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Risk assessment of syngas pipeline leakage based on a Dynamic Bayesian Network 认领 引用
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作者 Yuqi Liu Min Hua Xuhai Pan 《Emergency Management Science and Technology》 2025年第1期146-155,共10页
furnace,is vulnerable to corrosion and erosion from the transported medium,and is prone to leakage due to long-term high-pressure operation.Moreover,due to its compositional characteristics,syngas pose flammability,ex... furnace,is vulnerable to corrosion and erosion from the transported medium,and is prone to leakage due to long-term high-pressure operation.Moreover,due to its compositional characteristics,syngas pose flammability,explosive,and toxicity risks,which can potentially lead to severe accidents if a leak occurs.Therefore,it is essential to conduct a risk assessment of syngas pipeline leakage.This study proposes a risk assessment approach for syngas pipeline leakage in coal gasification using Dynamic Bayesian Network(DBN).First,the risk identification model is built using Bow-Tie(BT)analysis and then mapped into DBN using a mapping algorithm.Expert evaluation,improved similarity aggregation methods,and fuzzy set theory are employed to quantify prior probabilities.To address the uncertainty of the DBN model,a Leak Noisy-or gate model is introduced.Time series are added to predict the dynamic probabilities.Nine key hazard events,six highly sensitive factors,and the maximum causal chain are identified,and predict the dynamic probability of syngas pipeline leakage and potential consequences.This study provides a theoretical basis for routine maintenance and risk assessment of syngas pipelines. 展开更多
关键词 risk assessment dynamic bayesian network fuzzy set theory dynamic bayesian netwo leakage bow tie analysis coal gasification syngas pipeline
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Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks 认领 引用 被引量：19
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作者 Haoyu Mao Nuwen Xu +4 位作者 Xiang Li Biao Li Peiwei Xiao Yonghong Li Peng Li 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第10期2521-2538,共18页
One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the ev... One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the evolutionary mechanism of microfractures within the surrounding rock mass during rockburst development and develop a rockburst warning model.The study area was chosen through the combination of field studies with an analysis of the spatial and temporal distribution of microseismic(MS)events.The moment tensor inversion method was adopted to study rockburst mechanism,and a dynamic Bayesian network(DBN)was applied to investigating the sensitivity of MS source parameters for rockburst warnings.A MS multivariable rockburst warning model was proposed and validated using two case studies.The results indicate that fractures in the surrounding rock mass during the development of strain-structure rockbursts initially show shear failure and are then followed by tensile failure.The effectiveness of the DBN-based rockburst warning model was demonstrated using self-validation and K-fold cross-validation.Moment magnitude and source radius are the most sensitive factors based on an investigation of the influence on the parent and child nodes in the model,which can serve as important standards for rockburst warnings.The proposed rockburst warning model was found to be effective when applied to two actual projects. 展开更多
关键词 Microseismic monitoring Moment tensor Dynamic Bayesian network(DBN) Rockburst warning Shuangjiangkou hydropower station
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Target threat estimation based on discrete dynamic Bayesian networks with small samples 认领 引用 被引量：7
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作者 YE Fang MAO Ying +1 位作者 LI Yibing LIU Xinrui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第5期1135-1142,共8页
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr... The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications. 展开更多
关键词 discrete dynamic Bayesian network(DDBN) parameter learning missing data filling Bayesian estimation
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Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input 认领 引用 被引量：5
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作者 Long Chen Linqing Wang +2 位作者 Zhongyang Han Jun Zhao Wei Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第5期1437-1445,共9页
Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian netwo... Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian networks(KDBN),serving as an effective method for PIs construction,suffer from high computational load using the stochastic algorithm for inference.This study proposes a variational inference method for the KDBN for the purpose of fast inference,which avoids the timeconsuming stochastic sampling.The proposed algorithm contains two stages.The first stage involves the inference of the missing inputs by using a local linearization based variational inference,and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices.To verify the effectiveness of the proposed method,a synthetic dataset and a practical dataset of generation flow of blast furnace gas(BFG)are employed with different ratios of missing inputs.The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one. 展开更多
