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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 151-166页

作者： Bogdan, Ioana Corina Simion, Emil Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers "Transilvania" University of Brasov & CiTi Brasov Romania Department of Mathematical Methods and Models National University of Science and Technology POLITEHNICA Bucharest & CiTi Bucharest Romania

Cybersecurity is a field that unifies concepts from various fundamental areas: mathematics, physics, computer science, electronics, sociology, and management. This field is challenging for government authorities, academic institutions, and private entities alike. By pooling the resources of these three actors, we can build secure and resilient systems. In this context, several challenges emerge, such as the exchange of knowledge and expertise, collaborative research projects, the organization of internships and scholarship programs, the exchange of threat intelligence, training, and awareness, and finally, the standardization and regulatory component. The evolving nature of cyber threats and the growing sophistication of attackers necessitate robust and adaptable security frameworks. A "defense-in-depth security model" is mandatory in this context as it offers multiple layers of protection, ensuring that if one defense mechanism fails, others remain in place to prevent breaches. In this paper, we propose a defense-in-depth security model and highlight how key points within the model can be interconnected. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Critical infrastructures

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REES AAEE special issue on engineering education research capability development: introduction by guest editors

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Australasian Journal of engineering Education 2023年第1期28卷 2-7页

作者： Dart, Sarah Seniuk Cicek, Jillian Sohoni, Sohum Learning and Teaching Unit & Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Centre for Engineering Professional Practice and Engineering Education University of Manitoba Winnipeg Canada Department of Electrical Engineering and Computer Science Milwaukee School of Engineering Milwaukee United States

Since the late 20th century, engineering Education Research (EER) has been expanding globally as a field, although its identity varies across institutions and countries around the world. This diversity in how EER is experienced across contexts impacts how capability is developed, including identities, knowledge, practices, agendas, funding, and pathways. The theme of ‘engineering Education Research Capability Development’ was explored in papers, presentations, and workshops throughout the jointly held 2021 Research in engineering Education Symposium and Australasian Association for engineering Education conference. This special issue presents a continuation of that theme. The papers collectively contribute to expanding global understanding of the field, with implications for future capacity building efforts in engineering education practice and research. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： engineering education

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Data-Driven Evolutionary Algorithms for Optimizing Pumping Stations in Water Distribution Networks: Classifier-Guided Search Space Reduction 27th

Data-Driven Evolutionary Algorithms for Optimizing Pumping S...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Faúndez-Lizama, Thalía Gajardo-Sepúlveda, Nicolás Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766060

In the context of increasing water scarcity and the need to enhance water distribution network efficiency, this study focuses on optimizing the design of pumping stations using data-driven evolutionary algorithms guided by classification models. These facilities are critical components in water networks due to their high energy consumption and role in maintaining necessary water pressure. The proposed approach integrates a machine learning classifier model within the evolutionary process to pre-label solutions as feasible or infeasible, thereby reducing search space and computational costs associated with hydraulic simulations. Classification models include support vector classifier, artificial neural networks, and random forest. These models are trained on data generated from initial evolutionary optimization, enabling the classifier to predict the viability of new solutions. Results show that all tested classification models reduced the number of objective function evaluations and consequently, required hydraulic simulations. In some cases, the optimal solution defined by the original method was improved, demonstrating dual benefits by reducing both cost and computational effort. This work highlights that data-driven evolutionary optimization can effectively tackle highly complex problems, offering potential future applications in other areas of civil engineering and water resource management. Implementing this methodology promises significant time and resource savings in large-scale optimization processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hydraulic models

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Efficient One-Stage Deep Learning for Text Detection in Scene Images

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 65-71页

作者： Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Faculty of Exact Sciences Natural And Life Sciences University Mohamed Khider of Biskra Algeria Computer Science Department University Mohamed Khider of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy

Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, which may not be suitable for handling the diverse range of unstructured text scenarios encountered in real-world environments. To overcome this challenge, deep learning techniques have emerged as a powerful tool for adaptively learning and extracting features to digitize text. In this paper, we introduce a novel approach based on the YOLOv5 architecture for text detection. Our method leverages the strengths of YOLOv5, including its efficient and lightweight architecture, multi-scale prediction capability, and advanced data augmentation techniques. By employing these features, our proposed model can accurately identify regions in an image that are more likely to contain text. Through extensive experimentation on popular benchmark datasets, we have evaluated the performance of our proposed model. The results have demonstrated that our method outperforms state-of-the-art approaches in terms of both detection efficiency and accuracy. This indicates the effectiveness of our approach in handling the challenges posed by text detection in natural images. © Editura ELECTRA 2024. All rights reserved.

关键词： Deep Learning Natural scene image Text detection Yolov5

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Data-Driven Genetic Algorithm for the Optimization of Water Distribution Networks: A New Surrogate Model for Estimating Investment and Operational Costs in Pumping Stations 27th

Data-Driven Genetic Algorithm for the Optimization of Water ...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Gajardo-Sepúlveda, Nicolás Faúndez-Lizama, Thalía Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766039

The increase in water consumption and demand has highlighted the need to improve the efficiency of water distribution networks (WDNs). Pumping stations (PS) represent a significant challenge due to their high energy consumption and associated operational costs. This study presents a data-driven evolutionary optimization methodology focused on predicting cost and penalties in the design and operation of PS. The methodology integrates the functioning of genetic algorithms with machine learning techniques, developing a surrogate model capable of predicting associated costs and increasing the computational efficiency of optimization models. A case study is presented to validate the methodology, showing a significant reduction of 31.8% in evaluation times and a decrease in optimization time compared to traditional models. The results indicated that despite the reduction in computational effort, the increase in the final cost of the network was minimal, with only a 2.42% increase compared to the baseline model. These findings underscore the effectiveness of combining machine learning with genetic algorithms for the optimization of PS in WDNs, improving computational efficiency while maintaining high standards in solution quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Modernizing The Shopping Experience: The Smart Shopping Cart 6

Modernizing The Shopping Experience: The Smart Shopping Cart

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6th Iraqi International Conference on engineering Technology and its Applications, IICETA 2023

作者： Shakir, Aboothar Mahmood Mudhafar, Yousif Al-Muqarm, Abbas M. Ali The Islamic University College of Technical Engineering Computer Techniques Engineering Department Najaf Iraq University of Kufa Faculty of Computer Science and Mathematics Department of Computer Science Najaf Iraq

ISBN: (纸本)9798350303339

The holiday season brings out the crowds at shopping centers in major cities. When there are substantial savings to be obtained, this becomes even more true. People today often fill their shopping carts with a wide selection of products. Once a customer has finished shopping, he or she should head to the counter to settle the payment. It takes time for the cashier to create the bill using a barcode scanner. Because of this, there are often substantial wait times at the cash registers. This project proposes a solution for retail centers to address the issues mentioned above. This might be accomplished by installing RFID tags on all mall merchandise and retrofitting all shopping carts with a radio-frequency identification reader and a liquid crystal display. Any item placed in the shopping cart will have its barcode scanned automatically, and the trolley's digital signboard will update to show the item's name and price. When an item is removed from a shopping cart, its cost is subtracted from the grand total, and the relevant data is transmitted to the central billing unit through radio-frequency (RF) modules. Customers may save a ton of time since billing can be done right on the tram. The researchers worked on developing a shopping cart, which is smart, and this system allows for faster checkout and payment of the bill in a secure manner, and the adoption of technology in the store increases the demand for it continuously, the computer will remember the barcode data you enter and compare it to its database, from which it will get the necessary data. The database and RF model will be connected through software. Small radio-frequency (RF) electrical modules called a 'transmitter' and 'receiver' can be used to transmit and receive signals between any two devices. The data is sent from the transmitter module to the receiver module, which then processes the data. The group believes that the intelligent shopping cart will result in a more dependable checkout procedure and

