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15th KES International Conference on Sustainability and Energy in Buildings, SEB 2023

作者： Krechowicz, Maria Krechowicz, Adam Faculty of Management and Computer Modelling Kielce University of Technology Al. 1000-Lecia Państwa Polskiego 7 Kielce25-314 Poland Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Al. 1000-Lecia Państwa Polskiego 7 Kielce25-314 Poland

ISBN: (纸本)9789819985005

Nowadays, we can observe a growing interest in sustainable buildings. Sustainable development cannot be achieved without introducing innovations in green building;therefore, innovations are important component of sustainable engineers’ job. Innovative, sustainable buildings have numerous advantages, but it should be remembered that their design, and construction is more complex and associated with higher risk and uncertainty than traditional buildings. The goal of this work is to propose a new model of risk management which is based on fault tree analysis and Fuzzy sets theory. Sixteen unwanted events in innovative, sustainable building projects were identified, a fuzzy fault tree was developed. The application of the proposed approach was shown on one example of innovative, sustainable building in Poland. Two variants of project execution were compared: with and without introducing risk management strategy. Application of fuzzy sets by using linguistic variables by experts when assessing risks of particular unwanted events enabled to decrease uncertainty and avoid problems with obtaining the crisp values of the basic events probability. Application of the proposed model allowed us to identify the critical risks of the project, calculate top event risk, and compare the project’s risk level after introducing risk management strategies. The proposed model is helpful for designers, constructors, owners, and clients to develop successful innovative, sustainable buildings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault tree analysis

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear Systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on Systems and control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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STGNN: a new self-trained generalized neural networks task space control for robot manipulators

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Neural Computing and Applications 2025年 1-26页

作者： Elmogy, Ahmed Elawady, Wael El-Ghaish, Hany Computer Engineering Department Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta31527 Egypt

The inherent uncertainties in the dynamic and kinematic parameters of robot manipulators pose significant challenges for their control in task space. This paper introduces an innovative adaptive sliding mode control strategy, leveraging self-trained generalized neural networks to enhance the precision and robustness of robot manipulator control. The proposed framework called STGNN integrates deep neural networks for the real-time estimation and adjustment of dynamics’ and kinematics’ parameters. Key contributions of this work include the development of semi-supervised learning models for adaptive dynamics and kinematics estimation. By training the generalized neural network on different trajectories, the same models can be utilized to operate on a variety of tasks, significantly improving the robustness, scalability, and adaptability of the control system. The STGNN approach allows the models to leverage both labeled and unlabeled data, incorporating a self-training mechanism that enhances their ability to generalize and adapt to diverse operational scenarios. This capability is particularly beneficial for real-time applications, where the ability to learn and adapt on the fly is crucial. Simulation results demonstrate the superior performance of the STGNN framework in controlling robot manipulators in operational space, showcasing its potential for real-world applications. The proposed method significantly advances the adaptability, accuracy, and efficiency of robot control systems, offering notable improvements over conventional approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self adjusting control systems

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A Secure Real Estate Transaction Framework Based on Blockchain Technology and Dynamic Non-Fungible Tokens

A Secure Real Estate Transaction Framework Based on Blockcha...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Delia-Elena Bărbuţă Adrian Alexandrescu Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The real estate market, as an engine of economic growth, directly affects both financial stability and the development of communities at local and global levels. Nevertheless, it faces significant challenges, ranging from complex legislative processes and bureaucratic inefficiencies to fraud risks and high costs. Addressing these issues requires a paradigm shift to improve efficiency and transparency in this sector. The proposed solution aims to integrate emerging technologies such as blockchain and smart contracts to secure transactions and create a tamper-proof digital history. The implementation of dynamic non-fungible tokens (dNFTs) facilitates property transfers and digitally models real estate assets. A novel communication protocol and store policy are proposed for storing real estate information on the blockchain network and on decentralized storage so that the digital token representing the property can change based on external factors, e.g., change in ownership, actual updates being done on the property, or an update in valuation. The proposed framework ensures trustworthiness, transparency, traceability and security, while providing a flexible architecture with the main system components, including ways to model, update, list and buy a real estate property.

关键词： Solid modeling Costs Metaverse Biological system modeling Smart contracts Virtual reality Aerodynamics Stability analysis Nonfungible tokens Time factors

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Travel Planning Optimization System Employing Genetic Algorithms with Multiple Parameters

Travel Planning Optimization System Employing Genetic Algori...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Iuliana-Elena Rusu Adrian Alexandrescu Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Many people around the world enjoy traveling as a popular leisure activity. The process of trip planning can be time-consuming, requiring travelers to choose between various options and make decisions based on their preferences and *** paper addresses the tourist trip design problem by proposing a Travel Planning Optimization System (TPOS), which includes an architecture that employs genetic algorithms to streamline travel planning. The paper’s novelty lies in considering numerous travel factors, such as duration of visits, travel time, preferred types of locations, operating hours, destination popularity and the scheduling of places to eat in the daily program, into a single metric. This approach holds great promise in transforming travel planning by providing customized experiences that closely match the unique preferences and interests of each traveler, while using real location-related information in determining an efficient multiple-day itinerary.

