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Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

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Sinhala Sign Language Learning System for Hearing Impaired Community 4

Sinhala Sign Language Learning System for Hearing Impaired C...

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4th International Informatics and Software engineering Conference, IISEC 2023

作者： Idushan, K.A.S. Dilshan, P.G.T. Jayasundera, P.S. Madhushan, K.L.B. Krishara, Jenny Wijendra, Dinuka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350318036

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-To-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users. © 2023 IEEE.

关键词： Learning systems

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Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

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computers, Materials & Continua 2024年第8期80卷 2005-2030页

作者： Tareq Hamadneh Khalid Kaabneh Omar Alssayed Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Zoubida Benmamoun Zeinab Monrazeri Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Faculty of Engineering Liwa CollegeAbu Dhabi41009United Arab Emirates Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making *** this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in *** theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy *** tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) *** function served as input for the SOA ***,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and *** extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the *** study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain *** simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

关键词： Optimization supply chain management sustainable lot size optimization bio-inspired metaheuristic stork

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Optimal Task Allocation for Battery-Assisted and Price-Aware Mobile Edge Computing

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 199-203页

作者： Deng, Tao You, Lei Yu, Zhanwei Yuan, Di Soochow University School of Computer Science and Technology Suzhou215006 China Technical University of Denmark Department of Engineering Technology Ballerup2750 Denmark Uppsala University Department of Information Technology Uppsala751 05 Sweden

In this letter, we propose a battery-assisted approach to improve energy efficiency for mobile edge computing (MEC) networks by utilizing the space-time-varying characteristics of electricity price. We formulate a price-aware task allocation problem (PATA) that jointly considers the cost for task computation, the cost of task offloading, and the cost of battery degradation. PATA is seemingly a mixed integer non-linear programming problem. By a graph-based reformulation, solving PATA is mapped to finding minimum cost flows or convex cost flows in the graph. This discovery reveals that the global optimum of PATA is obtained in polynomial time. Performance evaluation manifests that the proposed approach significantly outperforms other approaches. © 2019 IEEE.

关键词： Integer programming

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A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

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computers, Materials & Continua 2022年第2期70卷 2365-2380页

作者： Tallat Jabeen Ishrat Jabeen Humaira Ashraf Nz Jhanjhi Mamoona Humayun Mehedi Masud Sultan Aljahdali Research Center for Modelling and Simulation(RCMS)NUST Islamabad44000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia College of Computer Science and Information Technology Jouf UniversityAl JoufSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in *** affects the whole world through personto-person *** virus spreads by the droplets of coughs and sneezing,which are quickly falling over the ***,anyone can get easily affected by breathing in the vicinity of the COVID-19 ***,vaccine for the disease is under clinical investigation in different pharmaceutical *** now,multiple medical companies have delivered health monitoring ***,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the *** proposed approach delineates is to fill a gap between recent technology trends and healthcare *** COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible *** scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher *** cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing *** Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and *** indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

关键词： COVID-19 coronavirus cryptography Monte Carlo algorithm edge computing

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Carbon Neutrality Approaches for IoT-Enabled Applications 3

Carbon Neutrality Approaches for IoT-Enabled Applications

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Kottayam District686635 India

ISBN: (纸本)9781665462167

Unlike others, IoT-enabled technology has expanded its base in various sectors, including finance, healthcare, agriculture, energy, and so forth. Tens of thousands of applications and products have evolved in recent years, leading to a constant threat to global climate sustainability due to the underlying carbon emissions. Researchers have developed standalone methods/approaches that aimed to tackle carbon emission problems. However, an article that expresses a list of carbon-neutral solutions and their associated technological challenges is not available. This article explores the carbon emission problems of IoT-enabled applications;and, it categorizes the carbon neutrality methods. The exploratory analysis of different carbon-neutral approaches, presented in this article, can present novel ideas and answers to questions that hover over any carbon-conscious IoT-enabled application developers. © 2023 IEEE.

