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Enhanced accident diagnosis for molten salt reactors using an adaptive residual CNN with Bayesian optimization 认领 引用
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作者 Chao‑Qun Wang Kai Wang +6 位作者 Qun Yang Rong‑Jian Liang Xin Yue Liang‑Jie Sun Wen‑Tao Jiang Zhao‑Zhong He Na‑Xiu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第8期280-296,共17页
Safety is of paramount importance in nuclear power plants.Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems.The convergence of Industry 4.0 technologies and deep ... Safety is of paramount importance in nuclear power plants.Accurate and reliable accident diagnosis is essential for ensuring operational safety in reactor systems.The convergence of Industry 4.0 technologies and deep learning methods has emerged as a promising approach for improving the operational safety of nuclear energy systems,particularly in fault detection and diagnosis(FDD)applications.This study proposes a novel adaptive accident diagnosis framework tailored for molten salt reactors(MSRs)based on an enhanced residual convolutional neural network(AM-RCNN).The AM-RCNN incorporates an anti-noise module implemented using the soft thresholding method,together with an attention mechanism,to improve robustness.Datasets representing eight distinct operational scenarios were generated using the RELAP5-TMSR simulation tool.An appropriate subset of input features for MSR accident diagnosis was selected using Pearson correlation analysis and random forest importance ranking.The models were subsequently trained,validated,optimized,and tested.Comparative analyses with conventional RCNN and CNN architectures demonstrate the diagnostic advantages of the proposed approach.In addition,the integration of Bayesian optimization further enhances the performance of the AM-RCNN.As a contribution to intelligent monitoring research for MSRs,the proposed method provides reliable decision support for nuclear system operation,particularly in autonomous scenarios. 展开更多
关键词 Accident diagnosis Feature selection Deep residual CNN Bayesian optimization Molten salt reactors
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Tuning nonlinear model predictive control via Bayesian optimization:a comparative performance analysis 认领 引用
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作者 Maria Alice de F.Marques Juarez dos Santos Azevedo +1 位作者 Julio Elias Normey-Rico Marcus V.Americano da Costa 《Control Theory and Technology》 EI CSCD 2026年第3期374-387,共14页
This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integr... This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integrates BO directly into the NMPC simulation loop and targets an economic cost function.This approach identifies weights that optimize an economic cost function,ensuring that controller performance is aligned with the operational economics of the process.A case study involving an interconnected tank system with nonlinear level and temperature dynamics illustrates the method.Two operating conditions are tested:an undisturbed scenario and a disturbed one,incorporating sensor noise and model–plant mismatch.In both scenarios,the BO-tuned NMPC outperforms Traditional and Satisficing-based weight strategies,yielding smoother control actions.In the disturbed case,cost reductions of up to 4.5%are achieved.The results confirm that offline BO-based tuning offers a viable and robust alternative to manual or online adaptive strategies,especially in systems where online retraining is impractical.Sensitivity tests further highlight the importance of informed search interval selection to ensure convergence and performance. 展开更多
关键词 Nonlinear predictive control Bayesian optimization Tuning parameters Cost function
Bayesian optimization-enabled efficient embedded ink writing of human tissue analogs 认领 引用
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作者 Shanti Quinto Haoran Cui +8 位作者 Lily Raymond Eric Shen Hopson Tan Liam Bond Yue Yuan Bin Duan Yan Wang Guangrui Chai Yifei Jin 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第4期782-795,I0004,共14页
Embedded ink writing has been extensively applied in recent years for various biomedical applications.Despite its outstanding ability to create complex structures,the challenge of optimizing multiple factors has hampe... Embedded ink writing has been extensively applied in recent years for various biomedical applications.Despite its outstanding ability to create complex structures,the challenge of optimizing multiple factors has hampered further utilization of this three-dimensional bioprinting strategy.In this work,we experimentally summarized the coupling effects of ink viscosity,support bath rheological properties,and key printing parameters on filament formation.Based on the gathered data,Bayesian optimization is used to establish a filament prediction platform,which can accurately estimate the rheology of support baths for printing alginate-based ink under the given conditions.Additionally,the platform is used to predict the optimal paramet ers for printing with chitosan ink.Two representative eye-relevant tissues are successfully fabricated using the predictions of the platform.The insights from this study lay the foundation for embedded ink writing strategies that can guide support bath design and identify optimal printing parameters,aiding efficient reconstruction of human tissues and organs in the future. 展开更多
