The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
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
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
Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at a vanishing chemical potential,we constructed a Bayesian holographic QCD model and systematically analyzed the th...Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at a vanishing chemical potential,we constructed a Bayesian holographic QCD model and systematically analyzed the thermodynamic properties of heavy quarkonium in QCD matter under varying temperatures and chemical potentials.We computed the quark-antiquark interquark distance,potential energy,entropy,binding energy,and internal energy.We present detailed posterior distribution results of the thermodynamic quantities of heavy quarkonium,including maximum a posteriori(MAP)value estimates and 95%confidence levels(CL).Through numerical simulations and theoretical analysis,we find that an increase in the temperature and chemical potential reduces the quark distance,thereby facilitating the dissociation of heavy quarkonium and leading to a suppressed potential energy.The increase in temperature and chemical potential also raises the entropy and entropy force,further accelerating the dissociation of heavy quarkonium.The calculated results of binding energy indicate that a higher temperature and chemical potential enhance the tendency of heavy quarkonium to dissociate into free quarks.The internal energy also increases with rising temperature and chemical potential.These findings provide significant theoretical insights into the properties of strongly interacting matter under extreme conditions and lay a solid foundation for the interpretation and validation of future experimental data.Finally,we also present the results for the free energy,entropy,and internal energy of a single quark.展开更多
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
A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are sys...A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.展开更多
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 LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/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
Research on neutron-induced fission product yields of^(232)Th 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 of^(232)Th 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 of^(232)Th 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
为明确机场服务质量的影响因素及旅客对机场服务质量满意程度,提高机场服务质量,运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)与贝叶斯网络结合的评估模型,建立大型运输机场服务...为明确机场服务质量的影响因素及旅客对机场服务质量满意程度,提高机场服务质量,运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)与贝叶斯网络结合的评估模型,建立大型运输机场服务质量评价指标体系。运用双向推理诊断模型评价机场服务质量,对机场满意度正向推理以及影响指标的反向敏感性诊断。对旅客机场服务质量满意度以及影响因素进行深入研究,并以某大型运输机场为例验证方法的可行性。研究结果表明,旅客对该机场的服务质量满意概率为0.67,一般满意概率为0.21。反向诊断得到行李提取系统、进出机场综合交通与城市连接的便利性、安检服务效率等影响因素敏感性较高。为机场科学制定提升服务质量措施提供理论基础。展开更多
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘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.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘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.
基金supported in part by the National Key Research and Development Program of China(No.2022YFA1604900)the National Natural Science Foundation of China(NSFC)(Nos.12405154,12235016,12221005,12435009,12275104,92570117)+7 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB34030000)the Fundamental Research Funds for the Central UniversitiesOpen fund for Key Laboratories of the Ministry of Education(No.QLPL2024P01)CUHK-Shenzhen University Development Fund(Nos.UDF01003041 and UDF03003041)Shenzhen Peacock Fund(No.2023TC0007)Ministry of Science and Technology of China(No.2024YFA1611004)the European Union–Next Generation EU through the research(No.P2022Z4P4B)“SOPHYA-Sustainable Optimized PHYsics Algorithms:fundamental physics to build an advanced society”under the program PRIN 2022 PNRR of the Italian Ministero dell’Universitàe Ricerca(MUR)。
文摘Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at a vanishing chemical potential,we constructed a Bayesian holographic QCD model and systematically analyzed the thermodynamic properties of heavy quarkonium in QCD matter under varying temperatures and chemical potentials.We computed the quark-antiquark interquark distance,potential energy,entropy,binding energy,and internal energy.We present detailed posterior distribution results of the thermodynamic quantities of heavy quarkonium,including maximum a posteriori(MAP)value estimates and 95%confidence levels(CL).Through numerical simulations and theoretical analysis,we find that an increase in the temperature and chemical potential reduces the quark distance,thereby facilitating the dissociation of heavy quarkonium and leading to a suppressed potential energy.The increase in temperature and chemical potential also raises the entropy and entropy force,further accelerating the dissociation of heavy quarkonium.The calculated results of binding energy indicate that a higher temperature and chemical potential enhance the tendency of heavy quarkonium to dissociate into free quarks.The internal energy also increases with rising temperature and chemical potential.These findings provide significant theoretical insights into the properties of strongly interacting matter under extreme conditions and lay a solid foundation for the interpretation and validation of future experimental data.Finally,we also present the results for the free energy,entropy,and internal energy of a single quark.
文摘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.
基金supported by the National Natural Science Foundation of China(72071106)Jiangsu Provincial Social Science Fund(23EYA001)+1 种基金Jiangsu Provincial Education Science Planning Fund(Ba/2024/08)Jiangsu Higher Education Association Fund(24FYHLX090)。
文摘A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.
基金supported by the Program for NIM-Basic Research Business Expenses Key Field Program,China(No.AKYCX2315).
文摘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 LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/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.
基金supported by the National Natural Science Foundation of China(Nos.12247126 and 12375123)Henan Postdoctoral Foundation(No.HN2024013)the Natural Science Foundation of Henan Province(No.242300421048)。
文摘Research on neutron-induced fission product yields of^(232)Th 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 of^(232)Th 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.
文摘为明确机场服务质量的影响因素及旅客对机场服务质量满意程度,提高机场服务质量,运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)与贝叶斯网络结合的评估模型,建立大型运输机场服务质量评价指标体系。运用双向推理诊断模型评价机场服务质量,对机场满意度正向推理以及影响指标的反向敏感性诊断。对旅客机场服务质量满意度以及影响因素进行深入研究,并以某大型运输机场为例验证方法的可行性。研究结果表明,旅客对该机场的服务质量满意概率为0.67,一般满意概率为0.21。反向诊断得到行李提取系统、进出机场综合交通与城市连接的便利性、安检服务效率等影响因素敏感性较高。为机场科学制定提升服务质量措施提供理论基础。
