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15th fib International PhD Symposium in Civil engineering, 2024

作者： Bartoli, M. Di Carlo, F. Rinaldi, Z. University of Rome Tor Vergata Department of Civil Engineering and Computer Science Engineering Via del Politecnico 1 Rome00133 Italy

ISBN: (纸本)9782940643240

Corrosion of rebars, causing a reduction of their cross-section and ductility, can significantly alter the structural behavior of reinforced concrete elements. Two main approaches can be found in literature: a first one in which corrosion effects are accounted for, in an engineering approach, through a modification of the constitutive law;a second one considering a reduction of the rebar area. In both cases a decay of the ultimate strain is considered. The work includes an experimental ongoing survey on artificially corroded steel rebars embedded in concrete prisms. Different diameters and corrosion degrees are considered. The results of the monotonic tensile tests allow the calibration of suiTable decay laws for the nominal yielding, ultimate strength, and ultimate strain. The study also addresses corrosion collecting and analyzing existing literature. to provide useful data and serve as a valuable resource for developing phenomenological degradation laws for corroded steel rebars. © Fédération Internationale du Béton–International Federation for Structural Concrete.

关键词： Tensile testing

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Bridge vertical deflection evaluation using clinometers data obtained by Micro Electro-Mechanical Systems (MEMS) sensors 15th

Bridge vertical deflection evaluation using clinometers data...

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15th fib International PhD Symposium in Civil engineering, 2024

作者： Bico, Francesco Filippo Di Carlo, Fabio Meda, Alberto Department of Civil Engineering and Computer Science Engineering University of Rome „Tor Vergata" Via Del Politecnico 1 Rome00133 Italy

ISBN: (纸本)9782940643240

This paper presents a bridge deflection measurement method based on data acquired by Micro Electro-Mechanical Systems (MEMS) sensors. The study shows a clinometer-based deflection curve method which can reconstruct the deflected shapes of bridges directly from acquired data. This work is supported by data collected from laboratory loading tests performed at University of Rome "Tor Vergata" on full scale reinforced concrete beams simulating the girders of a bridge. A data processing method based on polynomial functions was used to reconstruct the effective displacements of the structures. This new method based on data acquired by MEMS sensors does not need fixed observation points as other deflection measurement methods because the clinometers are installed on the structure directly. The goal is to build an artificial intelligence algorithm that starting from data acquired from the sensors in real time can monitor a bridge deflection and predict its future behaviour. © Fédération Internationale du Béton–International Federation for Structural Concrete.

关键词： Concrete beams and girders

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Bridge Displacement Analysis Adopting Satellite-Based Monitoring Techniques 15th

Bridge Displacement Analysis Adopting Satellite-Based Monito...

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15th fib International PhD Symposium in Civil engineering, 2024

作者： Celozzi, Teresa Di Carlo, Fabio Meda, Alberto Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Via del Politecnico 1 Rome00133 Italy

ISBN: (纸本)9782940643240

This study describes the analysis of data obtained from satellite monitoring, specifically focusing on existing bridges. It reports the results of data processing for the case study of the "Mezzocammino" Bridge in Rome using different resampling techniques. These processing operations utilized the ascending and descending datasets from the COSMO-SkyMed constellation through Multi-Temporal Differential Synthetic Aperture Radar Interferometry. The purpose of this article concerns the potential utilization of satellite-derived data for infrastructure monitoring without the need for on-site instrumentation installation. This approach offers significant advantages in managing existing heritage, becoming the primary tool for planning the management, intervention, and maintenance of existing infrastructure. © Fédération Internationale du Béton–International Federation for Structural Concrete.

关键词： Spatio-temporal data

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Deep Q-learning with hybrid quantum neural network on solving maze problems

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Quantum Machine Intelligence 2024年第1期6卷 1-11页

作者： Chen, Hao-Yuan Chang, Yen-Jui Liao, Shih-Wei Chang, Ching-Ray Department of Computer Science University of London LondonWC1E 7HU United Kingdom Department of Physics National Taiwan University Taipei106216 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106216 Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan320314 Taiwan

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches’ performance, advantages, and disadvantages to deep Q-learning problems, especially on larger-scale maze problems larger than 4 × 4. © 2024, The Author(s).

关键词： Reinforcement learning

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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Teaching Lean Software Development to Software Engineers 18th

Teaching Lean Software Development to Software Engineers

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18th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Kholmatova, Zamira Succi, Giancarlo Tulkunova, Navruza Faculty of Computer Science and Engineering Innopolis University Universitetskaya Ul. 1 Innopolis420500 Russia Department of Computer Science and Engineering Alma Mater Studiorum – Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy

ISBN: (纸本)9789819764686

This research paper proposes the implementation of a teaching strategy for the Lean software development course targeting third-year undergraduate students. Additionally, the intermediate results of applying the proposed methodology are provided. The primary objective is to suggest an instructional approach and enhance the overall learning experience. The proposed methodology employs a diverse set of research methods, including both qualitative and quantitative approaches, to comprehensively assess the outcomes associated with the Lean software development course. Specifically, the teaching strategy encompasses preliminary grade selection, mid-oral exams, quizzes, project-based learning, and a final exam. The proposed strategy will inform future advancements in curriculum design and instructional strategies, ultimately benefiting both students and the broader software engineering community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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Enhancing Anxiety Prediction: Leveraging Machine Learning with Functional Design Metrics 21

Enhancing Anxiety Prediction: Leveraging Machine Learning wi...

