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Association for Materials Protection and Performance Annual Conference and Expo 2024

作者： Karlsdottir, Sigrun Nanna Straume, Erlend Boakye, Gifty Oppong Pórhallsson, Andri Ísak Práinsdóttir, María Ýr Faculty of Computer Science Industrial and Mechanical Engineering University of Iceland Hjardarhagi 2-6 Reykjavik Iceland

Major challenges are encountered in the design and construction of deep superhot (>300°C) geothermal wells due to higher temperatures and pressure, and increased corrosiveness of the geothermal fluid. The corrosiveness of the geothermal fluid is due to the chemical composition which includes corrosive gases such as HCl, H2S and CO2. In this study L80-type 1 carbon steel and L80-13Cr stainless steel API casing materials were tested in a simulated superhot geothermal well environment at 350°C in water with Cl, H2S and CO2 for 168 hrs. in both vapor and liquid phases in high temperature and high pressure (HTHP) autoclave to investigate their corrosion behavior. The results showed that both API casing materials, L80-type 1 and L80-13Cr, were prone to corrosion attack in the simulated superhot deep geothermal well environment. The L80 grade Type 1 carbon steel tested in the liquid phase exhibited the highest corrosion rate and demonstrated vulnerability to localized corrosion or cracking in both liquid and vapor phases. Similarly, the L80 grade 13Cr stainless steel is not considered a suitable choice for harsh environments rich in H2S, CO2, and Cl at superheated temperatures. While it was prone to localized corrosion, it exhibited a low uniform corrosion rate ( © 2024 Association for Materials Protection and Performance (AMPP). All rights reserved.

关键词： Localized corrosion

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An Efficient Intrusion Detection Framework for Industrial Internet of Things Security

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computer Systems science & engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(IoT)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console *** the increased use of Industrial IoT(IIoT)applications,the risk of brutal cyber-attacks also *** leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for IoT infrastructure against any malicious ***,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the IoT infrastructure.A comprehensive evaluation examined on a new available benchmark TON_IoT dataset is *** data-driven IoT/IIoT dataset incorporates a label feature indicating classes of normal and attack-targeting IoT/IIoT ***,this data involves IoT/IIoT services-based telemetry data that involves operating systems logs and IoT-based traffic networks collected from a realistic medium-scale IoT *** is to classify and recognize the intrusion activity and provide the intrusion detection objectives in IoT environments in an efficient ***,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion *** performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed *** experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the IoT/IIoT infrastructure on most of the employed *** addition,the proposed work accomplishes high performance compared to other recent rela

关键词： Attacks intrusion detection machine learning deep learning industrial IoT TON_IoT dataset

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TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images 4th

TinyML Model for Solar Cell Defect Classification Based on E...

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4th International Conference on Electronic engineering and Renewable Energy Systems, ICEERE 2024

作者： Boubaker, S. Moussaoui, C. Mellit, Adel Benghanem, M. Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia Faculty of Sciences and Technology University of Jijel Jijel18000 Algeria Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

ISBN: (纸本)9789819606436

Being largely utilized as alternative sources of energy, photovoltaic (PV) panels are being deployed in many locations. However, those panels are extensively being prone to faults. A careful detection and diagnosis of those faults becomes a must for solar energy engineers, practitioners and decision-makers in order to extract the maximum of energy from them. The integration of artificial intelligence (AI) in the domain of solar energy management has become a trend in the few previous years because of the high potential provided by machine learning (ML) and deep learning (DL) algorithms in handling issues related to PV systems such as control and fault detection and isolation (FDI). In this paper, TinyML models for defect classification of photovoltaic modules based on electroluminescence (EL) images were developed. An open-source platform, Edge Impulse, was used to design and deploy the models. Two convolutional neural network models (mobileNetV1 and mobileNetV2) have been used and compared. A dataset of EL with three anomalies was used to develop the models. The validation test shows an accuracy of 88.44 and 76.38% for respectively MobileNet-V1 and MobileNet-V2. The quantized (Int8) model has been integrated into a microcontroller (Arduino Portenta H7) for real-time application. The obtained results can be considered as promising and can be extended to other case studies with more defects and larger datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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A Hybrid Approach with BRKGA and Data Mining for the Early/Tardy Scheduling Problem 13

A Hybrid Approach with BRKGA and Data Mining for the Early/T...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mendonça, Israel Fatyanosa, Tirana Noor Aritsugi, Masayoshi Silva, Pedro Henrique González Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan Brawijaya University Faculty of Computer Science Malang Indonesia Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational experiments underscore the potential of this hybrid approach that, although requiring slightly longer computational times, is better than the previous baseline BRKGA algorithm in terms of solution quality. © 2024 IEEE.