关键词 Industrial time series kernel dynamic Bayesian networks(KDBN) prediction intervals(PIs) variational inference
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A reconfigurable dynamic Bayesian network for digital twin modeling of structures with multiple damage modes 认领 引用 被引量：2
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作者 Yumei Ye Qiang Yang +3 位作者 Jingang Zhang Songhe Meng Jun Wang Xia Tang 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2023年第4期251-260,共10页
Dynamic Bayesian networks(DBNs)are commonly employed for structural digital twin modeling.At present,most researches only consider single damage mode tracking.It is not sufficient for a reusable spacecraft as various ... Dynamic Bayesian networks(DBNs)are commonly employed for structural digital twin modeling.At present,most researches only consider single damage mode tracking.It is not sufficient for a reusable spacecraft as various damage modes may occur during its service life.A reconfigurable DBN method is proposed in this paper.The structure of the DBN can be updated dynamically to describe the interactions between different damages.Two common damages(fatigue and bolt loosening)for a spacecraft structure are considered in a numerical example.The results show that the reconfigurable DBN can accurately predict the acceleration phenomenon of crack growth caused by bolt loosening while the DBN with time-invariant structure cannot,even with enough updates.The definition of interaction coefficients makes the reconfigurable DBN easy to track multiple damages and be extended to more complex problems.The method also has a good physical interpretability as the reconfiguration of DBN corresponds to a specific mechanism.Satisfactory predictions do not require precise knowledge of reconfiguration conditions,making the method more practical. 展开更多
关键词 Dynamic Bayesian network Reusable spacecraft Damage Reconfiguration
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Comparison of dynamic Bayesian network approaches for online diagnosis of aircraft system 认领 引用 被引量：2
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作者 于劲松 冯威 +1 位作者 唐荻音 刘浩 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第11期2926-2934,共9页
The online diagnosis for aircraft system has always been a difficult problem. This is due to time evolution of system change, uncertainty of sensor measurements, and real-time requirement of diagnostic inference. To a... The online diagnosis for aircraft system has always been a difficult problem. This is due to time evolution of system change, uncertainty of sensor measurements, and real-time requirement of diagnostic inference. To address this problem, two dynamic Bayesian network(DBN) approaches are proposed. One approach prunes the DBN of system, and then uses particle filter(PF) for this pruned DBN(PDBN) to perform online diagnosis. The problem is that estimates from a PF tend to have high variance for small sample sets. Using large sample sets is computationally expensive. The other approach compiles the PDBN into a dynamic arithmetic circuit(DAC) using an offline procedure that is applied only once, and then uses this circuit to provide online diagnosis recursively. This approach leads to the most computational consumption in the offline procedure. The experimental results show that the DAC, compared with the PF for PDBN, not only provides more reliable online diagnosis, but also offers much faster inference. 展开更多
关键词 online diagnosis dynamic Bayesian network particle filter dynamic arithmetic circuit
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Reliability analysis for wireless communication networks via dynamic Bayesian network 认领 引用 被引量：1
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作者 YANG Shunqi ZENG Ying +2 位作者 LI Xiang LI Yanfeng HUANG Hongzhong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第5期1368-1374,共7页
The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works ... The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices,radio propagation,network topology,and dynamic behaviors.Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks.As one of the most popular modeling methodologies,the dynamic Bayesian network(DBN)is proposed.However,it is insufficient for the wireless communication network which contains temporal and non-temporal events.To this end,we present a modeling methodology for a generalized continuous time Bayesian network(CTBN)with a 2-state conditional probability table(CPT).Moreover,a comprehensive reliability analysis method for communication devices and radio propagation is suggested.The proposed methodology is verified by a reliability analysis of a real wireless communication network. 展开更多
关键词 dynamic Bayesian network(DBN) wireless commu-nication network continuous time Bayesian network(CTBN) network reliability
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Dynamic Bayesian Network Based Prognosis in Machining Processes 认领 引用 被引量：3
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作者 董明 杨志波 《Journal of Shanghai Jiaotong university(Science)》 EI 2008年第3期318-322,共5页
