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How AI-enabled SDN technologies improve the security and functionality of industrial IoT network:Architectures,enabling technologies,and opportunities

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Digital Communications and Networks 2023年第6期9卷 1351-1362页

作者： Jinfang Jiang Chuan Lin Guangjie Han Adnan MAbu-Mahfouz Syed Bilal Hussain Shah Miguel Martínez-García Department of Internet of Things Engineering Hohai UniversityChangzhou213022China Software College Northeastern UniversityShenyang110819China Council for Scientific and Industrial Research(CSIR) Pretoria0184South Africa School of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUK Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLE113TTUK Department of Electrical and Electronic Engineering Science University of JohannesburgJohannesburgSouth Africa

The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication *** brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network ***,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT *** paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT *** surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional *** with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also ***,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

关键词： Industrial internet of things(IIoT) Industry 4.0 Artificial intelligence(AI) Machine intelligence Software-defined networking(SDN)

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges

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Environmental Science and Pollution Research 2024年 1-14页

作者： Abubakar, Umar Alfa Lemar, Gul Sanga Bello, Al-Amin Danladi Ishaq, Aliyu Dandajeh, Aliyu Adamu Jagun, Zainab Toyin Houmsi, Mohamad Rajab Department of Water Resources and Environmental Engineering Ahmadu Bello University Kaduna Zaria1045 Nigeria Department of Botany Faculty of Biology Kabul University Kart-E-Char Kabul Afghanistan School of Built Environment Engineering and Computing Leeds Beckett University City Campus Leeds United Kingdom New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar Nasiriyah64001 Iraq

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R2, has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications. © The Author(s) 2024.

关键词： Anaerobic digestion

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Exact Instability Margin Analysis and Minimum-Norm Strong Stabilization - Phase Change Rate Maximization

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IEEE Transactions on Automatic Control 2024年第4期69卷 2084-2099页

作者： Hara, Shinji Kao, Chung-Yao Khong, Sei Zhen Iwasaki, Tetsuya Hori, Yutaka Tokyo Institute of Technology Global Scientific Information and Computing Center Tokyo152-8550 Japan National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung80421 Taiwan University of California at Los Angeles Department of Mechanical and Aerospace Engineering Los AngelesCA90095 United States Keio University Applied Physics and Physico Informatics Yokohama223-8522 Japan Monaco

This article is concerned with a new optimization problem named 'phase change rate maximization' for single-input-single-output linear time-invariant systems. The problem relates to two control problems, namely robust instability analysis against stable perturbations and minimum-norm strong stabilization. We define an index of the instability margin called 'robust instability radius (RIR)' as the smallest H∞-norm of a stable perturbation that stabilizes a given unstable system. This article has two main contributions. It is first shown that the problem of finding the exact RIR via the small-gain condition can be transformed into the problem of maximizing the phase change rate at the peak frequency with a phase constraint. Then, we show that the maximum is attained by a constant or a first-order all-pass function and derive conditions, under which the RIR can be exactly characterized, in terms of the phase change rate. Two practical applications are provided to illustrate the utility of our results. © 1963-2012 IEEE.

关键词： Robust control

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High-performance computing at a crossroads

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Science (New York, N.Y.) 2025年第6736期387卷 829-831页

作者： Deelman, Ewa Dongarra, Jack Hendrickson, Bruce Randles, Amanda Reed, Daniel Seidel, Edward Yelick, Katherine Information Sciences Institute University of Southern California Marina del Rey CA United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Oak Ridge National Laboratory Oak Ridge TN United States Department of Mathematics University of Manchester Manchester United Kingdom Computing Directorate Lawrence Livermore National Laboratory Livermore CA United States Department of Biomedical Engineering Duke University Durham NC United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT United States Office of the President University of Wyoming Laramie WY United States Department of Electical Engineering and Computer Sciences University of California Berkeley Berkeley CA United States

Long-term plans and comprehensive vision are needed.

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LINEAR QUADRATURE METHOD OF MOMENTS FOR SOLVING THE PDF MODEL EQUATION FOR TURBULENT REACTIVE FLOWS

LINEAR QUADRATURE METHOD OF MOMENTS FOR SOLVING THE PDF MODE...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Wang, Yunchu Wang, Yize Yuan, Li NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9780791888667