关键词： Measurement Processor scheduling Roads Systems architecture control systems Scheduling Planning Optimization Genetic algorithms Meteorology

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Multifunctional control system to enhance the operational capability of the unmanned multirotor platform dedicated to the diagnostics of building structures

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第3期73卷

作者： Czyba, Roman Domin, Jaroslaw Górski, Marcin Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Silesian University of Technology Faculty of Electrical Engineering Gliwice Poland Silesian University of Technology Faculty of Civil Engineering Gliwice Poland

Drones have become common devices that are increasingly used, from toy mini drones to very advanced autonomous flying platforms used in various industries. The safety of using engineering structures and the need to shorten the time of carrying out diagnostic activities, and thus reduce costs, force the use of innovative tools to carry out an automated and unmanned process of assessing the technical condition of existing engineering structures, especially large-size ones, including bridges strengthened with external FRP (fibre reinforced polymer) composite overlays. There are conventional methods for assessing the technical condition of existing building structures conducted by inspectors and experts, involving the use of typical inspection and diagnostic tools. During the research, it was found that it is possible to conduct an automated and unmanned process of assessing the technical condition of engineering structures using an unmanned flying platform equipped with a thermal imaging diagnostic device. The uniqueness of the presented solution is that the drone is equipped with a docking module that allows the drone to be temporarily attached to the structure being diagnosed for the duration of diagnostic activities. To ensure appropriate conditions for structural diagnostics, a unique system of automated diagnostic processes and a multi-stage structure of the control system in all phases of operation were developed. The article presents a solution for the control system based on control consistent with the reference model. In particular, the focus was on the difficult and dangerous issue of altitude control during the docking phase. Simulation tests conducted in the MATLAB/Simulink environment, as well as laboratory and field tests on a real drone proved the correctness of the proposed solutions, which facilitated the undisturbed operation of the diagnostic module. © 2025 The Author(s).

关键词： Drones

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Deep Learning-Based Retinal Blood Vessel Segmentation Using U-Net Architecture

Deep Learning-Based Retinal Blood Vessel Segmentation Using ...

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E-Health and Bioengineering Conference (EHB)

作者： Ligia-Gabriela Boazu Marian-Alexandru Petraru Otilia Zvorișteanu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iași Iași Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Retinal blood vessel segmentation is an essential component in the diagnosis and monitoring of various ophthalmological conditions, such as diabetic retinopathy, glaucoma or macular degeneration, providing valuable information about the state of blood vessels and ocular blood circulation. Traditional methods for segmenting blood vessels from retinal images present certain limitations in terms of accuracy and robustness, especially under variable lighting conditions, the presence of artifacts or low-quality images. However, current techniques based on convolutional neural networks have demonstrated the ability to achieve precise and robust segmentations in a variety of conditions, outperforming traditional methods. Nevertheless, neural networks face challenges regarding the limited availability of retinal image datasets necessary for training and evaluating these solutions. In this paper we describe a solution for retinal blood vessel segmentation using the U-Net architecture. This neural network achieves impressive results even with very small datasets, demonstrating the efficiency and robustness of the U-Net architecture for retinal blood vessel segmentation.

关键词： Training Macular degeneration Image segmentation Neural networks Blood vessels Network architecture Retina Robustness Monitoring Biomedical imaging

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Enhancing Learning and Therapy for Children with Autism: Innovative Activities with the NAO Robot

Enhancing Learning and Therapy for Children with Autism: Inn...

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E-Health and Bioengineering Conference (EHB)

作者： Iustina-Bianca Bulai Otilia Zvorişteanu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Worldwide, autism affects 1 in 100 children, with diagnoses steadily rising since the 2000s. Romania lacks official statistics on autism prevalence, but global estimates suggest similarity. Children with autism struggle with social interactions and verbal communication, prompting interest in social robots for therapy and education. Tailored robot-assisted sessions offer potential for cognitive development, vocabulary enhancement, social interaction, and behavioral stimulation like concentration and attention. The NAO robot's user-friendly design, technical capabilities, and integrated features make it well-suited for these purposes. This paper presents activities that exemplify the concept of “learning through play,” allowing children to engage in exploration, experimentation, and effective learning in an enjoyable manner.

关键词： Autism Vocabulary Hospitals Social robots Education Biomedical engineering

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