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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Jin, Xin Chen, Shiyu Nanjing University of Science and Technology School of Mechanical Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is everywhere in people's lives. It controls users' activities in social networks. Understanding the mechanism of interactions between users in social networks can benefit various applications, such as recommendations. Traditional recommendation systems based on collaborative filtering suppose that the behaviors of users are independent of each other and ignore the influence relationships among users. However, different historical behaviors and social relationships of users result in different influences among users. First, the complete user behavior sequences are fed into a recurrent neural network to capture long term patterns of user interest in items. Secondly, a self-attention network is used to obtain short lived interests of users. Then the outputs of the recurrent neural network as queries are fed into another self-attention network to obtain user representations that combine transient effect of and long term patterns of users' interests, thereby predicting the ratings of users to items. Finally, the proposed recommendation system is used to recommend products that need to be promoted to the maximum number of users. The proposed method is verified to outperform the state-of-the-art algorithms on three real-world datasets. © 2024 IEEE.

关键词： Chatbots

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Eulerian-DL Forecasting of Wind Turbulence for Wind Turbines: Enabling Smart Sustainable Energy 10

Eulerian-DL Forecasting of Wind Turbulence for Wind Turbines...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassanian, Reza Riedel, Morris University of Iceland The Faculty of Industrial Engineering Mechanical Engineering and Computer Science Reykjavik Iceland Juelich Supercomputing Centre Germany

ISBN: (纸本)9798331532895

Currently, wind power is the fastest-growing sus-tainable energy resource, and accurately predicting wind velocity for individual turbines is essential. Wind behavior is turbulent, with variable patterns, characteristics, and unpredictable extreme events. Moreover, the wind reaching each turbine is impacted by wake loss effects from surrounding turbines, which underscores the need for a precise local prediction model for each turbine to enhance accuracy in smart wind power production. This study proposes a novel approach using local measurements at fixed points, adopting the Eulerian perspective in fluid dynamics to forecast wind turbulence for the coming hour using a deep learning model. A long short-term memory (LSTM) forecasting model is tuned and applied. Laboratory data from wind tunnel tests are utilized to account for velocity fluctuations and wake loss effects, aiming to establish similarity to an affected single wind turbine. Model training and inference run on the high-performance computing (HPC) DEEP-DAM systems at the Julich Supercomputing Centre. Three train-test datasets are employed to evaluate the model. The measured metrics indicate that the mean absolute error is 0.048, the root mean square error is approximately 0.065, and the Pinball loss for the 90th percentile is 0.0042. The model demonstrates a robust predictive capability, supporting the development of advanced instruments for individ-ual turbines and facilitating smart, clean, and sustainable energy production. © 2024 IEEE.

关键词： Wakes

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Droplet Spreading Prediction of the Textured Surfaces: A Machine Learning Approach

Droplet Spreading Prediction of the Textured Surfaces: A Mac...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Meshram, Ganesh Sahadeo Chakraborty, Suman Chakrabarti, Partha Pratim IIT Kharagpur Mechanical Engineering West Bengal India IIT Kharagpur Computer Science and Engineering West Bengal India

ISBN: (纸本)9798331539948

Surface wettability plays a crucial role in a wide range of applications, including waterproof coatings, medical diagnostics, microfluidics, and the textile industry. It is typically characterized by contact angle measurements and droplet spreading behavior on surfaces. However, traditional methods for assessing surface wettability often come with limitations, such as the need for complex experimental setups and high costs. To address these challenges, machine learning (ML) offers a promising alternative for making accurate and rapid predictions of surface wettability properties. In this study, we developed and evaluated several regression models, including Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Random Forest Regression (RFR), and Artificial Neural Networks (ANN). Our analysis focused on predicting droplet spreading dynamics on a variety of surfaces, using a dataset of 2,500 samples generated through Lattice Boltzmann Method (LBM) simulations. The performance of these models was rigorously assessed using key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2). Among the models, Random Forest Regression stood out, delivering exceptional performance with an R2 score of 0.999 during training and 0.991 during testing. The model's superior performance, reflected in its low RMSE and MAE values, underscores the potential of ML to surpass traditional methods, providing a faster, more data-driven approach to predicting and analyzing droplet spreading behavior on surfaces. © 2024 IEEE.

