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Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble learning

FATIGUE & FRACTURE OF engineering MATERIALS & STRUCTURES 2025年第4期48卷 1847-1865页

作者： Huang, Qinghui Hu, Dianyin Wang, Rongqiao Sergeichev, Ivan Sun, Jingyu Qian, Guian Chinese Acad Sci Inst Mech State Key Lab Nonlinear Mech LNM Beijing Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing Peoples R China Beihang Univ Res Inst Aeroengine Beijing Peoples R China Skolkovo Inst Sci & Technol Ctr Mat Technol Moscow Russia

In situ fatigue crack propagation experiment was conducted on laser cladding with coaxial powder feeding (LCPF) K477 under various stress ratios and temperatures. Multiple crack initiation sites were observed by using in situ scanning electron microscopy (SEM). The fatigue short crack growth rate was measured, and the impacts of temperature and stress ratio on this growth rate were analyzed. Based on these experiments, the experimental data were expanded, and three ensemble learning algorithms, that is, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were employed to establish a fatigue short crack growth rate model controlled by multiple parameters. It is indicated that the RF model performs the best, achieving a coefficient of determination (R2) of up to 0.88. The fatigue life predicted by the machine learning (ML) method agrees well with the experimental one.

关键词： ensemble learning fatigue life interpolation multiple crack initiation short crack growth rate

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Multi-modal machine learning in Gastrointestinal Endoscopy: A Review 24

Multi-modal Machine Learning in Gastrointestinal Endoscopy: ...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Chan, In Neng Wong, Pak Kin Yan, Tao Hu, Yanyan Chan, Chon In Department of Electromechanical Engineering University of Macau China Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Xiangyang China Department of Gastroenterology Kiang Wu Hospital China School of Mechanical Engineering Hubei University of Arts and Science. China

ISBN: (纸本)9798400718274

The gastrointestinal (GI) tract plays a crucial role in the human body but is frequently affected by diseases that pose a significant global health issue. GI endoscopy is the primary diagnostic tool for early disease detection. However, its efficacy is limited by human factors such as fatigue and diagnostic variability. The integration of artificial intelligence (AI) in GI endoscopy, particularly through machine learning (ML) and deep learning (DL), offers a promising solution to enhance diagnostic accuracy and efficiency. Many studies have primarily focused on the sole use of endoscopic images, which limits the potential benefits of more comprehensive analysis with unimodal data. With advancements in DL algorithms that can handle complex data modalities, multi-modal machine learning (MMML) has emerged as an innovative approach that leverages multiple data modalities to enhance model performance by combining diverse information sources. Despite its potential, there is no specific review on the application of MMML in GI endoscopy. This paper reviews the applications of MMML in GI endoscopy and highlights its benefits in addressing the complexities of GI endoscopy tasks. Besides, it also explores the potential clinical applications and analyzes the challenges and provides recommendations for further advancements in this emerging field. Copyright © 2024 held by the owner/author(s).

关键词： Adversarial machine learning

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Understanding the local structure and thermophysical behavior of Mg-La liquid alloys via machine learning potential

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international Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 439-449页

作者： Jia Zhao Taixi Feng Guimin Lu National Engineering Research Center for Integrated Utilization of Salt Lake Resource East China University of Science and TechnologyShanghai 200237China Joint International Laboratory for Potassium and Lithium Strategic Resources East China University of Science and TechnologyShanghai 200237China

The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.

关键词： magnesium-lanthanum liquid alloys local structure macroscopic properties thermodynamic behavior deep potential mo-lecular dynamic simulation

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PLSRP: prompt learning for send-receive path prediction

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international JOURNAL OF machine learning AND CYBERNETICS 2025年第4期16卷 2219-2230页

作者： Wei, Qi Lu, Yi-Hong Du, Dan Cai, Huan-Tao Lai, Pei-Yuan Wang, Chang-Dong Guangdong Univ Finance & Econ Sch Business Adm Guangzhou 510320 Guangdong Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China South China Technol Commercializat Ctr Guangzhou 511458 Guangdong Peoples R China

