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Bayesian system identification and chaotic prediction from data for stochastic Mathieu-van der Pol-Duffing energy harvester 认领 引用
1
作者 Di Liu Shen Xu Jinzhong Ma 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2023年第2期89-92,共4页
In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester... In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester system.Then the proposed method is applied to estimate the coefficients of the chaotic model and the response output paths of the identified coefficients compared with the observed,which verifies the effectiveness of the proposed method.Finally,a partial response sample of the regular and chaotic responses,determined by the maximum Lyapunov exponent,is applied to detect whether chaotic motion occurs in them by a 0-1 test.This paper can provide a reference for data-based parameter iden-tification and chaotic prediction of chaotic vibration energy harvester systems. 展开更多
关键词 Vibration energy harvester Approximate Bayesian computation 0–1 test Parameter identification Chaotic prediction
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Research on the intelligent characterization of interwell section architecture based on Bayesian expert systems 认领 引用
2
作者 De-Gang Wu Sheng-He Wu +2 位作者 Zhen-Hua Xu Lei Liu Ming-Cheng Liu 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1986-2001,共16页
The characterization of interwell section architecture is critical for revealing reservoir lateral heterogeneity and connectivity.This process integrates well and seismic data with geological knowledge yet faces inher... The characterization of interwell section architecture is critical for revealing reservoir lateral heterogeneity and connectivity.This process integrates well and seismic data with geological knowledge yet faces inherent multiple solutions.Current characterization methods remain hampered by high levels of manual intervention,insufficient automation,and difficulties in evaluating the uncertainty of interwell section architecture.To address these challenges,this study presents an intelligent method for the automated characterization of reservoir architecture along section directions based on a Bayesian expert system.The approach quantifies domain knowledge via prior normal distributions.By utilizing well and seismic data,Bayesian probabilistic reasoning infers the guiding influence of each individual piece of domain knowledge on predicting the interwell distribution of architectural elements.A weighted ensemble decision framework then integrates these inferences to determine the interwell distributions of architectural elements and associated uncertainties.Case studies demonstrate that the method effectively evaluates uncertainty,generates geologically consistent section characterizations,achieves 81%consistency in blind well sand body predictions,and excels in delineating the lateral boundaries and contact relationships of architectural elements. 展开更多
关键词 Bayesian inference Interwell architectural characterization Expert system Uncertainty evaluation
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Personalized Recommendation System Using Deep Learning with Bayesian Personalized Ranking 认领 引用
3
作者 Sophort Siet Sony Peng +1 位作者 Ilkhomjon Sadriddinov Kyuwon Park 《Computers, Materials & Continua》 SCIE EI 2026年第3期1423-1443,共21页
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively... Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately. 展开更多
关键词 Recommendation systems traditional collaborative filtering Bayesian personalized ranking
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Active learning for global optimization of highly non-stationary geotechnical engineering systems using Bayesian compressive sensing 认领 引用
4
作者 Peiping Li Yin-Fu Jin +1 位作者 Lei Wang Xiangsheng Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5324-5341,共18页
Global optimization constitutes a crucial challenge in design optimization in geotechnical engineering,which aims to maximize the performance objective function of a geotechnical engineering system,thereby achieving t... Global optimization constitutes a crucial challenge in design optimization in geotechnical engineering,which aims to maximize the performance objective function of a geotechnical engineering system,thereby achieving the optimal output.For complex geotechnical engineering systems with computationally time-consuming models and highly non-stationary responses,the direct application of stochastic optimization algorithms usually requires numerous evaluations of the original model,resulting in significantcomputational expense.To tackle this challenge,this study develops an innovative and efficientglobal optimization(EGO)method using Bayesian compressive sensing(BCS)and active learning for highly non-stationary geotechnical engineering problems,referred to as BCS-based EGO.In BCS-based EGO,BCS is utilized to train a response surface from a training sample set,enabling the efficientexecution of the stochastic optimization algorithm and providing response predictions along with the associated uncertainty at each search point.The response surface results are combined with an active learning sampling criterion to adaptively identify additional optimal sampling points,updating the response surface and training sample set to enhance the accuracy of response prediction and global optimization,until the stopping criterion of active learning is satisfied.The proposed method is capable of handling highly non-stationary data because BCS is data-driven and non-parametric.Moreover,it efficientlyaddresses the challenge of underestimating the factor of safety and failure probability in limit equilibrium method-based slope stability and reliability analysis using the potential slip surface method.Investigations utilizing three highly non-stationary benchmark examples and two highly nonstationary engineering examples indicate that BCS-based EGO performs well with sparse sampling points. 展开更多
