Hierarchical Bayesian Network Research Articles

Fault detection and isolation in system of multiple sources of energy using hierarchical Bayesian belief networks

Fault detection and isolation in system of multiple sources of energy using hierarchical Bayesian belief networks

BCNN: An Effective Multifocus Image fusion Method Based on the Hierarchical Bayesian and Convolutional Neural Networks

BCNN: An Effective Multifocus Image fusion Method Based on the Hierarchical Bayesian and Convolutional Neural Networks

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling.

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states.

A hierarchical Bayesian network-based semi-mechanistic model for handling data variabilities in dynamical process systems

This work presents a hierarchical Bayesian network-based model for handling dynamic nature, data variabilities, and information fusion in a dynamical process system. The overall dynamics has been captured by physics-based models while uncertainty and local variabilities in the model parameters have been accounted for by using a hierarchical Bayesian network formalism. The resulting semi-mechanistic model has been investigated in two cases, a simulated case of an enzymatic reaction system and a real-world scenario of pandemic risk forecasting. The first case study on the enzymatic reaction system shows effectiveness of the proposed semi-mechanistic framework in a closed system with limited data from different sources. The second case illustrates scope of the proposed semi-mechanistic framework to open systems by investigating transmission of disease under evolving conditions of the advent of multiple waves of a pandemic outbreak. The findings have been presented in terms of the most probable values and credible intervals that account for data variabilities in different scenarios. The model is benchmarked and validated for the fatality forecast of the Coronavirus Disease of 2019 by the observed data and benchmark models reported by the Center for Disease Prevention and Control of the United States [https://covid.cdc.gov/covid-data-tracker/#datatracker-home].

Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex.

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.

Cyber Physical System for Distributed Network Using DoS Based Hierarchical Bayesian Network

Cyber Physical System for Distributed Network Using DoS Based Hierarchical Bayesian Network

A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention

Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model – hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.

Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

In the context of modern engineering, environmental, and societal concerns, there is an increasing demand for methods able to identify rational management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level, often assuming statistical, structural, or cost independence among components, due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks, decoupling the originally joint system state space to component networks conditional on shared random variables. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.

Modeling the effect of emergency response on domino effects in the coal gasification process by fuzzy hierarchical analysis and Bayesian network.

Emergency response has an important impact on the mitigation of the domino effects. However, consequence analysis of the domino effect often ignores emergency response due to its complexity and uncertainty. In this paper, Fuzzy Analytic Hierarchy Process (FAHP) is adopted to evaluate the reliability of emergency response process. On this basis, using Bayesian Network (BN) model, the domino effect under the influence of emergency response is modeled. Based on the total duration of the above-mentioned emergency response process to determine the consequences of domino effect under the action of safety barriers. The application of the approach has been demonstrated by an illustrative case study. The results show that the probability of domino effect is reduced by an order of magnitude when active barriers, passive barriers and emergency response are considered together. This work can provide relevant basis for formulating safety protection measures in chemical process industry.

Importance measures for critical components in complex system based on Copula Hierarchical Bayesian Network

In order to identify the vulnerable components and ensure the required reliability of mechatronics systems, importance measures of critical components are crucially used in the early design of systems. However, complex mechatronics systems have the properties of hierarchy, nonlinearity, dependency, uncertainty, and randomness, which make it difficult to analyze the coupling failure mechanisms, model the system, estimate its reliability, and complete importance measures of its components. This paper proposes importance measures for components with continuous time degradation. The Wiener process model is used to describe the continuous-time degradation process, and the Copula Hierarchical Bayesian Network (CHBN) is developed for system reliability estimation. Six importance measures are proposed for continuous-time degrading components. These importance measures provide a time-dependent analysis of the criticality of components, thus adding insights on the contributions of the components on the system reliability or performance over time. A case study on the harmonic gear drive is then conducted to demonstrate the use of the proposed importance measures. The results of the study show that the CHBN-based importance measures can be a valuable decision-support tool for designers in the early design of systems.

Hierarchical Bayesian Networks for Predicting the Structural Behavior of Coupled Systems

<div class="section abstract"><div class="htmlview paragraph">Simulations play an important role in the continuing effort to reduce development time and risks. However, large and complex models are necessary to accurately simulate the dynamic behavior of complex engineering systems. In recent years, the use of data-driven models based on machine learning (ML) algorithms has become popular for predicting the structural dynamic behavior of mechanical systems. Due to their advantages in capturing non-linear behavior and efficient calculation, data-driven models are used in a variety of fields like uncertainty quantification, optimization problems, and structural health monitoring. However, the black box structure of ML models reduces the interpretability of the results and complicates the decision-making process. Hierarchical Bayesian Networks (HBNs) offer a framework to combine expert knowledge with the advantages of ML algorithms. In general, Bayesian Networks (BNs) allow connecting inputs, parameters, outputs, and experimental data of various models to predict the overall system-level dynamic behavior. This characteristic of BNs enables a divide and conquer approach. Hence, complex engineering systems can be split into more easily describable subsystems. HBNs are an extension of BNs that can use knowledge about the structure of the data to introduce a bias that can contribute to improving the modelling result. In this work, an approach to design a HBN is presented where each model in the network can be a parametric reduced finite-element models. The influence of the hierarchical approach is evaluated by comparing a HBN and a BN of the model from the Sandia structural dynamics challenge.</div></div>

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio’s long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer’s signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer’s activities.