关键词 Bayesian optimization Embedded ink writing Support bath design Three-dimensional(3 D)printing parameter prediction Biomedical applications
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Bearing Fault Diagnosis with Hybrid CNN-RNN:A Unified-Loop Hyperparameter Optimization Framework via Surrogate-Based Bayesian Optimization 认领 引用
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作者 Jaewan Lee Seonghwan Park Junghwan Kook 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期252-273,共22页
In bearing fault diagnosis for Prognostics and Health Management(PHM),the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing,model configuration,and decision fusio... In bearing fault diagnosis for Prognostics and Health Management(PHM),the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing,model configuration,and decision fusion.However,these components are often optimized independently,resulting in fragmented workflows that limit global optimality,reproducibility,and computational efficiency of the model.This study presents a computationally unified three-stage sequential optimization framework that systematically coordinates the preprocessing selection,model hyperparameter optimization,and decision-level fusion within a consistent surrogate-based optimization architecture.In the first stage,candidate preprocessing schemes reflecting physical faultmechanisms—outer race,inner race,rolling element,and cage faults—are evaluated using a training-free Class Separability Score(CSS),which enables efficient screening without introducing model bias.In the second stage,the hyperparameters of two complementary architectures—an EfficientNetV2-based Convolutional Neural Network(CNN)operating in the time–frequency domain and a Gated Recurrent Unit(GRU)-based Recurrent Neural Network(RNN)operating in the time domain—are optimized independently by maximizing the cross-validated performance.In the third stage,a stacking-based late fusion model is constructed using out-of-fold posterior probabilities to learn the optimal decision rules that exploit cross-model complementarity.All stages are governed by a unified optimization container integrating LatinHypercube Sampling(LHS),Kriging surrogate modeling with expected improvement,and BayesianOptimization withHyperband(BOHB),enabling surrogate-assisted exploration under multi-fidelity computational budgets.Numerical experiments on benchmark datasets,including CaseWestern ReserveUniversity(CWRU)andMAFAULDA,demonstrate the stable convergence behavior of the sequential optimization process and consistent performance gains over single-model baselines.Reproducibility is ensured through fixed data partitions,randomseeds,and normalization boundaries across all stages.The proposed framework provides a structured and reproducible computational modeling approach for bearing fault diagnosis,highlighting how coordinated optimization across multiple stages can improve the robustness and efficiency of complex PHM workflows. 展开更多
关键词 Bearing fault diagnosis hybrid CNN-RNN Bayesian optimization surrogate modeling class separability score
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Machine learning-encoded multiscale modelling and Bayesian optimization framework to design programmable metamaterials 认领 引用 被引量：2
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作者 Yizhe Liu Xiaoyan Li +1 位作者 Yuli Chen Bin Ding 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第1期226-245,共20页
Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structur... Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials. 展开更多
关键词 Artificial neural network Multiscale computation Bayesian optimization Inverse design Programmable metamaterials
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Low orbit regional enhanced navigation constellation for BDS3 design based on Bayesian optimization algorithm 认领 引用 被引量：2
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作者 Chunhua Jiang Zhenyu Luo +1 位作者 Meiqian Guan Huizhong Zhu 《Geodesy and Geodynamics》 EI CSCD 2025年第5期558-568,共11页