关键词 Efficientglobal optimization(EGO) Highly non-stationary data Bayesian compressive sensing(BCS) Active learning Slope reliability analysis
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Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm 认领 引用
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作者 Bo Yang Ruyi Zheng +2 位作者 Yucun Qian Boxiao Liang Jingbo Wang 《Global Energy Interconnection》 EI CSCD 2025年第2期316-337,共22页
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a... Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively. 展开更多
关键词 Photovoltaic cell Bayesian neural network Artificial ecosystem optimization Parameter identification
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Dynamic Reliability Assessment Approach for Deepwater Subsea Wellhead Systems via Hybrid Bayesian Networks 认领 引用
6
作者 LI Jia-yi CHANG Yuan-jiang +2 位作者 LIU Xiu-quan XU Liang-bin CHEN Guo-ming 《China Ocean Engineering》 SCIE EI CSCD 2025年第1期100-110,共11页
The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic ... The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life. 展开更多
关键词 deepwater subsea wellhead system reliability accumulation fatigue damage hybrid Bayesian network
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Combined Fault Tree Analysis and Bayesian Network for Reliability Assessment of Marine Internal Combustion Engine 认领 引用 被引量：1
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作者 Ivana Jovanović Çağlar Karatuğ +1 位作者 Maja Perčić Nikola Vladimir 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第1期239-258,共20页
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ... This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels. 展开更多
关键词 Fault tree analysis Bayesian network Reliability Redundancy Internal combustion engine
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Inverse Design of Composite Materials Based on Latent Space and Bayesian Optimization 认领 引用 被引量：1
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作者 Xianrui Lyu Xiaodan Ren 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1-25,共25页
Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement ... Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials. 展开更多
关键词 Variational autoencoder denoising diffusion generation model composite materials Bayesian opti-mization SHapley Additive exPlanations
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Study of meteorological‐hydrological drought propagation under reservoir regulation using a Copula‐Bayesian network in the Hanjiang River Basin 认领 引用
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作者 Yanping Qu Cheng Li +3 位作者 Yachao Zhang Siyu Zhao Tianliang Jiang Qinghua Ye 《River》 EI CSCD 2026年第1期33-54,共22页
Reservoir operations play a pivotal role in modifying drought propagation processes,particularly by influencing the transition from meteorological to hydrological drought.This study investigates the drought propagatio... Reservoir operations play a pivotal role in modifying drought propagation processes,particularly by influencing the transition from meteorological to hydrological drought.This study investigates the drought propagation characteristics in the middle reaches of the Hanjiang River Basin,China,under both natural and observed(reservoir‐influenced)conditions.The Standardized Precipitation Evapotranspiration Index and Standardized Streamflow Index were utilized to characterize meteorological and hydrological drought,respectively.The Soil and Water Assessment Tool was employed to reconstruct natural streamflow,providing a baseline for comparison.A nonlinear copula function was applied to model the dependence between meteorological and hydrological drought characteristics,and a Copula‐Bayesian network was developed to quantify propagation probabilities.Under the regulation of the Danjiangkou Reservoir,drought propagation characteristics for 1–12‐month timescales have shifted markedly:the average propagation time downstream was prolonged from 0.25–0.70 months to 0.94–2.36 months,while the propagation rate declined from 0.83–0.89 to 0.48–0.65,and the sensitivity decreased from 0.83–0.96 to 0.68–0.79.In the natural scenario,the optimal propagation model was based on the Gumbel copula,whereas the observed scenario was best fitted by the Frank copula.The likelihood of hydrological drought increased with the intensity and duration of meteorological drought.However,compared to natural conditions,reservoir regulation significantly delayed the onset and reduced the probability of hydrological drought occurrence.These findings elucidate the nonlinear dynamics of drought propagation and underscore the regulating effect of large‐scale reservoirs on downstream hydrological responses. 展开更多
关键词 Copula‐Bayesian network Danjiangkou Reservoir drought propagation hydrological drought meteorological drought
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A hierarchical Bayesian–MCMC model for predicting the geometric dimensions of corrosion defects in wet gas gathering pipelines 认领 引用
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作者 Si-Jia Chen Min Qin +2 位作者 Ke-Xi Liao Yong-Chun Mu Xiao-Dong Hao 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1487-1504,共18页
