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High‑Order ADER Discontinuous Galerkin Schemes for a Symmetric Hyperbolic Model of Compressible Barotropic Two‑Fluid Flows

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Communications on Applied Mathematics and Computation 2024年第4期6卷 2119-2154页

作者： Laura Río‑Martín Michael Dumbser Department of Civil Environmental and Mechanical EngineeringUniversity of TrentoVia Mesiano 77Trento 38123Italy Department of Information Engineering and Computer Science University of Trentovia Sommarive 9PovoTrento 38123Italy

This paper presents a high-order discontinuous Galerkin(DG)finite-element method to solve the barotropic version of the conservative symmetric hyperbolic and thermodynamically compatible(SHTC)model of compressible two-phase flow,introduced by Romenski et ***[59,62],in multiple space *** the absence of algebraic source terms,the model is endowed with a curl constraint on the relative velocity *** this paper,the hyperbolicity of the system is studied for the first time in the multidimensional case,showing that the original model is only weakly hyperbolic in multiple space *** restore the strong hyperbolicity,two different methodologies are used:(i)the explicit symmetrization of the system,which can be achieved by adding terms that contain linear combinations of the curl involution,similar to the Godunov-Powell terms in the MHD equations;(ii)the use of the hyperbolic generalized Lagrangian multiplier(GLM)curl-cleaning approach *** PDE system is solved using a high-order ADER-DG method with a posteriori subcell finite-volume limiter to deal with shock waves and the steep gradients in the volume fraction commonly appearing in the solutions of this type of *** illustrate the performance of the method,several different test cases and benchmark problems have been run,showing the high order of the scheme and the good agreement when compared to reference solutions computed with other well-known methods.

关键词： Compressible two-fluid flows Symmetric hyperbolic and thermodynamically compatible(SHTC)systems Hyperbolic systems with curl involutions High-order ADER discontinuous Galerkin(DG)schemes with subcell finite-volume limiter Conservative form of hyperbolic models

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Robust Car-Following Control of Connected and Autonomous Vehicles: A Stochastic Model Predictive Control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive Control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

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computers, Materials & Continua 2024年第2期78卷 1631-1648页

作者： Lanyao Zhang Shichao Kan Yigang Cen Xiaoling Chen Linna Zhang Yansen Huang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Engineering Central South UniversityChangsha410083China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing100044China College of Civil Engineering Guizhou UniversityGuiyang550025China Guizhou Lianjian Civil Engineering Quality Inspection Monitoring Center Co. Ltd.Guiyang550025China

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be ***,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside ***,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature ***,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly *** two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection *** experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior *** the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 ***,it achieves 100%optimal detection performance in five *** the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

关键词： Anomaly detection normalizing flow source domain feature space target domain feature space bidirectional mapping residual network

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

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Tsinghua science and Technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

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Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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Using FliPSi to Generate Data for Machine Learning Algorithms 28

Using FliPSi to Generate Data for Machine Learning Algorithm...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Liebert, Artur Wittke, Christian Ehrhardt, Jonas Jaufmann, Richard Widulle, Niklas Eilermann, Sebastian Krantz, Maria Niggemann, Oliver Helmut-Schmidt University Computer Science in Mechanical Engineering Hamburg Germany

ISBN: (纸本)9798350339918

Cyber-Physical Production Systems (CPPS) are becoming increasingly important in modern manufacturing, which leads to a growing need for automated anomaly detection, maintenance decision-making, and fault diagnosis. Artificial Intelligence (AI) and Machine Learning (ML) algorithms are often used to perform these tasks. However, there is a shortage of real data sets with fault modes, making it difficult to train ML algorithms. To overcome this problem, we propose the use of the Flexible Production Simulation (FliPSi) to generate simulated data for the development, training and evaluation of ML algorithms. FliPSi is a simulation tool developed in Unity, a game engine, which is used to simulate modular CPPS. It allows the construction of different CPPS, data collection, and generating data which could realistically originate from CPPS. The focus of FliPSi is to generate data for the development, training and testing of anomaly detection and diagnosis algorithms. In this paper, we show that data generated with FliPSi can be used to train ML algorithms, specifically recurrent neural networks (RNNs) and variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), which are specifically designed to process sequential data and that the algorithms trained with these data sets can be employed to detect simulated anomalies. With this, we show that FliPSi can be employed to overcome a blocking issue in using ML algorithms for anomaly detection. © 2023 IEEE.

