scholarly journals Reliability Analysis of Gasifier Lock Bucket Valve System Based on DBN Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ming Liu ◽  
Jiayue Ma ◽  
Yili Duo ◽  
Tie Sun
Keyword(s):  
Monte Carlo ◽  
Structure Learning ◽  
Weak Link ◽  
Dynamic Bayesian Network ◽  
Predictive Inference ◽  
Parameter Learning ◽  
Valve System ◽  
Failure Data ◽  
System A ◽  
Reverse Inference

In order to solve the problem of zero-failure data and dynamic failure in gasification system, a dynamic Bayesian network (DBN) combined with Monte Carlo simulations is proposed to analyze the reliability of the gasifier lock bucket valve system. On the basis of studying the structure of the gasifier lock bucket valve system, the reliability model of the system is built based on DBN, and the structure learning is realized. The Monte Carlo simulation is used for the timed ending test in Bayesian estimation, which effectively solves the problem of zero-failure data and realizes the parameter learning. Through the Metropolis-Hastings (M-Hs) algorithm, the prior distribution of dynamic node is randomly sampled to obtain the target distribution, which makes the reliability predictive inference for DBN of the gasifier lock bucket valve system faster and more accurate. The obtained reliability prediction is a curve varying with time. The results show that the valve frequent switch node of DBN of the gasifier lock bucket valve system is identified as the weak link by the powerful reverse inference for DBN, which needs to be paid more attention to. This method can effectively improve the maintenance level of the gasifier lock bucket valve system and can effectively reduce the possibility of accidents.

scholarly journals Spatial transmission network construction of influenza-like illness using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

2020 ◽  
Author(s):  
Jianqing Qiu ◽  
Huimin Wang ◽  
Lin Hu ◽  
Changhong Yang ◽  
Tao Zhang
Keyword(s):  
Temporal Trend ◽  
Structure Learning ◽  
Moving Average ◽  
Dynamic Bayesian Network ◽  
Autoregressive Moving Average ◽  
Transmission Network ◽  
Parameter Learning ◽  
Vector Autoregressive ◽  
Influenza Transmission ◽  
Spatio Temporal

Abstract Background: Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network.Methods: The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network.Results: The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance.Conclusions: This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors’ incidence and the transmission relationships.

scholarly journals Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianqing Qiu ◽  
Huimin Wang ◽  
Lin Hu ◽  
Changhong Yang ◽  
Tao Zhang
Keyword(s):  
Temporal Trend ◽  
Structure Learning ◽  
Moving Average ◽  
Dynamic Bayesian Network ◽  
Autoregressive Moving Average ◽  
Transmission Network ◽  
Parameter Learning ◽  
Vector Autoregressive ◽  
Influenza Transmission ◽  
Spatio Temporal

Abstract Background Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network. Methods The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network. Results The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance. Conclusions This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors’ incidence and the transmission relationships.

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

Role of the magnetic anisotropy in the magnetocaloric effect for a superparamagnetic nanoparticle system: a Monte Carlo study

2008 ◽  
Vol 205 (6) ◽  
pp. 1343-1348 ◽  
Author(s):  
D. Baldomir ◽  
D. Serantes ◽  
M. Pereiro ◽  
J. Rivas ◽  
C. Vázquez-Vázquez ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Magnetic Anisotropy ◽  
Magnetocaloric Effect ◽  
Monte Carlo Study ◽  
System A ◽  
Nanoparticle System

scholarly journals Biological Network Inference with GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

2021 ◽  
Author(s):  
Kaixian Yu ◽  
Zihan Cui ◽  
Xin Sui ◽  
Xing Qiu ◽  
Jinfeng Zhang
Keyword(s):  
Monte Carlo ◽  
Network Structure ◽  
Network Inference ◽  
Sequential Monte Carlo ◽  
Structure Learning ◽  
Biomedical Science ◽  
Optimal Network ◽  
Genomic Studies ◽  
Complex Correlation ◽  
Open Question

Abstract Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.

scholarly journals Dynamic Safety Management on the Key Equipment of Coal Gasification Based on Dbt-Dbn Method

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Han Gao ◽  
Yili Duo ◽  
Tie Sun ◽  
Xuefeng Yang
Keyword(s):  
Risk Assessment ◽  
Coal Gasification ◽  
Failure Modes ◽  
Safety Management ◽  
Dynamic Bayesian Network ◽  
Dynamic Correlation ◽  
Dynamic Risk ◽  
Failure Data ◽  
Dynamic Risk Assessment ◽  
Bow Tie

Gasifier system is one of the important components of coal gasification device. The technical characteristics of this system mainly lie in the following facts as huge technical scale and high complexity, and there is a dynamic correlation between the failure modes of gasification equipment. Traditional safety analysis methods such as fault tree and bow-tie diagram suffer from drawbacks as being static and ineffective in handling uncertainty, which hamper their application to risk analysis of process systems. This paper presents a newly developed model based on Dynamic Bow-Tie (DBT) and Dynamic Bayesian network (DBN) for quantitative dynamic risk assessment of gasifier system. In the meantime, in order to cope with the uncertainty of the failure data, fuzzy numbers and the defuzzification method are used to transform the experts' language into the failure rates. The results showed that dynamic risk assessment can solve the difficulties dealing with complex dynamic systems which have process variables and characteristics such as multiple, failure correlations, and noncoherence. And it also has important theoretical significance and application value for coal chemical industry to improve the scientificity of risk assessment.

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