The application of Low Earth Orbit(LEO)satellite navigation can enhance geometric structure,increase observations and contribute to navigation and positioning.To improve the performance of the navigation constellation... The application of Low Earth Orbit(LEO)satellite navigation can enhance geometric structure,increase observations and contribute to navigation and positioning.To improve the performance of the navigation constellation in China,this study proposes an optimized method of LEO-enhanced navigation constellation for BDS based on Bayesian optimization algorithm.In this paper,four different optimal LEO constellation configurations are designed,and their enhancements to BDS3 navigation performance are analyzed,including Geometric Dilution of Precision(GDOP),the numbers of visible satellites,and the rapid convergence of precision point positioning(PPP).Additionally,the enhancement advantages in China compared to other regions are further discussed.The results demonstrate that regional enhanced constellations with 70,72,80,and 81 satellites at an altitude of 1000 km can significantly improve the navigation performance of the navigation constellation.Globally,the addition of optimized LEO constellations has reduced the hybrid constellation GDOP by 19.0%,18.3%,19.9%,and 20.3%.Similar results can be obtained using the genetic algorithm(GA),but the computational efficiency of Bayesian optimization algorithm is 53.9%higher than that of the genetic algorithm.The number of visible satellites of enhanced constellations in China has increased by more than four on average,which is better than that in other regions.In the PPP experiment,the convergence time of the stations in China and other regions is shortened by 83.0%and 76.2%,respectively,and the navigation performance of hybrid constellations in China is better. 展开更多
关键词 LEO constellation design Orbit optimization Bayesian optimization Precision point positioning(PPP)
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Machine learning for soil parameter inversion enhanced with Bayesian optimization 认领 引用 被引量：1
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作者 Anfeng HU Chi WANG +3 位作者 Senlin XIE Zhirong XIAO Tang LI Ang XU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第11期1034-1051,共18页
Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal ... Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process. 展开更多
关键词 ABAQUS software Bayesian optimization Machine learning(ML)algorithms Parameter inversion Settlement prediction
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Bayesian optimization of operational and geometric parameters of microchannels for targeted droplet generation 认领 引用
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作者 Zifeng Li Xiaoping Guan +3 位作者 Jingchang Zhang Qiang Guo Qiushi Xu Ning Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第8期244-253,共10页
Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian O... Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices. 展开更多
关键词 Bayesian optimization VOF Microchannels CFD Rib structure Optimal design
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Bayesian optimization and explainable machine learning for High-dimensional multi-objective optimization of biodegradable magnesium alloys 认领 引用
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作者 Peng Peng Yi Peng +6 位作者 Fuguo Liu Shuai Long Cheng Zhang Aitao Tang Jia She Jianyue Zhang Fusheng Pan 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第35期132-145,共14页
Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.Thi... Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.This study presents a Bayesian optimization(BO)-based multi-objective framework inte-grated with explainable machine learning(ML)to efficiently explore and optimize the high-dimensional design space of biodegradable Mg alloys.Using ultimate tensile strength(UTS),elongation(EL)and cor-rosion potential(Ecorr)as objective properties,the framework balances these conflicting objectives and identifies optimal solutions.A novel biodegradable Mg alloy(Mg-4.6Zn-0.3Y-0.2Mn-0.1Nd-0.1Gd,wt.%)was successfully designed,demonstrating a UTS of 320 MPa,EL of 22%and Ecorr of−1.60 V(tested in 37℃ simulated body fluid).Compared to JDBM,the UTS has increased by 13 MPa,the EL has improved by 6.1%,and the Ecorr has risen by 0.02 V.The experimental results presented close agreement with predicted values,validating the proposed framework.The Shapley Additive Explanation method was em-ployed to interpret the ML models,revealing extrusion temperature and Zn content as key parameters driving the optimization design.The strategy provided in this study is universal and offers a potential approach for addressing high-dimensional multi-objective optimization challenges in material develop-ment. 展开更多
关键词 Biodegradable magnesium Alloy design Machine learning Multi-objective Bayesian optimization
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Simulation of laser plasma wakefield acceleration with external injection based on Bayesian optimization 认领 引用
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作者 Jianhua ZHONG Jiabao GUAN +3 位作者 Lanxin LIU Guoxing XIA Jike WANG Yuancun NIE 《Plasma Science and Technology》 SCIE EI CSCD 2025年第4期22-29,共8页