关键词： Scheduling algorithms

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Development of Fuzzy Based Models for Detection System with More Accuracy for the Implementation of Healthcare/Clinical System 4

Development of Fuzzy Based Models for Detection System with ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Pune India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Many electronic infection detection systems often use binary classification techniques, which identify patient data as either pathology-indicating or normal in relation to different forms of infections. The Moni-ICU system, which is used in the critical care units of Vienna General Hospital, is noteworthy for introducing a novel third category called borderline. This categorization indicates patient data that partially satisfies the corresponding needs by closely aligning with virus-related clinical concepts or infections related to healthcare (HAI) surveillance categories. The probability of classifying patient data into normal, borderline, of pathological categories with respect to infection-related medical ideas and HAI definitions was calculated by integrating fuzzy sets of propositional fuzzy rules. This deviation from dichotomous approaches prevents the possible misunderstanding of borderline findings with normal ones, a major difficulty in binary classifications. Data collection included a thorough analysis of 5099 individuals treated over a two-year period, including adult patients across 10 ICUs at Vienna General Hospital. The Moni-ICU knowledge base, which includes fuzzy sets and rules for four top-level HAI definitions and 10 infection-related clinical concepts, made the categorization process easier. In order to verify the accuracy and dependability of the system, the assessment focused on how suitable borderline classifications were in relation to normal values for related HAI criteria. © 2024 IEEE.

关键词： Fuzzy rules

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Integration of Backscatter Modulation in FMCW Radar Systems: Demonstration at 24 GHz with Slow and Fast Modulation Rates 7

Integration of Backscatter Modulation in FMCW Radar Systems:...

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7th IEEE MTT International Conference on Microwaves for Intelligent Mobility, ICMIM 2024

作者： Domnik, Christoph Meuleners, Michael Degen, Christoph Faculty of Electrical Engineering and Computer Science Hochschule Niederrhein - University of Applied Sciences Krefeld47805 Germany

ISBN: (纸本)9783800763641

This paper examines a cutting-edge approach that melds backscatter modulation principles with a frequency modulated continuous wave (FMCW) radar system. Building upon radio-frequency identification (RFID) backscatter modulation techniques, we present a fusion of FMCW radar, a reflecting antenna and load modulation. This combination of backscatter communication with radar describes an alternative candidate to those being typically discussed within the field of joint communication and sensing. In this paper we describe the working principle by means of simulation and prove its feasibility by measurements with a radar operating at a frequency of 24 GHz. We thereby differentiate between a slow symbol rate, for which a data symbol spans one or more radar chirps, and a fast symbol rate with more than one symbol being modulated onto one radar chirp. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Backscattering

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VeFog - Efficient Message Dissemination in VANETs based on Edge, Fog and Cloud Computing 47

VeFog - Efficient Message Dissemination in VANETs based on E...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Jelić, Borna Gluhaković, Mario Mijić, David Ivešić, Toni Balen, Josip TTTech Auto Osijek Croatia Faculty Of Electrical Engineering Computer Science And Information Technology Osijek Croatia

ISBN: (纸本)9798350382495

Technological progress has had a dramatic impact on the automotive industry and the concept of intelligent and connected vehicles is developing rapidly. Vehicles are being equipped with various in-vehicle and environmental sensors that generate huge amounts of data, as well as onboard units that can calculate, store and communicate data with other vehicles, infrastructure and the environment. This has led to the development of Vehicular Ad-hoc Networks (VANETs), which face various challenges due to a heterogeneous and highly dynamic environment, where traditional solutions need to be improved and adapted to meet the networking and processing requirements. The integration of edge and fog computing in VANETs reduces the overhead by placing the necessary resources at the edge of the network closer to the information source. In this paper, we propose VeFog - the new architecture based on edge and fog computing for efficient data processing and dissemination in VANETs. The performance evaluation and simulation results show that the VeFog architecture is efficient, reduces processing time and saves resources with minimal infrastructure utilization. © 2024 IEEE.

关键词： Fog computing

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Hybrid Fuzzy Decision-Making Model 4

Hybrid Fuzzy Decision-Making Model

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Srv Media Pvt. Ltd India India Pune India Department of Cse India Computer Science and Engineering Faculty of Engineering and Technology Parul University India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

Establishing a sustainable the healthcare system necessitates the development of social, economic, and environmental norms. Although many studies concentrate on these elements separately, an integrated method of handling evaluating environmentally friendly development in healthcare institutions is conspicuously lacking. To address this gap, this paper presents a technique for studying indicators of sustainable development in healthcare organizations by modifying dimensions, criteria, and characteristics. In all, the three sustainability aspects are covered by the study with 62 traits and 15 criteria identified. Furthermore, a unique implementation technique is proposed to enable this methodology to be applied to real-world issues. This paper makes three contributions: it provides a new methodology for gathering basic data to measure long-term viability levels in healthcare systems;it presents the ability to identify implementation weaknesses in sustainability aspects;and it determines the sustainability level of the healthcare system under imprecise conditions. The study findings provide practitioners and specialists with significant perspectives to assess the sustainability levels of healthcare systems and improve their overall efficacy. © 2024 IEEE.

关键词： Sustainable development goals

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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