With the rapid growth of the courier industry driven by societal development, efficiently and accurately predicting the send-receive path has become a critical issue. Existing methods suffer from several limitations, including discrepancies between pretraining and downstream data, and inconsistencies between training and testing targets. To address these challenges, this paper introduces a pioneering approach that applies prompt learning to send-receive path prediction. The proposed method employs a "pretraining-prompt-finetuning" paradigm, where a model is pretrained on a large-scale dataset and then finetuned using prompt vectors to adapt to downstream tasks. This novel strategy effectively bridges the gap between pretraining and finetuning data, ensuring better model generalization with minimal additional cost. Furthermore, we incorporate an actor-critic reinforcement learning framework, where the actor network generates paths and the critic network evaluates them. This framework optimizes the model based on rewards calculated from non-differentiable test criteria, effectively addressing the inconsistency between training and testing objectives. This approach is better suited to adapt to various delivery scenarios, enhancing prediction accuracy and efficiency. Experiments conducted on two real-world datasets demonstrate the superiority of the proposed method.

关键词： Send-receive path prediction Prompt learning Pretraining Reinforcement learning Actor-critic framework

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machine learning-aided hysteretic response prediction of double skin composite wall under earthquake loads

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JOURNAL OF BUILDING engineering 2025年 101卷

作者： Wang, Shiye Wang, Wei Wu, Yongtao Xie, Zhiyang Gao, Yuqing Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China

Accurately and rapidly simulating the hysteretic behavior of double skin composite wall (DSCW) under earthquake loads enhance the efficiency of seismic performance assessments for high-rise steel-concrete composite structures. The application of artificial intelligence in civil engineering enables a shift from purely physics-based models to methods that integrate physical mechanisms with data-driven approaches. To this end, a machine learning-aided model for simulating the hysteretic behavior of DSCW under earthquake loads is proposed. Firstly, a fiber-based uniaxial material model incorporating softening was proposed to replicate the primary damaging mechanisms of DSCW, validated through comparison with experimental data. A comprehensive dataset covering a broad range of engineering metrics of DSCW was generated, incorporating various loading protocols. The modified Bouc-Wen model was utilized to calibrate hysteretic curves, solving an inverse problem via optimization techniques to determine consistent hysteretic parameters (CHPs) of DSCW. The analysis results confirm the highest accuracy of the artificial neural network (ANN) model. The SHapley Additive exPlanations (SHAP) analysis provided interpretability, highlighting the significant influence of geometric parameters and confirming the interdependence among Bouc-Wen model parameters. The performance of the ANN model was evaluated against a Transformer-based end-to-end deep learning model. The results indicated that the ANN model exhibited superior performance, particularly under asymmetric loading conditions, due to its integration of physical mechanisms into the force-displacement constitutive relationship.

关键词： Double skin composite wall machine learning Hysteretic response Consistent hysteretic parameter Earthquake loads

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A Benchmark for Missing data Imputation Techniques: Development Perspectives and Performance Comparative 7th

A Benchmark for Missing Data Imputation Techniques: Developm...

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7th international conference on Optimization and learning

作者： Cabrera-Sanchez, Juan-Francisco Cruz-Corona, Carlos Escolano, Andres Yanez Silva-Ramirez, Esther-Lydia Univ Cadiz Dept Comp Sci & Engn Puerto Real Spain Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9783031779404;9783031779411

Knowledge extraction from information stored in databases is always subject to the presence of missing values. Missing data is an unavoidable problem that affects many disciplines of researchers and data scientists. Inasmuch as machine learning algorithms cannot work with incomplete data in the data sets, data imputation is an essential task to obtain quality data. This research approach provides an overview of the data missingness mechanism and the process of generating synthetic missing data, the imputation of all types of variables, and the performance assessment of several imputation methods. Traditional algorithms, machine learning methods and various Autoencoder-based deep learning architectures have been studied. An exhaustive analysis and comparison of 21 heterogeneous data sets in various areas has been proposed. They have been exposed to a perturbation procedure with different missingness mechanisms and various missingness rates, covering the different possibilities that can occur in real life. The experimental results show that deep learning models outperform the other methods studied. Furthermore, the performance of data imputation methods does not depend on the missingness mechanism or the synthetic missingness generation method used nor on the percentage of missing values.