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21st International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2024

作者： Javed, Abqa Ajmal, Sahar Tajammal, Taliah Aslam, Muhammad Martinez-Enriquez, A.M. Sheikh, Usman Sadiq University of Engineering and Technology Department of Computer Science Lahore Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Pakistan Virtual University of Pakistan Department of Computer Science and Information Technology Lahore Pakistan CINVESTAV-IPN D.F. Department of Computer Science Mexico Medicine Bronglais General Hospital Clinical Fellow in Respiratory SY23 1ER United Kingdom

ISBN: (纸本)9798350377545

Fear, nervousness, and social phobia all contribute to anxiety episodes reaching their peak in seconds. Using data collected from patient questionnaires, this study seeks to anticipate and diagnose anxiety disorders effectively and precisely using machine learning approaches. However, current research offers standardized metrics for assessing machine learning approaches throughout the design process. This work presents a systematic method for efficiently extracting literature from digital sources and emphasizes the need for data engineers to evaluate the efficiency of machine learning approaches to assure forecast quality. The International Organization for Standardization (ISO) 9126 standard is used to assess machine learning approaches for anxiety prediction to address this gap. This evaluation considers parameters such as functionality, accuracy, resilience, throughput, explainability, privacy, and correctness. These defined characteristics serve as the benchmark to measure the efficacy of machine learning approaches in predicting anxiety disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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Incorporating Onboard Visual Sensing and Geographic Information for Lane-Level Vehicle Localization

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IEEE Sensors Journal 2025年第12期25卷 21867-21877页

作者： Lin, Huei-Yung Li, Jun-Yi Su, Ming-Wei National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan

With the recent advances in deep learning and artificial intelligence, vehicular technologies are progressing rapidly toward fully autonomous driving. Critical components of advanced driver assistance systems (ADAS) have been extensively investigated in the past decade. In this paper, we address the challenge of precise vehicle localization using visual perception and geographic information. The proposed method incorporates drivable area detection, lane line detection, and image-map matching to achieve lane-level localization accuracy. It takes advantage of digital maps and images from the onboard cameras to simultaneously optimize vehicle localization and lane detection. A confidence estimation is proposed to generate virtual lane lines for the lane marking identification. Our networks are pre-trained on the Cityscapes, CULane and TuSimple datasets, and fine-tuned with the newly proposed TRoad and TLane. In the experiments, the performance evaluated using MAE and RMSE have demonstrated a significant improvement over the conventional GPS/GNSS-based vehicle localization techniques. Source code and datasets are available at https://***/Wally0924/Enhancing-Vehicle-Localization-Using-Lane-Detection-and-Geographic-Information. © 2001-2012 IEEE.

关键词： Advanced driver assistance systems

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Artificial intelligence learning platform in a visual programming environment: exploring an artificial intelligence learning model

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Educational Technology Research and Development 2024年第2期72卷 997-1024页

作者： Chang, Jui-Hung Wang, Chi-Jane Zhong, Hua-Xu Weng, Hsiu-Chen Zhou, Yu-Kai Ong, Hoe-Yuan Lai, Chin-Feng Department of Computer Science and Information Engineering Computer and Network Center National Cheng Kung University No.1 University Rd. Tainan 701 Taiwan Department of Nursing College of Medicine National Cheng Kung University No.1 University Rd. Tainan 701 Taiwan Department of Engineering Science National Cheng Kung University No.1 University Rd. Tainan 701 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No.1 University Rd. Tainan 701 Taiwan

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students’ learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study employed the use of a self-built AI platform (Ladder) for students to systematically learn and apply AI learning model established by the partial least squares (PLS) method to investigate the influence between variables (learning attitudes, self-regulated learning, AI anxiety, individual impact, computational thinking abilities, cognitive styles). This study was particularly conducted in the department of computer science and Information engineering of a top national university in Southern Taiwan. The valid data were collected from 65 students (55 male students;10 female students). Furthermore, this study demonstrated the relationship between cognitive style, self-regulated learning and computational thinking. For the first time, it explored the impact of AI anxiety and completed existing research on it. The results of this study show that interest in learning positively affects learning attitudes. In addition, learning attitudes have a positive influence on each individual’s performance. Based on multiple theories and the artificial intelligence learning platform, the model proposed in this study effectively understood students’ learning status. © 2023, Association for Educational Communications and Technology.

关键词： Artificial intelligence Cognitive style Computational thinking Learning platform Self-regulated learning

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