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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning 53

Kidney Tumor Recognition from Abdominal CT Images using Tran...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Wasi, Sefatul Alam, Saadia Binte Rahman, Rashedur Amin, M Ashraful Kobashi, Syoji Independent University Center for Computational & Data Sciences Department of Computer Science and Engineering Dhaka Bangladesh1299 Bangladesh University of Hyogo Graduate School of Engineering 2167 Shosha Hyogo Himeji Japan

ISBN: (纸本)9781665464161

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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An Enhanced Energy Efficient Clustering for Biomedical Wireless Sensor Networks

An Enhanced Energy Efficient Clustering for Biomedical Wirel...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Vennira Selvi, G. Hl, Chandrakala Sheeja Kumari, V. Ponmaniraj, S. Anbazhagan, K. Nanthini, S. Presidency University Rajanukunte Yelahanka School of Information Science Bengaluru India Presidency University Rajanukunte Yelahanka School of Computer Science and Engineering Bengaluru India Saveetha School of Engineering Simats Department of Computational Intelligence Chennai India Saveetha School of Engineering Simats Department of Data Science Chennai India

ISBN: (纸本)9798350317060

Biomedical Wireless Sensor Networks (BWSN) is major technique for Health Care applications for providing quality of life with minimum cost. To enforce the quality of medical care provided to the citizen, such networks should be integrated with existing network infrastructures. The requirement of quality of service should be concentrated on development stages and are monitored for the duration of network operation. The life time of the network is most relevant feature for ensuring the quality of medical care requirements. In order to increases the network lifetime, maintaining the remaining energy of each sensor node with reliable communication link throughout the network. An energy aware clustering for biomedical wireless sensor network (EACBWSN) is proposed for increasing the network lifetime by minimizing the energy consumption among sensor nodes and provides a reliable communication link between sensor nodes. The proposed algorithm efficiently prolong the lifetime of the network when compared with existing algorithm. © 2023 IEEE.

关键词： Sensor nodes

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Late and Early Blight Diseases Identification of Potatoes with a Light Weight Hybrid Transfer Learning Model

Late and Early Blight Diseases Identification of Potatoes wi...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Siddique, Abu Zobayer Bin Das, Shoibal Tabassum, Poonam Tasir, All Moon Roy, Shovon Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

Potatoes are one of the world's most important commodities, and leaf maladies such as early and late blight can substantially reduce their yield and quality. Hence, both farmers and researchers must prioritize quick and precise illness diagnosis. In our research, we propose a strategy based on transfer learning for classifying toxic and diseased potato leaf tissue. We specifically used our dataset of potato leaf photos to fine-tune the Mobile-Net model, which was a pre-trained convolutional neural network. To enhance the model's functionality, we also added a few more layers. Our study found that, in comparison to other state-of-the-art methods, our methodology outperformed them all by achieving a multi-class classification accuracy of 99%. Our method can be used to detect and monitor potato leaf maladies in real-world situations, which could eventually contribute to enhancing potato productivity and food security. © 2023 IEEE.

关键词： Deep learning

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Ensemble Filter Scenario Based Control on Linearized Power System Manifold

Ensemble Filter Scenario Based Control on Linearized Power S...

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International Conference on System Reliability and science (ICSRS)

作者： Bahri Uzunoğlu Department of Electrical Engineering Computational and data-enabled science and engineering in energy systems Uppsala University Sweden Department of Mathematics Florida State University USA

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

This article integrates the widely recognised ensemble filters that are employed for power system state estimation and their ensemble scenarios to controls for power systems linearised on manifolds. The approach has the effect of directly providing constraint scenarios for any optimal real-time fast power system management for any ensemble-based state estimator used in the power system, thereby reducing the computational task for both radial and meshed power systems and addressing the computational needs of stochastic external state parameter that is not controlled by the grid operator. New and unparalleled obstacles have surfaced in the recent past for power systems. Tighter guidelines for grid reliability and service quality, the growing spread of electric mobility that has given rise to a new class of intermittent loads with distinct spatiotemporal patterns, and the growing use of distributed microgenerators from renewable power sources-which are frequently characterised by unpredictable behavior-are some of these challenges. For proof of concept, the method is shown on a simple example.

关键词： Manifolds Renewable energy sources Uncertainty Decision making Transforms Power systems Spatiotemporal phenomena Kalman filters Reliability State estimation

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gonzalez-Quesada, Juan Carlos Cabrerizo, Francisco Javier Herrera-Viedma, Enrique Pedrycz, Witold Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Alberta Department of Electrical & Computer Engineering EdmontonABT6R 2V4 Canada

ISBN: (纸本)9781665410205

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus. © 2024 IEEE.

关键词： Information granules

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Oladejo, Sunday Oladayo Ekwe, Stephen Obono Sumbwanyanbe, Mbuyu Mnkandla, Ernest Shoewu, Oluwagbemiga Omotayo University of South Africa Departments of Computer Science and Elect. Engineering CSET Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Stellenbosch University School for Data Science and Computational Thinking Stellenbosch South Africa Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa University of South Africa Department of Electrical Engineering CSET Johannesburg South Africa University of South Africa Department for Computer Science CSET Johannesburg South Africa

ISBN: (纸本)9798350358155

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages. © 2024 IEEE.

关键词： data Analysis Machine Learning Network Management Network Slicing Network Virtualization

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Emergency Care Patient Prediction using Electronic Health Records (EHR) data: An End-to-End Machine Learning Pipeline

Emergency Care Patient Prediction using Electronic Health Re...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Maruful Abedin Sumon Tor A. Kwemebe Venkata K. Melapu Md Mohiuddin Hasan Computational Data-Enabled Science and Engineering Jackson State University Jackson USA Department of Mathematics and Computer Science Jackson State University Jackson USA Computer Science and Engineering Jackson State University Jackson USA

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than $75 \%$ is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency.

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