In laser wakefield acceleration,injecting an external electron beam at a certain energy is a promising approach for achieving a high-quality electron beam with low energy spread and low emittance.In this paper,the pro... In laser wakefield acceleration,injecting an external electron beam at a certain energy is a promising approach for achieving a high-quality electron beam with low energy spread and low emittance.In this paper,the process of laser wakefield acceleration with an external injection at 10 pC has been studied in simulations.A Bayesian optimization method is used to optimize the key laser and plasma parameters so that the electron beam is accelerated to the expected energy with a small emittance and energy spread growth.The effect of the rising edge of the plasma on the transverse properties of the electron beam is simulated and optimized in order to ensure that the external electron beam is injected into the plasma without significant emittance growth.Finally,a high-quality electron beam with an energy of 1.5 GeV,a normalized transverse emittance of 0.5 mm·mrad and a relative energy spread of 0.5%at 10 pC is obtained. 展开更多
关键词 laser wakefield acceleration Bayesian optimization external injection high quality electron beam(Some figures may appear in colour only in the online journal)
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Forecasting carbon price using a hybrid framework based on Bayesian optimization algorithm 认领 引用 被引量：1
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作者 Hao-Zhen Li Tian-Ming Shao +2 位作者 Xin Gao Feng Gao Arash Farnoosh 《Petroleum Science》 SCIE EI CAS CSCD 2025年第12期5314-5328,共15页
With the European Union(EU)introducing the Carbon Border Adjustment Mechanism(CBAM),accurately forecasting EU carbon price is crucial for exporters to estimate export costs,plan low-carbon strategies,and mitigate trad... With the European Union(EU)introducing the Carbon Border Adjustment Mechanism(CBAM),accurately forecasting EU carbon price is crucial for exporters to estimate export costs,plan low-carbon strategies,and mitigate trade risks.In the petroleum sector,carbon pricing directly influences upstream investment returns and carbon intensity targets,thereby closely linking emissions markets with fossil energy strategies.Existing models often fail to fully capture the nonlinear,non-stationary nature of carbon prices and their dependence on external factors.This study proposes a novel hybrid framework that combines improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)with gated recurrent unit-convolutional neural network-long short-term memory network-Bayesian optimization(GRU-CNN-LSTM-BO).Empirical results based on the EU emissions trading system(ETS)market demonstrate that the proposed model significantly improves forecasting accuracy.Among all experiments,the proposed GRU-CNN-LSTM-BO framework achieves the best performance,yielding the lowest MAE(1.3872),RMSE(1.7038),MAPE(0.0166),and MSPE(0.0004),as well as the highest R2(0.9400).Compared to all benchmark models,the GRU-CNN-LSTM-BO model achieves reductions in MAE and RMSE ranging from 5.38%to 63.65%and 8.97%to 64.41%,respectively.To further validate the generalization ability and predictive performance of the proposed model,it is also applied to China's ETS.The results show that the GRU-CNN-LSTM-BO model also performs very well in China's ETS. 展开更多
关键词 Carbon price forecasting ICEEMDAN GRU CNN LSTM Bayesian optimization
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Automated Brain Tumor Classification from Magnetic Resonance Images Using Fine-Tuned Efficient Net-B6 with Bayesian Optimization Approach 认领 引用
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作者 Sarfaraz Abdul Sattar Natha Mohammad Siraj +2 位作者 Majid Altamimi Adamali Shah Maqsood Mahmud 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期4179-4201,共23页
A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance im... A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance imaging(MRI)is a crucial tool for diagnosing and localizing brain tumors.However,themanual interpretation of MRI images is tedious and prone to error.As artificial intelligence advances rapidly,DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors.In this study,we introduce a deep convolutional neural network(DCNN)framework for brain tumor classification that uses EfficientNet-B6 as the backbone architecture and adds additional layers.The model achieved an accuracy of 99.10%on the public Brain Tumor MRI datasets,and we performed an ablation study to determine the optimal batch size,optimizer,loss function,and learning rate to maximize the accuracy and robustness of the model,followed by K-Fold cross-validation and testing the model on an independent dataset,and tuning Hyperparameters with Bayesian Optimization to further enhance the performance.When comparing our model to other deep learning(DL)models such as VGG19,MobileNetv2,ResNet50,InceptionV3,and DenseNet201,aswell as variants of the EfficientNetmodel(B1–B7),the results showthat our proposedmodel outperforms all othermodels.Our investigational results demonstrate superiority in terms of precision,recall/sensitivity,accuracy,specificity,and F1-score.Such innovations can potentially enhance clinical decision-making and patient treatment in neurooncological settings. 展开更多
关键词 Brain tumor classification convolutional neural network magnetic resonance imaging deep learning Bayesian optimization