Quadrature-based moment methods (QBMMs) are an alternative approach for the numerical solution of the probability density function (PDF) model equation for turbulent combustion. In this work, we propose a new QBMM called Linear Quadrature Method Of Moments (LQMOM) for the univariate PDF case. We use a set of predefined quadrature nodes and weights in the sample space to establish linear equations for the PDF values at the nodes in the moment inversion. The LQMOM is more economic than the original QMOM which needs to solve nonlinear equations in the moment inversion. However, the LQMOM may return negative PDF values for a non-smooth PDF. So we construct the LQMOM-QMOM hybrid algorithm. Further, we develop the linear Conditional Quadrature Method Of Moments (LCQMOM) and a simplified version (LCQMOM-S) for the multivariate PDF case by referring to the main idea of the original CQMOM. The LQMOM-QMOM hybrid algorithm is tested in a plug-flow reactor example, and the LQMOM(-S)-CQMOM hybrid algorithms in conjunction with a LES method are tested in two compressible turbulent reactive flow examples. The numerical results show that the hybrid algorithms can reduce 20-35% computational cost compared with the respective QMOM and CQMOM. Copyright © 2024 by ASME.

关键词： Probability density function

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Phase-field-based lattice Boltzmann method for containerless freezing

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Physical Review E 2024年第3期110卷 035301页

作者： Jiangxu Huang Lei Wang Zhenhua Chai Baochang Shi School of Mathematics and Statistics School of Mathematics and Physics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing

In this paper we first propose a phase-field model for the containerless freezing problems, in which the volume expansion or shrinkage of the liquid caused by the density change during the phase change process is considered by adding a mass source term to the continuum equation. Then a phase-field-based lattice Boltzmann (LB) method is further developed to simulate solid-liquid phase change phenomena in multiphase systems. We test the developed LB method by the problem of conduction-induced freezing in a semi-infinite space, the three-phase Stefan problem, and the droplet solidification on a cold surface, and the numerical results are in agreement with the analytical and experimental solutions. In addition, the LB method is also used to study the rising bubbles with solidification. The results of the present method not only accurately capture the effect of bubbles on the solidification process, but also are in agreement with the previous work. Finally, a parametric study is carried out to examine the influences of some physical parameters on the sessile droplet solidification, and it is found that the time of droplet solidification increases with the increase of droplet volume and contact angle.

关键词： Drops & bubbles Liquid-solid phase transition

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HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach 31

HPC Application Parameter Autotuning on Edge Devices: A Band...

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31st Annual IEEE International Conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Hossain, Abrar Badawy, Abdel-Hameed A. Islam, Mohammad A. Patki, Tapasya Ahmed, Kishwar Department of Electrical Engineering and Computer Science The University of Toledo United States Department of Electrical and Computer Engineering New Mexico State University United States Department of Computer Science and Engineering The University of Texas Arlington United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

ISBN: (纸本)9798331509095

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices. © 2024 IEEE.

关键词： Edge computing

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Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients

arXiv

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arXiv 2024年

作者： Bastiani, Zachary Kirby, Robert M. Hochhalter, Jacob Zhe, Shandian Kahlert School of Computing United States Scientific Computing and Imaging Institute United States Department of Mechanical Engineering University of Utah United States

This paper proposes a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery. Despite the success of the state-of-the-art method, DSR, it is built on recurrent neural networks, purely guided by data fitness, and potentially meet tail barriers, which can zero out the policy gradient and cause inefficient model updates. To overcome these limitations, we use transformers in conjunction with breadth-first-search to improve the learning performance. We use Bayesian information criterion (BIC) as the reward function to explicitly account for the expression complexity and optimize the trade-off between interpretability and data fitness. We propose a modified risk-seeking policy that not only ensures the unbiasness of the gradient, but also removes the tail barriers, thus ensuring effective updates from top performers. Through a series of benchmarks and systematic experiments, we demonstrate the advantages of our approach. © 2024, CC BY-NC-SA.

关键词： Recurrent neural networks

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Rigorous biaxial limit of a molecular-theory-based two-tensor hydrodynamics

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Journal of Differential Equations 2023年第1期366卷 862-911页

作者： Li, Sirui Xu, Jie School of Mathematics and Statistics Guizhou University Guiyang 550025 China LSEC NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing (ICMSEC) Academy of Mathematics and Systems Science (AMSS) Chinese Academy of Sciences Beijing China

We consider a two-tensor hydrodynamics derived from the molecular model, where high-order tensors are determined by closure approximation through the maximum entropy state or the quasi-entropy. We prove the existence and uniqueness of local in time smooth solutions to the two-tensor system. Then, we rigorously justify the connection between the molecular-theory-based two-tensor hydrodynamics and the biaxial frame hydrodynamics. More specifically, in the framework of Hilbert expansion, we show the convergence of the solution to the two-tensor hydrodynamics to the solution to the frame hydrodynamics. © 2023 Elsevier Inc.

关键词： Biaxial limit Frame hydrodynamics Liquid crystals Two-tensor hydrydynamics

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