Internal corrosion is a major threat to the safety of natural gas pipelines,with defect geometry—depth,length,and width—playing a critical role in structural integrity assessments.While corrosion depth prediction ha... Internal corrosion is a major threat to the safety of natural gas pipelines,with defect geometry—depth,length,and width—playing a critical role in structural integrity assessments.While corrosion depth prediction has been widely studied,systematic probabilistic modeling of defect length and width remains limited.This study develops a hierarchical Bayesian-Markov Chain Monte Carlo(HB-MCMC)framework to jointly predict corrosion defect dimensions from in-line inspection(ILI)data.The framework integrates non-centered parameterization and adaptive sampling to improve inference efficiency and employs a hierarchical dynamic thresholding procedure for robust data preprocessing and outlier filtering.Field data from two transmission pipelines in Southwest China,comprising 1845 defect records,are analyzed.Results demonstrate that defect length and width both increase with depth,with width exhibiting stronger sensitivity.Model diagnostics confirm convergence and reliable uncertainty quantification.To further explore underlying mechanisms,OLGA multiphase flow simulations are combined with statistical predictions,providing flow-parameter profiles along the pipelines and enabling correlation analysis between local hydrodynamics and defect geometry.The proposed framework not only enhances predictive capability for defect length and width but also provides new insights into flow-corrosion interactions under real operating conditions,offering a reproducible and data-driven tool for corrosion assessment. 展开更多
关键词 Corrosion defects Defect dimensions Hierarchical Bayesian Markov Chain Monte Carlo Corrosion prediction
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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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Bayesian parametric calibration of Shanghai deep clayey soil with CPTu data under partial drainage conditions 认领 引用
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作者 Changhong Wang Tian Fang +2 位作者 Zhaoxin Wu Kun Wang Daofei Tang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期3003-3022,共20页
The constraint in the engineering applications of Shanghai,China,involving the deep 8th soil layer of clay,lies between drained and undrained circumstances,which impacts the stress–strain relationship of the clayey s... The constraint in the engineering applications of Shanghai,China,involving the deep 8th soil layer of clay,lies between drained and undrained circumstances,which impacts the stress–strain relationship of the clayey soil.Existing constitutive models cannot fully reflect the partial drainage mechanics,and the geotechnical parameters exhibit substantial randomness in a natural environment.Hence,it is difficult to describe the mechanical behaviors of deep clayey soil if only uniform geotechnical parameters are used.Based on the Modified Cam–Clay(MCC)model and asymptotic state theory,an enhanced constitutive model considering partial drainage is proposed.Moreover,by embedding the comprehensive model into the cylindrical cavity expansion theory,the mechanical interpretation of the piezocone penetration test(CPTu)data is derived.Combined with the in‑situ test data,key constitutive parameters are calibrated by the stochastic mechanics‑based Bayesian method.Firstly,the MCC model considering the asymptotic state is proposed.Secondly,the enhanced MCC model is combined with cylindrical cavity expansion theory to establish the mechanical transformation model between key geotechnical parameters and CPTu data.Finally,taking the extremely deep foundation pit of the Yunling comprehensive facility in Shanghai,China,as an example,the method of calibrating key geotechnical parameters is studied by the Markov Chain Monte Carlo(MCMC)algorithm.Using the calibrated parameters,the construction process of the ultra‑deep foundation pit excavation is simulated,and the numerical results are compared with the monitoring data.The results show that using CPTu data to determine the strain increment ratio can reasonably obtain the strength indices and other important constitutive parameters under partial drainage conditions.Bayesian calibration significantly reduces the randomness of geotechnical parameters,thereby providing reliable predictions for infrastructure design and construction. 展开更多
关键词 Partial drainage Constitutive model Piezocone penetration test Cylindrical cavity expansion theory Bayesian method MCMC sampling
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A dynamic function allocation method for single pilot operations in commercial aircraft based on Bayesian-enhanced Q-learning 认领 引用
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作者 Yue LUO Yuting DAI +3 位作者 Miao WANG Lei WANG Ruiyuan HONG Guoqing WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期411-430,共20页
Human-machine collaboration is a key feature of Single Pilot Operations(SPO).With only a single pilot in the cockpit,workload monitoring and adjustment become even more critical compared to Dual-Pilot Operations(DPO).... Human-machine collaboration is a key feature of Single Pilot Operations(SPO).With only a single pilot in the cockpit,workload monitoring and adjustment become even more critical compared to Dual-Pilot Operations(DPO).Hence,a dynamic function allocation mechanism must be established—increasing the Level of Automation(LOA)under high workload conditions and reducing it under low workload conditions to maintain situational awareness.To address the challenges of excessive subjectivity and limited knowledge transfer in the existing dynamic function allocation methods,this paper proposes a dynamic function allocation method based on Bayesianenhanced Q-Learning(BQL).First,a Bayesian Network(BN)is constructed to predict HumanMachine System(HMS)performance,determining when reallocation should be triggered.Compared to the existing trigger mechanisms,this approach enables earlier activation while maintaining non-intrusive.Then,the BN-predicted HMS performance is integrated into the reward feedback for the reinforcement learning algorithm,allowing the system to continuously refine its strategy through interaction with the environment.Finally,flight experiments conducted in a low-fidelity SPO simulator,incorporating both objective physiological monitoring and subjective assessments,validate the effectiveness of the proposed method. 展开更多