关键词： Textile industry

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A Transformation Matrix Explains the Sequential Forecasting Data-Driven Deep Learning Model

A Transformation Matrix Explains the Sequential Forecasting ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hassanian, Reza Helgadóttir, Ásdís Riedel, Morris University of Iceland The Faculty of Industrial Engineering Mechanical Engineering and Computer Science Reykjavik Iceland Juelich Supercomputing Centre Germany

ISBN: (纸本)9798350367560

Deep learning (DL) data-driven models (DDMs) offer significant potential for predicting nonlinear events, particularly in the absence of an analytical or known mathematical foundation. This study provides the first empirical evidence of a transformation matrix derived from a data-driven DL model for the sequential prediction of physical properties, where both the input and output are single-variable time series. The matrix's performance is tested on two different datasets to evaluate its effectiveness, demonstrating substantial proficiency in prediction modeling. The mathematical approach is explained, and the results are presented. Different portions of the training sets (60% and 80%) are examined, showing significant prediction accuracy. Furthermore, the DDM DL transformation matrix is square, has eigenvalues, and is able to reveal the correlations, stability, and limits of the system in which the model is used. This approach has the potential to provide explainable insights into data-driven DL models. © 2024 IEEE.

关键词： Prediction models

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Heat Transfer Area Optimization for Heat Exchanger System

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computer Modeling in engineering & sciences 2025年第4期143卷 335-349页

作者： Yu-Cheng Liao Fu-I Chou Po-Yuan Yang Jyh-Horng Chou Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia UniversityTaichung407Taiwan

This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi ***-GSO algorithm is used to optimize the heat transfer area of the heat exchanger *** R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global *** conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout ***,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system ***,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the *** optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are ***,fixing the systemoperating temperature parameters at a constant value is *** paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger *** results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.

关键词： Heat exchanger system allowable tolerance-based group search optimization Taguchi method tolerance design

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YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

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computers, Materials & Continua 2024年第7期80卷 1143-1161页

作者： Zhuhong Chu Jianxun Zhang Chengdong Wang Changhui Yang Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Institute of Flexible Electronics(Future Technologies) Xiamen UniversityXiamen361000China College of Mechanical Engineering Chongqing University of TechnologyChongqing400054China

Rail fasteners are a crucial component of the railway transportation safety *** fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and *** inspection not only consumes time but also poses the risk of potential *** the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are *** have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial ***,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate ***,we collected and prepared the GKA dataset,sourced from real train *** testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection *** the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference ***-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

关键词： Rail fastening detection deep learning anomalous rail fastening variant YOLO feature reinforcement

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Development of Mg-Zn-Mn Surface Composites Reinforced with ZnO Through Friction Stir Processing and Investigation of Corrosion and Wear Behavior

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Journal of Bio- and Tribo-Corrosion 2025年第2期11卷 1-11页

作者： Ramesh, S. Kudva, S. Aditya Gurumurthy, B.M. Kumar, Prakash Kumar, Sanjeev Prashanth, B. H. Maruthi Adiga, Shamantha Anne, Gajanan Karthik, B.M. School of Computer Science and Engineering RV University Bengaluru India Department of Mechanical Engineering Shri Madhwa Vadiraja Institute of Technology and Management Udupi574115 India Department of Mechanical and Industrial Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal567104 India Univlabs Technologies Private Limited Haryana Gurugram122003 India Department of Mechanical Engineering Gaya College of Engineering Gaya823003 India Department of Mechanical Engineering A.G.M Rural College of Engineering & Technology Varur Karnataka Hubballi581207 India Visvesvaraya Technological University Karnataka Belagavi India