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Physics-Informed Gaussian Process Regression with Bayesian Optimization for Laser Welding Quality Control in Coaxial Laser Diodes 认领 引用
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作者 Ziyang Wang Lian Duan +2 位作者 Lei Kuang Haibo Zhou Ji’an Duan 《Computers, Materials & Continua》 SCIE EI 2025年第8期2587-2604,共18页
The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise co... The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging. 展开更多
关键词 Coaxial laser diodes laser welding physics-informed Gaussian process regression Bayesian optimization
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Research on an Air Pollutant Data Correction Method Based on Bayesian Optimization Support Vector Machine 认领 引用
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作者 Xingfu Ou Miao Zhang Wenfeng Chen 《Journal of Electronic Research and Application》 2025年第4期190-203,共14页
Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by... Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM2.5,PM10,CO,NO2,SO2,and O3.The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data. 展开更多
关键词 Air quality monitoring Data calibration Support vector regression Bayesian optimization Machine learning
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Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization 认领 引用 被引量：123
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作者 Wengang Zhang Chongzhi Wu +2 位作者 Haiyi Zhong Yongqin Li Lin Wang 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第1期469-477,共9页
Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random fo... Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model. 展开更多
关键词 Undrained shear strength Extreme gradient boosting Random forest Bayesian optimization k-fold CV
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Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning 认领 引用 被引量：15
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作者 Hongtao Zhang Huadong Fu +1 位作者 Yuheng Shen Jianxin Xie 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2022年第6期1197-1205,共9页
It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties.The purpose of ... It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties.The purpose of this work is to develop a new type of Cu-Ni-Co-Si alloy saving scarce and expensive Co element,in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy,while the properties are as the same level as C70350 alloy.Here we adopted a strategy combining Bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters(cold rolling deformation 90%,aging temperature 450℃,and aging time 1.25 h)of the new copper alloy with only 32 experiments(27 basic sample data acquisition experiments and 5 iteration experiments),which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy.The experimental hardness,tensile strength,and electrical conductivity of the new copper alloy are HV(285±4),(872±3)MPa,and(44.2±0.7)%IACS(international annealed copper standard),reaching the property level of the commercial lead frame C70350 alloy.This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties. 展开更多
关键词 copper alloy process design machine learning Bayesian optimization utility function
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Bottom hole pressure prediction based on hybrid neural networks and Bayesian optimization 认领 引用 被引量：5
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作者 Chengkai Zhang Rui Zhang +4 位作者 Zhaopeng Zhu Xianzhi Song Yinao Su Gensheng Li Liang Han 《Petroleum Science》 SCIE EI CAS CSCD 2023年第6期3712-3722,共11页
Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal co... Many scholars have focused on applying machine learning models in bottom hole pressure (BHP) prediction. However, the complex and uncertain conditions in deep wells make it difficult to capture spatial and temporal correlations of measurement while drilling (MWD) data with traditional intelligent models. In this work, we develop a novel hybrid neural network, which integrates the Convolution Neural Network (CNN) and the Gate Recurrent Unit (GRU) for predicting BHP fluctuations more accurately. The CNN structure is used to analyze spatial local dependency patterns and the GRU structure is used to discover depth variation trends of MWD data. To further improve the prediction accuracy, we explore two types of GRU-based structure: skip-GRU and attention-GRU, which can capture more long-term potential periodic correlation in drilling data. Then, the different model structures tuned by the Bayesian optimization (BO) algorithm are compared and analyzed. Results indicate that the hybrid models can extract spatial-temporal information of data effectively and predict more accurately than random forests, extreme gradient boosting, back propagation neural network, CNN and GRU. The CNN-attention-GRU model with BO algorithm shows great superiority in prediction accuracy and robustness due to the hybrid network structure and attention mechanism, having the lowest mean absolute percentage error of 0.025%. This study provides a reference for solving the problem of extracting spatial and temporal characteristics and guidance for managed pressure drilling in complex formations. 展开更多