关键词 Bayesian network Dynamic function allocation Human-machine system Reinforcement learning Single Pilot Operations(SPO)
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Online accelerating precursor identification and dynamic probabilistic prediction for rock slope failures using Bayesian inference 认领 引用
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作者 Mingxi Chen Zihan Fu +2 位作者 Feng Xiong Jie Jiang Qinghui Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3779-3803,共25页
Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes ... Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference.By integrating the Bayesian online changepoint detection(BOCD)method with a typical dimensionless trend(TDT)model,the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration(OA).Subsequently,a Bayesian approach is employed to estimate the parameters of the inverse velocity(INV)method,enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages.Building on this,the influence of starting point(SP)selection,trend update(TU),and multi-data fusion on prediction reliability is evaluated,and a novel decision criterion for impending slope failure is proposed.The feasibility of the proposed methods is then validated using 73 rock slope failure cases.Results show that using INV data,the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA.In time-to-failure analysis,the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model,along with TU detection and multi-data fusion.The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds. 展开更多
关键词 Rock slopes Accelerating precursor identification Time-to-failure analysis Failure probability criterion Bayesian inference Inverse velocity(INV)method
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Bayesian multivariate track geometry degradation modeling and its use in condition-based inspection 认领 引用
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作者 Huy Truong‑Ba Sinda Rebello +2 位作者 Michael E.Cholette Venkat Reddy Pietro Borghesani 《Railway Engineering Science》 EI 2026年第2期217-241,共25页
Effective maintenance of railway infrastructure is crucial for safe and comfortable transportation.Among the various degradation modes,track geometry deformation due to repeated loading significantly impacts operation... Effective maintenance of railway infrastructure is crucial for safe and comfortable transportation.Among the various degradation modes,track geometry deformation due to repeated loading significantly impacts operational safety.Detecting and maintaining acceptable track geometry involve the use of track recording vehicles(TRVs)that inspect and record geometric parameters.This study aims to develop a novel track geometry degradation model that considers multiple indicators and their correlations,accounting for both imperfect manual and mechanized tamping.A multivariate Wiener model is formulated to capture the characteristics of track geometry degradation.To address data limitations,a hierarchical Bayesian approach with Markov Chain Monte Carlo(MCMC)simulation is employed.This research contributes to the analysis of a multivariate predictive model,which considers the correlation between the degradation rates of multiple indicators,providing insights for rail operators and new track-monitoring systems.The model’s performance is validated through a real-world case study on a commuter track in Queensland,Australia,using actual data and independent test datasets.Additionally,the study demonstrates the application of the proposed multivariate degradation model in developing a condition-based inspection policy for track geometry,potentially reducing the number of TRVs runs while maintaining abnormal detection levels and failure rates. 展开更多
关键词 Track geometry degradation Multivariate degradation model Multivariate wiener process Bayesian approach Degradation prediction
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Fast identification of γ‑emitting radionuclides based on sequential Bayesian approach 认领 引用
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作者 Xuan Zhang Jian-Wei Huang +5 位作者 Lin-Jian Wan Jia-Cheng Liu Xiao-Le Zhang De-Hong Li Fei Tuo Zhi-Jun Yang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期1-15,共15页
The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi... The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr3(Ce)detector in both low-and natural background radiation environments with typical radionuclides(137Cs,60Co,and 133Ba).The results show that this approach can identify 137Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10−3μGy/h),60Co in 8.1 s and 9.8 s(approximately 4.8×10−2μGy/h),and 133Ba in 4.05 s and 5.99 s(approximately 3.4×10−2μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments. 展开更多
关键词 Sequential Bayesian approach Fast radionuclides identification LaBr3(Ce)detector Low background radiation laboratory
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Bayesian neural network evaluation method on the neutron-induced fission product yields of232Th 认领 引用