Magnesium (Mg) composites exhibit exceptional properties, making them ideal for diverse applications in the medical, aerospace, and energy industries. The extensive use of Mg-based composites has driven ongoing efforts to enhance their properties and performance. Therefore, the present work focused on the fabrication of Mg-Zn-Mn surface composites reinforced with Zinc oxide (ZnO) through friction stir processing (FSP). The effect of the addition of ZnO on microstructure, mechanical properties, corrosion, and wear behavior was investigated. The development of the microstructure is analyzed using a scanning electron microscope (SEM) and X-ray diffraction analysis. Surface topography and roughness are analyzed using atomic force microscopy (AFM) and revealed a Ra value of 68.4 nm. The mechanical properties of the friction stir processed samples are investigated using Vickers microhardness equipment. The FSP/ZnO-Mg-Zn-Mn surface nanocomposites demonstrated a microhardness of 152.7 Hv, which is 2.4 times greater than that of the H-Mg-Zn-Mn base materials. The enhancement in the corrosion resistance of the FSP/ZnO-Mg-Zn-Mn surface nanocomposites is primarily due to the combined effect of the refined grains and the uniform dispersion of ZnO particles. Furthermore, this surface composite had the best wear resistance as a result of significant grain refinement and higher hardness. © The Author(s) 2025.

关键词： Grain refinement

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UNSUPERVISED SAMPLE SELECTION FOR ACTIVE LEARNING WITH QUADRATIC PROGRAMMING

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2024年第3期6卷 339-350页

作者： Wang, Yunbin Song, Na Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350108 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China

Graph Neural Networks (GNNs), which gained popularity recently, is facing the problem of reducing the cost of acquiring large datasets. Although a portion of the work combining GNN with active learning has been moderately successful, there are still some shortcomings in this research area. Most of the studies using clustering methods can only obtain local optima, and some of them suffer from the problem of difficult convergence. Moreover, the result of clustering is often undesired if the amount of data in each class is not balanced. Some methods obtain higher performance by combining multiple metrics, but are limited by the adverse effects of under-training the initial network. For these reasons, we propose an active learning framework based on quadratic programming in this paper. This framework transforms the sample sampling process into an optimal solution problem, which can obtain the global optimal solution and avoid the problem of hard convergence. Experimental results on several datasets demonstrate that the proposed method outperforms other baselines. © 2024 Journal of Applied and Numerial Optimization.

关键词： Active learning

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A fuzzy TOPSIS method for surface integrity criteria ranking using the wire electrical discharge machining process

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Journal of engineering and Applied science 2023年第1期70卷 120页

作者： Sawyerr, Babatunde Alade Fasina, Ebun Adedeji, Wasiu Oyediran Adeniran, Mofoluso Kehinde Oke, Sunday Ayoola Rajan, John Department of Computer Science University of Lagos Lagos Nigeria Department of Mechanical Engineering Osun State University Osogbo Nigeria Department of Mechanical Engineering University of Lagos Lagos Nigeria Department of Manufacturing Engineering Vellore Institute of Technology Chennai India

The objective of this study is to propose a method called the fuzzy technique for order preference by similarity to the ideal solution (F-TOPSIS) to select parameters of the wire electrical discharge machining (WEDM) process. Consequently, the parameters principally influencing the outputs of the WEDM process were identified and examined using the F-TOPSIS framework where the inputs of three decision makers, representing their opinions are incorporated into the analysis. The idea of parametric selection in a WEDM process is multicriteria-based when the production of nitinol-60 smart memory alloy (SMA) is critically considered. Current approaches of TOPSIS and analytic hierarchy process (AHP) evaluations to select the WEDM parameters fail because of the constraints of linguistic evaluations, but the use of F-TOPSIS crosses over the restriction to choose the best parameters in a WEDM process for nitinol-60 SMA. In this work, the experimental results obtained from published research were utilised to validate the proposed method. The validation of the suitability of F-TOPSIS, aided by the published work on the WEDM process, analysed the surface integrity of nitinol-60 SMA. From the five outputs, the closeness coefficients of the best and worst are found to be 0.7567 and 0.2838, respectively. This research aids the process engineer in optimising the outputs in the WEDM process, to select the best output. Hence, the research showcased how the WEDM process could select outputs efficiently, thus aiding process engineers to maintain the process to optimise parametric resource allocations and guarantee utmost productivity. © 2023, Faculty of engineering, Cairo University.

关键词： Analytic hierarchy process

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