Condition based maintenance (CBM) is becoming more and more popular in equipment main-tenance. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostic... Condition based maintenance (CBM) is becoming more and more popular in equipment main-tenance. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostics. A dynamic Bayesian network (DBN) based prognosis method was investigated to predict the remaining useful life (RUL) for an equipment. First, a DBN based prognosis framework and specific steps for building a DBN based prognosis model were presented. Then, the corresponding inference algorithms for DBN based prognosis were provided. Finally, a prognosis procedure based on particle filtering algorithms was used to predict the RUL of drill-bits of a vertical drilling machine, which is commonly used in industrial process. Preliminary experimental results are promising. 展开更多
关键词 dynamic Bayesian network （DBN） prognosis remaining useful life
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Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network 认领 引用 被引量：7
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作者 Tang Zheng Gao Xiaoguang 《Journal of Systems Engineering and Electronics》 SCIE EI 2008年第4期702-708,共7页
The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with se... The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with self-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discrete dynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discrete dynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesize different targets which are not predominant. In this way, various features at the same time, as well as the same feature appearing at different time complement mutually; in addition, the accuracy and reliability of electronic jamming decision making are enhanced significantly. 展开更多
关键词 self-defense electronic jamming discrete dynamic Bayesian network decision-making model
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Prediction of visibility in the Arctic based on dynamic Bayesian network analysis 认领 引用 被引量：3
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作者 Shijun Zhao Yulong Shan Ismail Gultepe 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2022年第4期57-67,共11页
With the accelerated warming of the world,the safety and use of Arctic passages is receiving more attention.Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and open... With the accelerated warming of the world,the safety and use of Arctic passages is receiving more attention.Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and opening of ice-free northern passages.Numerical weather prediction and statistical prediction are two methods for predicting visibility.As microphysical parameterization schemes for visibility are so sophisticated,visibility prediction using numerical weather prediction models includes large uncertainties.With the development of artificial intelligence,statistical prediction methods have received increasing attention.In this study,we constructed a statistical model with a physical basis,to predict visibility in the Arctic based on a dynamic Bayesian network,and tested visibility prediction over a 1°×1°grid area averaged daily.The results show that the mean relative error of the predicted visibility from the dynamic Bayesian network is approximately 14.6%compared with the inferred visibility from the artificial neural network.However,dynamic Bayesian network can predict visibility for only 3 days.Moreover,with an increase in predicted area and period,the uncertainty of the predicted visibility becomes larger.At the same time,the accuracy of the predicted visibility is positively correlated with the time period of the input evidence data.It is concluded that using a dynamic Bayesian network to predict visibility can be useful over Arctic regions for projected climatic changes. 展开更多
关键词 Arctic visibility prediction artificial neural network dynamic Bayesian network
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Dynamic Bayesian Network for Continuous Health Index Estimation and Condition Assessment of Power Transformers 认领 引用 被引量：1
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作者 Shuaibing Li Hongyu Li +1 位作者 Yi Cui Yongqiang Kang 《High Voltage》 SCIE EI CSCD 2026年第2期385-398,共14页
To address the limitations of traditional transformer health index(HI),such as high dependence on data integrity and inability to assist in diagnosing latent faults,this study proposes an HI prediction method based on... To address the limitations of traditional transformer health index(HI),such as high dependence on data integrity and inability to assist in diagnosing latent faults,this study proposes an HI prediction method based on dynamic Bayesian network(DBN).The method first constructs a DBN for characterising the health state indicator system of power transformers according to their structural composition and operational environmental conditions.Next,the entropy weight method and fuzzy membership function are employed to determine the prior probability tables between DBN nodes,whereas the logistic regression method is used to establish conditional probability tables.Finally,existing literature data and field measurement data are utilised to validate the proposed method.The results demonstrate that:(1)the developed DBN can not only effectively calculate the current HI of transformers but also predict HI values for different future time intervals and(2)it can evaluate the overall health state of transformers and identify potential faults in individual components based on node-specific HI values.Therefore,this method provides valuable support for operation and maintenance personnel in making informed decisions regarding maintenance strategies,batch retirement and replacement plans. 展开更多