关键词： Missing data machine learning Deep learning Autoencoders

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international conference on machine learning and data engineering, icmlde 2023

International Conference on Machine Learning and Data Engine...

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2nd international conference on machine learning and data engineering, icmlde 2023

The proceedings contain 324 papers. The topics discussed include: sentiment analysis of self-driving car dataset: a comparative study of deep learning approaches;unleashing the potential of boosting techniques to optimize station-pairs passenger flow forecasting;a cross-platform movie filtering and recommendation system using big data analytics;a comparative analysis of advanced machine learning algorithms to diagnose Parkinson’s disease;exploring twitter sentiments for predicting match outcomes in the game of cricket;economic order quantity models with exponential demand rate and single level trade credit;identification of brain diseases using image classification: a deep learning approach;and a short review for handwritten math expression recognition techniques.

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Federated learning with Discriminative Naive Bayes Classifier 25th

Federated Learning with Discriminative Naive Bayes Classifie...

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25th international conference on Intelligent data engineering and Automated learning

作者： Torrijos, Pablo Alfaro, Juan C. Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informaticos Albacete 02071 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. This paper proposes a new federated approach for Naive Bayes (NB) classification, assuming discrete variables. Our approach federates a discriminative variant of NB, sharing meaningless parameters instead of conditional probability tables. Therefore, this process is more reliable against possible attacks. We conduct extensive experiments on 12 datasets to validate the efficacy of our approach, comparing federated and non-federated settings. Additionally, we benchmark our method against the generative variant of NB, which serves as a baseline for comparison. Our experimental results demonstrate the effectiveness of our method in achieving accurate classification.

关键词： Federated learning Bayesian network classifiers Naive Bayes Discriminative learning

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Comparative Study of machine learning Models for the Detection of Abusive Messages: Case of Wolof-French Codes Mixing data 7th

Comparative Study of Machine Learning Models for the Detecti...

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7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Ndao, Ibrahima Dramé, Khadim Sambe, Gorgoumack Diallo, Gayo Laboratoire d’Informatique et d’Ingénierie pour l’Innovation Université Assane Seck de Ziguinchor Ziguinchor Diabir Senegal AHead Bordeaux Population Health - INSERM 1219 and LABRI University Bordeaux France

ISBN: (纸本)9783031864926

This paper presents a comparative study of machine learning models for detecting abusive messages, focusing on code-mixed data in Wolof and French languages. With the increasing use of digital platforms, there has been a surge in derogatory comments, necessitating effective detection strategies. The study introduces a meticulously annotated dataset of 2022 Twitter tweets, manually classified as abusive or not. Extensive experiments are conducted with various machine learning algorithms, including deep learning, with a focus on comparing their performance on the test dataset. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Deep learning

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A comparison between geomembrane-sand tests and machine learning predictions

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GEOSYNTHETICS international 2025年第2期32卷 180-193页

作者： Tanga, A. T. Araujo, G. L. Silva Evangelista, F. Univ Brasilia Dept Civil & Environm Engn FT Brasilia DF Brazil

The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary depending on the type and properties of both the geosynthetic and the soil. This paper introduces a machine learning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with fourteen influencing parameters recorded for each interface, influencing the shear strength outcome. In the analysis, Pearson's correlation coefficient is employed to measure the linear interdependence between each pair of input-input and input-output variables. Following the linear regression analysis, an optimized random forest is utilized to project the interface friction angle. The random forest algorithm divides the selected data into training and testing sets, and only 3% of the training set and 6% of the testing set exceed +/- 5 degrees from the actual records. The coefficient of determination (R-2) indicates strong agreement between the predicted and laboratory study friction angles, with R-2 = 0.93 for the training set and R-2 = 0.92 for the testing set. Consequently, the random forest algorithm demonstrates effectiveness in predicting interface friction angles.

关键词： Geosynthetics geomembrane interface friction angle machine learning random forest

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