possible approaches to identify attacks is to analyze the physical parameters associated with IoT devices that describe the behavior of devices on the network, such as power consumption. Power analysis allows you to detect power-hungry attacks such as crypto mining and denial-of-service attacks. The paper proposes a new method for cyberattack detection in the infrastructure of the Internet of Things that is based on the analysis of energy consumption. In order to improve the accuracy of detecting cyberattacks, the approach also uses the analysis of the actions of the IoT software. The proposed approach makes it possible to detect attacks such as DoS/DDoS with high efficiency with a level of detection up to 99.95%.

关键词： Energy consumption Power demand Software Behavioral sciences Internet of Things Object recognition Data mining

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