关键词 condition assessment power transformers entropy weight method continuous health index fuzzy membership function dynamic bayesian network dynamic bayesian network dbn characterising health state indicator system
A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study 认领 引用 被引量：7
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作者 CAI Bao-ping ZHANG Yan-ping +5 位作者 YUAN Xiao-bing GAO Chun-tan LIU Yong-hong CHEN Guo-ming LIU Zeng-kai JI Ren-jie 《China Ocean Engineering》 SCIE EI CSCD 2020年第5期597-607,共11页
Under unanticipated natural disasters, any failure of structure components may cause the crash of an entire structure system. Resilience is an important metric for the structure system. Although many resilience metric... Under unanticipated natural disasters, any failure of structure components may cause the crash of an entire structure system. Resilience is an important metric for the structure system. Although many resilience metrics and assessment approaches are proposed for engineering system, they are not suitable for complex structure systems, since the failure mechanisms of them are different under the influences of natural disasters. This paper proposes a novel resilience assessment metric for structure system from a macroscopic perspective, named structure resilience, and develops a corresponding assessment approach based on remaining useful life of key components. Dynamic Bayesian networks(DBNs) and Markov are applied to establish the resilience assessment model. In the degradation process, natural degradation and accelerated degradation are modelled by using Bayesian networks, and then coupled by using DBNs. In the recovery process, the model is established by combining Markov and DBNs. Subsea oil and gas pipelines are adopted to demonstrate the application of the proposed structure metric and assessment approach. 展开更多
关键词 structure resilience structure system remaining useful life dynamic Bayesian networks
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Predicting mobile users’behaviors and locations using dynamic Bayesian networks 认领 引用 被引量：4
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作者 Jianrong Hou Hui Zhao +1 位作者 Xiaofeng Zhao Jie Zhang 《Journal of Management Analytics》 2016年第3期191-205,共15页
This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in dif... This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in different locations and online social interactions.We combine dynamic Bayesian networks with a majority voting model which is based on social interaction information to estimate the users’behaviors and predict the locations.By analyzing Instagram media records,spanning a period of 3 months,we explore rarely visited locations,which are often ignored as noise in previous research.In comparison,our model,using Instagram data with two existing location prediction models,shows that(1)our location prediction is more accurate and robust in both the general location and the location outside the living area;(2)social relations are instrumental in the location prediction as social interaction information can increase the accuracy of the prediction. 展开更多
关键词 location prediction dynamic Bayesian network majority voting social interaction Instagram
Construction Project Progress Management: Development of a Multi-Source Data Fusion Model for Dynamic Risk Probability Prediction 认领 引用
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作者 LIU Rui 《外文科技期刊数据库（文摘版）工程技术》 2026年第5期068-072,共5页
Schedule control is a critical component in ensuring the achievement of project timelines, playing a vital role in enhancing investment returns, optimizing resource utilization, and fulfilling contractual obligations.... Schedule control is a critical component in ensuring the achievement of project timelines, playing a vital role in enhancing investment returns, optimizing resource utilization, and fulfilling contractual obligations. However, construction projects inherently involve significant investments, prolonged durations, multiple stakeholders, and complex environments, all subject to influences from human factors, machinery, materials, methodologies, environmental conditions, and management practices. This results in distinct dynamic characteristics of schedule risks: over time, various risk sources may emerge, diminish, or evolve, with mitigation strategies varying depending on the project phase. Traditional schedule management approaches rely solely on quantitative or static analytical methods such as the Critical Path Method and Plan Review Techniques, making decisions based on fixed activity time constraints, which fail to adequately capture the nonlinear progression of schedule risks during construction. 展开更多
关键词 Construction project schedule management Real-time risk estimation Integrated information processing Dynamic Bayesian network Smart construction
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A method for modeling and evaluating the interoperability of multi-agent systems based on hierarchical weighted networks 认领 引用 被引量：1
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作者 DONG Jingwei TANG Wei YU Minggang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第3期754-767,共14页
Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weight... Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies. 展开更多
关键词 complex network agent interoperability susceptible-infected-recovered model dynamic Bayesian network
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A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving 认领 引用 被引量：8
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作者 Jinxin Liu Yugong Luo +3 位作者 Zhihua Zhong Keqiang Li Heye Huang Hui Xiong 《Engineering》 SCIE EI CAS CSCD 2022年第12期228-239,共12页
In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisio... In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety.In this paper,we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction,which consists of a driving inference model(DIM)and a trajectory prediction model(TPM).The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network.The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information.To further improve the prediction accuracy and realize uncertainty estimation,we develop a Gaussian process-based TPM,considering both the short-term prediction results of the vehicle model and the driving motion characteristics.Afterward,the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios.The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods. 展开更多
关键词 Autonomous driving Dynamic Bayesian network Driving intention recognition Gaussian process Vehicle trajectory prediction
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Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance:Subsea Christmas Tree System as A Case Study 认领 引用 被引量：1
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作者 WU Qi-bing CAI Bao-ping +5 位作者 FAN Hong-yan WANG Guan-nan RAO Xi GE Weifeng SHAO Xiao-yan LIU Yong-hong 《China Ocean Engineering》 SCIE EI CSCD 2024年第2期198-209,共12页
Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of predic... Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method. 展开更多
关键词 remaining useful life Wiener process dynamic Bayesian networks maintenance subsea Christmas tree system
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Parameter uncertainty modeling of safety instrumented systems 认领 引用 被引量：1
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作者 Bao-Ping Cai Wen-Chao Li +6 位作者 Yong-Hong Liu Yan-Ping Zhang Yi Zhao Xiang-Di Kong Zeng-Kai Liu Ren-Jie Ji Qiang Feng 《Petroleum Science》 SCIE EI CAS CSCD 2021年第6期1813-1828,共16页
In this study,a novel safety integrity level(SIL)determination methodology of safety instrumented systems(SISs)with parameter uncertainty is proposed by combining multistage dynamic Bayesian networks(DBNs)and Monte Ca... In this study,a novel safety integrity level(SIL)determination methodology of safety instrumented systems(SISs)with parameter uncertainty is proposed by combining multistage dynamic Bayesian networks(DBNs)and Monte Carlo simulation.A multistage DBN model for SIL determination with multiple redundant cells is established.The models of function inspection test interval and function inspection test stages are alternately connected to form the multistage DBNs.The redundant cells can have different M out of N voting system architectures.An automatic modeling of conditional probability between nodes is developed.The SIL determination of SISs with parameter uncertainty is constructed by using the multistage DBNs and Monte Carlo simulation.A high-pressure SIS in the export of oil wellplatform is adopted to demonstrate the application of the proposed approach.The SIL and availability of the SIS and its subsystems are obtained.The influence of single subsystem on the SIL and availability of the SIS is studied.The influence of single redundant element on the SIL and availability of the subsystem is analyzed.A user-friendly SIL determination software with parameter uncertainty is developed on MATLAB graphical user interface. 展开更多
关键词 Safety integrity level Dynamic Bayesian network Monte Carlo Parameter uncertainty
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Co-regulated Protein Functional Modules with Varying Activities in Dynamic PPI Networks 认领 引用 被引量：4
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作者 Yuan Zhang Nan Du +2 位作者 Kang L Kebin Jia Aidong Zhang 《Tsinghua Science and Technology》 SCIE EI CAS 2013年第5期530-540,共11页
Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple ge... Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate. 展开更多
关键词 dynamic protein-protein interaction networks dynamic active modules varying activities Bayesian graphical mode
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Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model 认领 引用 被引量：2
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作者 Yixuan Tu Kun Jia +3 位作者 Shunlin Liang Xiangqin Wei Yunjun Yao Xiaotong Zhang 《International Journal of Digital Earth》 SCIE EI 2020年第4期487-503,共17页
A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because th... A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R2=0.7757,RMSE=0.0881)performed better than either the previous method(R2=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R2=0.7457,RMSE=0.1249). 展开更多
关键词 Dynamic Bayesian network fractional vegetation cover global land surface satellite radiative transfer model dynamic vegetation model
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