关键词 Bottom hole pressure Spatial-temporal information Improved GRU Hybrid neural networks Bayesian optimization
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Bayesian optimization for active flow control 认领 引用 被引量：4
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作者 Antoine B.Blanchard Guy Y.Cornejo Maceda +4 位作者 Dewei Fan Yiqing Li Yu Zhou Bernd R.Noack Themistoklis P.Sapsis 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2021年第12期1786-1798,共13页
A key question in flow control is that of the design of optimal controllers when the control space is high-dimensional and the experimental or computational budget is limited.We address this formidable challenge using... A key question in flow control is that of the design of optimal controllers when the control space is high-dimensional and the experimental or computational budget is limited.We address this formidable challenge using a particular flavor of machine learning and present the first application of Bayesian optimization to the design of open-loop controllers for fluid flows.We consider a range of acquisition functions,including the recently introduced output-informed criteria of Blanchard and Sapsis(2021),and evaluate performance of the Bayesian algorithm in two iconic configurations for active flow control:computationally,with drag reduction in the fluidic pinball;and experimentally,with mixing enhancement in a turbulent jet.For these flows,we find that Bayesian optimization identifies optimal controllers at a fraction of the cost of other optimization strategies considered in previous studies.Bayesian optimization also provides,as a by-product of the optimization,a surrogate model for the latent cost function,which can be leveraged to paint a complete picture of the control landscape.The proposed methodology can be used to design open-loop controllers for virtually any complex flow and,therefore,has significant implications for active flow control at an industrial scale. 展开更多
关键词 Bayesian optimization Flow control Drag reduction Turbulence
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Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach 认领 引用 被引量：6
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作者 Jingcheng Mei Guojiang Zhang +1 位作者 Donglian Qi Jianliang Zhang 《Global Energy Interconnection》 EI CSCD 2021年第5期493-500,共8页
To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security con... To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on-site maintenance operations.Considering the computational complexity of the mixed integer programming(MIP)problem,a machine learning(ML)approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization(BO)in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency. 展开更多
关键词 Transmission maintenance scheduling Mixed integer programming(MIP) Machine learning Bayesian optimization(BO) Branch-and-bound
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Prediction on compression indicators of clay soils using XGBoost with Bayesian optimization 认领 引用 被引量：2
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作者 WU Hong-tao ZHANG Zi-long Daniel DIAS 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第11期3914-3929,共16页
The determination of the compressibility of clay soils is a major concern during the design and construction of geotechnical engineering projects.Directly acquiring precise values of compression indicators from consol... The determination of the compressibility of clay soils is a major concern during the design and construction of geotechnical engineering projects.Directly acquiring precise values of compression indicators from consolidation tests is cumbersome and time-consuming.Based on experimental results from a series of index tests,this study presents a hybrid method that combines the extreme gradient boosting(XGBoost)model with the Bayesian optimization strategy to show the potential for achieving higher accuracy in predicting the compressibility indicators of clay soils.The results show that the proposed XGBoost model selected by Bayesian optimization can predict compression indicators more accurately and reliably than the artificial neural network(ANN)and support vector machine(SVM)models.In addition to the lowest prediction error,the proposed XGBoost-based method enhances the interpretability by feature importance analysis,which indicates that the void ratio is the most important factor when predicting the compressibility of clay soils.This paper highlights the promising prospect of the XGBoost model with Bayesian optimization for predicting unknown property parameters of clay soils and its capability to benefit the entire life cycle of engineering projects. 展开更多
关键词 machine learning clay soils compression indicators XGBoost Bayesian optimization
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