关键词： Internet of things

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Hybrid-Aware Attention Network Incorporating Knowledge Graph for Recommendation 14

Hybrid-Aware Attention Network Incorporating Knowledge Graph...

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14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Xu, Jianyong Zhang, Lichen Guangdong University of Technology Faculty of Computer Science and Technology Guangdong Province Guangzhou China Guangzhou Vocational and Technical University of Science and Technology Faculty of Computer Science and Technology Guangdong University of Technology School of Information Engineering Guangdong Province Guangzhou China

ISBN: (纸本)9798350336269

In recent years, knowledge graphs have been widely applied in the research of recommendation algorithms. However, most recommendation models focus on modeling item features in the relational space of the knowledge graph while neglecting the differences between the user side and the item side during feature extraction. Furthermore, they fail to comprehensively capture and fully utilize the high-order interaction relationships between users and items. To address these issues, we propose a method that combines knowledge graph and graph convolutional network, named the Hybrid-Aware Attention Network Incorporating Knowledge Graph for Recommendation (HAN). HAN jointly extracts the interaction information between users and items and the associative information of the knowledge graph to enable accurate recommendations. To validate the feasibility and effectiveness of the proposed model, experiments were conducted on three real datasets, demonstrating that the proposed algorithm achieves favorable recommendation performance compared to other benchmark models. © 2023 IEEE.

关键词： Knowledge graph

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Future Directions in Recommender System: Opportunities for Improving Recommender Systems using Machine Learning and Deep Learning Techniques 2

Future Directions in Recommender System: Opportunities for I...

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Ramachandra, H.V. Shilpa, Biradar Dr Ambedkar Institute of Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Karnataka Belagavi590 018 India Reva University School of Computer Science and Engineering Karnataka Bengaluru560064 India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560056 India

ISBN: (纸本)9798350388916

Recommender systems (RS) are algorithms which provides the users with customized suggestions for the things that are most relevant to them. The vast expansion of internet content accessibility has left users with an excessive array of options. It is therefore crucial for web platforms to offer recommendations of items to each user, in order to increase user satisfaction and engagement. This paper offers a thorough overview of the main difficulties and issues encountered in the development of recommender systems, as well as a synopsis of recent research findings and solutions. Paper began with a basic overview of recommender systems, briefly outlining the many types of RSs and covered the various research projects completed to date by numerous researchers, along with their conclusions and summaries, in the related work chapter. In the next section, we conducted an exploratory analysis of the many issues and potential fixes pertaining to recommender systems. Finally, we have summed up the various metrics needed to assess recommender system quality. © 2024 IEEE.

关键词： Collaborative learning

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Extending generalized unsupervised manifold alignment

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science China(information sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment Unsupervised Manifold Alignment relaxed integer programming Relaxed Integer Programming Frank Wolfe algorithm Frank Wolfe Algorithm

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Developing Automated Feature Selection Methods with Artificial Intelligence 1

Developing Automated Feature Selection Methods with Artifici...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Dehankar, Pooja Mandal, Sanjeev Kumar Hemelatha, S. Vijayaraghavan, P. Junnarkar, Aparna A. Bhambu, Pawan Ajeenkya D. Y. Patil University School of Engineering Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350359756

This studies article examines the capability of using artificial Intelligence (AI) to improve automatic feature choice strategies (AFSM). AFSM is a procedure that determines the maximum essential capabilities in a dataset, which allows to reduce fees and improve version accuracy. By using combining AI and AFSM strategies, its miles feasible to broaden greater sensible and effective characteristic selection algorithms that may discover the highest quality function combos. The research focuses in the main on supervised gaining knowledge of algorithms, together with assist Vector Machines (SVMs), to conduct the evaluation. It explains the idea of characteristic selection, provides an outline of existing AFSM strategies, and then explores capacity improvements that may come from combining AI and AFSM. It additionally offers an example of an AI-enabled AFSM set of rules and discusses viable advantages and concerns. The studies in the long run concludes that AI-enabled AFSM should provide better feature selection accuracy and better performance than traditional techniques when implemented to numerous datasets. © 2023 IEEE.

关键词： Support vector machines

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