关键词： Anomaly detection

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Rule-based Explaining Module: Enhancing the Interpretability of Recurrent Relational Network in Sudoku Solving

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Machine Graphics and Vision 2023年第3-4期32卷 125-145页

作者： Cheewaprakobkit, Pimpa Shih, Timothy K. Lau, Timothy Lin, Yu-Cheng Lin, Chih-Yang Department of Information Technology Asia-Pacific International University Saraburi Thailand Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan

computer vision has gained significant attention in the field of information technology due to its widespread application that addresses real-world challenges, surpassing human intelligence in tasks such as image recognition, classification, natural language processing, and even game playing. Sudoku, a challenging puzzle that has captivated many people, exhibits a complexity that has attracted researchers to leverage deep learning techniques for its solution. However, the reliance on black-box neural networks has raised concerns about transparency and explainability. In response to this challenge, we present the Rule-based Explaining Module (REM), which is designed to provide explanations of the decision-making processes using Recurrent Relational Networks (RRN). Our proposed methodology is to bridge the gap between complex RRN models and human understanding by unveiling the specific rules applied by the model at each stage of the Sudoku puzzle solving process. Evaluating REM on the Minimum Sudoku dataset, we achieved an accuracy of over 98.00%. © 2023 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Image recognition

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Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

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Environmental science and Pollution Research 2024年第22期31卷 32449-32463页

作者： Rajak, Upendra Chaurasiya, Prem Kumar Verma, Tikendra Nath Dasore, Abhishek Ağbulut, Ümit Meshram, Kundan Saleel, CAhamed Saboor, Shaik Cuce, Erdem Mian, Zhibao Department of Mechanical Engineering RGM College of Engineering and Technology Nandyal518501 India Department of Mechanical Engineering National Institute of Technology Raipur492010 India Department of Mechanical Engineering Maulana Azad National Institute of Technology Vellore462003 India Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia Selangor UPM Serdang43400 Malaysia Department of Mechanical Engineering Faculty of Mechanical Engineering Yildiz Technical University Istanbul34349 Turkey Department of Civil Engineering Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Mechanical Engineering College of Engineering King Khalid University Abha61421 Saudi Arabia School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore632014 India Department of Mechanical Engineering Faculty of Engineering and Architecture Recep Tayyip Erdogan University Zihni Derin Campus Rize53100 Turkey School of Engineering and the Built Environment Birmingham City University BirminghamB4 7XG United Kingdom Faculty of Science and Engineering School of Computer Science University of Hull Cottingham Road HullHU6 7RX United Kingdom Center for Research Impact & amp Outcome Chitkara University Punjab Rajpura140401 India

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Diesel engines

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Shifting From Syntactic to Semantics for Knowledge Exemplification Using Semantic Web (SW) Mining Techniques

Shifting From Syntactic to Semantics for Knowledge Exemplifi...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Zaidi, Taskeen Kumar, Ajay Pundeer, Shivali Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

Using Semantic Web technologies is critical to improving knowledge sharing and administrative efficiency and fostering semantic interoperability amongst healthcare information systems. This article offers a thorough evaluation of software technologies that takes into account both the technical and content elements. In-depth discussions of ontology, interoperability guidelines, ontology creation, types of ontologies, ontology editors, and real-world applications of ontologies in healthcare are provided in this study. The proprietor of the website wants to help visitors by giving them useful information that will help them achieve their goals. By examining user behaviour, page content, and the sequence in which URLs are viewed, web mining techniques are utilised to classify users and sites. In order to extract conceptual linkages and apply online structure mining procedures, which concentrate on fully structured forms, this study provides two web mining techniques: web use mining and web content mining. Web structure mining aids in the retrieval process and has been utilised in the development of ontologies. © 2024 IEEE.

关键词： Semantic Web

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