关键词： Liquid crystal displays

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Enhancing the Efficiency of Navigation Systems: Dijkstra Algorithm Optimization Through Integration of Douglas-Peucker Line Simplification Technique 23

Enhancing the Efficiency of Navigation Systems: Dijkstra Alg...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Haq, Muhammad Yasir Anshari Sianturi, Riswan Septriayadi Pinandito, Aryo Informatics Engineering Department Universitas Brawijaya Faculty of Computer Science Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

Smartphones have grown popular in the current digital era among people. Map-based applications are one of many that are used daily by people to acquire information about specific locations. Google Maps and Waze are two popular examples of map-based applications for travel and navigation, as they provide detailed information while the user navigates from one point to another. One of the most commonly used algorithms in map-based applications is the Dijkstra algorithm. Although the Dijkstra algorithm can determine the shortest route between two points, there is an issue when the selected route involves a large number of data points that need to be processed. The more data points involved, the longer it takes to do a route calculation in the Dijkstra algorithm. This can lead to a degradation in system performance, especially on smartphones with limited resources. To address this issue, the Douglas-Peucker algorithm is proposed as a solution to reduce the number of points involved in the calculation using the line simplification technique. Line simplification means reducing unnecessary points that are considered unimportant. This study suggested that integrating the Douglas-Peucker line simplification technique before Dijkstra could improve the system performance more than the system without integrating line simplification in terms of reducing both memory usage and the time required to compute the solution. © 2023 ACM.

关键词： Navigation systems

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Iot - Enabled Technologies for Sustainable Smart Agriculture and their Comprehensive Survey

Iot - Enabled Technologies for Sustainable Smart Agriculture...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Santosh, S. Raghavendra, R. Deemed-to-be University Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bangalore India Deemed to Be University Department of Computer Science and IT Jain Bangalore India

ISBN: (纸本)9798350322309

Both the quality and amount of food demanded have increased, necessitating agricultural technology and intensification. Agricultural innovation is booming thanks to the Internet of Things, or IoT, is a cutting-edge technology. Research institutions and scientific organizations are creating IoT-based goods and solutions to solve a range of agricultural concerns. This study offers a thorough literature assessment by examining IoT technology and their current applications in a range of farming-related businesses. The comprehensive reviews of the literature conducted for this study were entirely based a review of the literature publications published in government publications during the previous ten years. Carefully selected articles from a wide variety have been arranged into courses. The major objective gathers all pertinent studies on IoT agricultural applications, sensors/devices, communication protocols, and community types. Additionally, it addresses the main problems and challenges that are the focus of modern agricultural research. © 2023 IEEE.

关键词： Internet of things

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An Analysis of Logit Learning with the r-Lambert Function 63

An Analysis of Logit Learning with the r-Lambert Function

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Gavin, Rory Cao, Ming Paarporn, Keith University of Groningen Faculty of Science and Engineering Netherlands University of Colorado Department of Computer Science Colorado Springs United States

ISBN: (纸本)9798350316339

The well-known replicator equation in evolutionary game theory describes how population-level behaviors change over time when individuals make decisions using simple imitation learning rules. In this paper, we study evolutionary dynamics based on a fundamentally different class of learning rules known as logit learning. Numerous previous studies on logit dynamics provide numerical evidence of bifurcations of multiple fixed points for several types of games. Our results here provide a more explicit analysis of the logit fixed points and their stability properties for the entire class of two-strategy population games - by way of the r-Lambert function. We find that for Prisoner's Dilemma and anti-coordination games, there is only a single fixed point for all rationality levels. However, coordination games exhibit a pitchfork bifurcation: there is a single fixed point in a low-rationality regime, and three fixed points in a high-rationality regime. We provide an implicit characterization for the level of rationality where this bifurcation occurs. In all cases, the set of logit fixed points converges to the full set of Nash equilibria in the high rationality limit. © 2024 IEEE.

关键词： Bifurcation (mathematics)

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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