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作者 Chun-Yuan Qiao Ya-Xuan Wang +2 位作者 Chun-Wang Ma Jun-Chen Pei Yong-Jing Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第3期132-142,共11页
Research on neutron-induced fission product yields of232Th is crucial for understanding the competition between symmetric and asymmetric fission in actinide nuclei.However,obtaining complete isotopic yield distribu... Research on neutron-induced fission product yields of232Th is crucial for understanding the competition between symmetric and asymmetric fission in actinide nuclei.However,obtaining complete isotopic yield distributions over a wide range of neutron energies remains a challenge.In this study,a Bayesian neural network model was developed to predict the independent(IND)and cumulative fission yields of232Th under neutron irradiation at various incident energies.To address the limited availability of experimental data for the analysis of IND mass distributions,we substituted mass-number-based yields with the yields of specific isotopes.Furthermore,physical phenomena or quantities,such as the odd-even effect and isospin,were introduced as constraints to enhance the physical consistency of the predictions.The impact of these constraints was evaluated using mass-chain yield distributions and their dependence on energy.Incorporating physical constraints significantly improves the prediction accuracy,yielding more reliable and physically meaningful fission yield data for nuclear physics and reactor design applications. 展开更多
关键词 Bayesian neural network 232Th Independent fission yield Cumulative fission yield Odd–even effect Isospin
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Bayesian evaluation of photofission product yields of Th isotopic chains 认领 引用
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作者 Meng‑Die Zhou Chun‑Yuan Qiao +5 位作者 Pu Jiao Hong‑Wei Wang Hui‑Ling Wei Gong‑Tao Fan Jin‑Gen Chen Chun‑Wang Ma 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第7期210-220,共11页
The fragment yields in photon-induced fission reactions of thorium(Th)isotopes are important for modern nuclear energy applications and for understanding the evolution of the nuclear structures of their isotopic chain... The fragment yields in photon-induced fission reactions of thorium(Th)isotopes are important for modern nuclear energy applications and for understanding the evolution of the nuclear structures of their isotopic chains.Bayesian neural network(BNN)models were constructed to describe the fragment yields in photonuclear fission reactions of thorium isotopes,ranging from 216Th to 232Th,especially those of 232Th,at various incident photon energies.The predicted results of the optimized BNN models were in good agreement with the measured data for these reactions.The double-layer BNN models successfully illustrated the systematic transition from asymmetric to symmetric fission in thorium isotopes,including the associated oddeven effects,energy dependence,and leftward shift in mass yield distributions.The developed BNN models provide a new tool for predicting the fragment yields in thorium photonuclear fission reactions. 展开更多
关键词 Photonuclear fission reaction Thorium isotopes 232Th Bayesian neural network Total element yield Mass chain yield Odd-even staggering phenomenon
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Global ROTI forecasting with a Bayesian model based in long‑tail distributions 认领 引用
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作者 Enric Monte‑Moreno Heng Yang Manuel Hernández‑Pajares 《Satellite Navigation》 SCIE EI CSCD 2026年第1期367-393,I0008,共27页
This study introduces a Bayesian probabilistic model for forecasting the fluctuations in the Rate of TEC Index(ROTI),which indicate the presence of ionospheric disturbances that can impact Global Navigation Satellite ... This study introduces a Bayesian probabilistic model for forecasting the fluctuations in the Rate of TEC Index(ROTI),which indicate the presence of ionospheric disturbances that can impact Global Navigation Satellite Systems(GNSS)and communication networks.The forecast method divides the Earth into a grid of 2.5◦latitude by 5◦longitude cells to predict when ROTI will exceed thresholds of 0.1,0.25 and 0.5 TECU/min,with time horizons ranging from 30 min to 6 h.The method is based on the burstiness property of long‑tailed distributions and provides as a forecast the median value of activity at each range,of both ROTI amplitude and forecast horizon.Previous proposed ROTI forecasting methods may degrade when faced with missing data points and the irregular,heavy‑tailed characteris tics of ROTI.In contrast,our model,based on the power‑law dynamics observed in the persistent and bursty nature of long‑tail distributions,allows for gaps in the measurements and provides a global forecast for regions covered by the network of GNSS stations.The performance of the model has been validated against historical GNSS data across various ionospheric conditions,demonstrating its robustness.The proposed Bayesian probabilistic model demonstrates robust forecasting capabilities,validated across diverse ionospheric conditions using historical GNSS data.It achieves strong performance metrics,with Weighted Kappa values exceeding 40%for prediction horizons up to 120 min and maintaining Mean Precision above 65%across all tested horizons from 30 min to 6 h.By forecast ing the probability of ROTI exceeding specific levels,this method helps to identify geographical regions where GNSS reliability may be compromised,thereby aiding in the mitigation of adverse space weather effects on critical naviga tion and communication systems. 展开更多
关键词 Ionosphere ROTI Forecast GNSS Long‑tail distributions Bayesian model Space weather
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