Sustainability Measurement of Transportation Systems in China: A System-Based Bayesian Network Approach

Sustainability has been a challenging issue in the transportation industry, which necessitates obtaining a better measurement of transport sustainability performance. To appropriately measure performance, this paper presents a hybrid approach based on the hierarchical Bayesian network model (BNM) and Principal Component Analysis (PCA). The proposed BNM encompasses social, economic, environmental, and technological dimensions, where each dimension consists of various subdivisions. The Conditional Probability Table of the model is determined by PCA. Twenty-three sustainable transportation indicators involved in the different stages of traffic management are used to kick off the calculation and probability propagation. The results show that the overall transport sustainability of the selected cities is generally at a medium level, indicating that there is much room for further improvement. The sustainability-economic coupling analysis exhibits the nonlinear relationship between sustainability and economic level, revealing that economic growth does not necessarily lead to the enhancement of the transport sustainability. Additionally, the sensitivity analysis reveals that “Accessibility,” “Serviceability,” “Reliability,” and “Innovation” demonstrate an upward trend, indicating their great effect on transportation sustainability. Last, the policy implications of this study can not only offer a solution for the current needs of transportation systems but also serve as more transparent decision-making to develop a sustainable transportation system in the future.

Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

The dynamics of fecal indicator bacteria, such as fecal coliforms (FC) in streams, are influenced by the interactions of a myriad of factors. To predict complex spatiotemporal patterns of FC in streams and assess the relative importance of numerous controlling factors, the adoption of a hierarchical Bayesian network (HBN) was proposed in this study. By introducing latent variables correlated to the observed variables into a Bayesian network, the HBN can represent causal relationships among a large set of variables with a multilevel hierarchy. The study area encompasses 215 sites across the watersheds of the four major rivers in South Korea. The monitoring data collected during the 2012–2019 period included 32 input variables pertaining to meteorology, geography, soil characteristics, land cover, urbanization index, livestock density, and point sources. As model endpoints, the exceedance probability of the FC standard concentration as well as two pollution characteristics (i.e., pollution degree and type), derived from FC load duration curves were used. The probability of exceeding an FC threshold value (200 CFU/100 mL) showed spatiotemporal variations, whereas pollution degree and type showed spatial variations that represent long-term severity and relative dominance of nonpoint and point source fecal pollution, respectively. The conceptual model was validated using structural equation modeling to develop the HBN. The results demonstrate that the HBN effectively simplified the model structure, while showing strong model performance (AUC = 0.81, accuracy = 0.74). The results of the sensitivity analysis indicate that land cover is the most important factor in predicting the probability of exceedance and pollution degree, whereas the urbanization index explains most of the variability in pollution type. Furthermore, the results of the scenario analysis suggest that the HBN provides an interpretable framework in which the interaction of controlling factors has causal relationships at different levels that can be identified and visualized.

Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

Multiscale leaf area index assimilation for Moso bamboo forest based on Sentinel-2 and MODIS data

Leaf area index (LAI) is an important driving factor in forest ecosystems carbon cycle, the acquisition of multiscale LAI can illustrate the carbon cycle at different scales and its dynamic response to environmental changes. Moso bamboo forest (MBF) has strong carbon sequestration capability and unique phenological characteristics of on-years and off-years. In this study, based on the MODIS LAI and Sentinel-2 reflectance products in 2018–2019, we coupled the Hierarchical Bayesian Network (HBN) algorithm, LAI dynamic model and PROSAIL model to acquire multiscale spatiotemporal assimilated LAI (500-m, 100-m, 20-m) of MBF. First, the results showed that band7 (B7) and band8a (B8a) of Sentinel-2 were highly sensitive to LAI; the simulated leaf and canopy reflectance by PROSAIL demonstrated higher accuracies and lower errors in these two bands. Second, after applying the Savitzky-Golay smoothing, compared with the observed LAI (Obs_eLAI), the accuracy of MODIS LAI was improved by 121%, and the error was reduced by 24%. Third, there was a significant correlation between the multiscale assimilated LAI (HBN_LAIs) and Obs_eLAI (R500m_HBN_LAI2=0.80,R100m_HBN_LAI2=0.82,R20m_HBN_LAI2=0.80). Finally, the assimilated results in 2018 (off-year) were better than those in 2019 (on-year); the variations in multiscale spatiotemporal assimilated LAI were consistent with the actual growth trends of MBF. This study provides a feasible way to accurately obtain multiscale high-resolution LAI for the carbon cycle simulation of bamboo forests.

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data.

Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering

Clustering high-dimensional data under the curse of dimensionality is an arduous task in many applications domains. The wide dimension yields the complexity-related challenges and the limited number of records leads to the overfitting trap. We propose to tackle this problematic using the graphical and probabilistic power of the Bayesian network. Our contribution is a new loose hierarchical Bayesian network model that encloses latent variables. These hidden variables are introduced for ensuring a multi-view clustering of the records. We propose a new framework for learning our proposed Bayesian network model. It starts by extracting the cliques of highly dependent features and it proceeds to learn representative latent variable for each features’ clique. The experimental results of our comparative analysis prove the efficiency of our model in tackling the distance concentration challenge. They also show the effectiveness of our model learning framework in skipping the overfitting trap, on benchmark high-dimensional datasets.

Efficacy of Non-pharmacologic Auxiliary Treatments in Improving Defecation Function in Children With Chronic Idiopathic Constipation: A Systematic Review and Network Meta-analysis.

Background: Non-pharmacologic auxiliary treatments have been considered crucial therapies for treating chronic idiopathic constipation (CIC) during the past decades worldwide. Several treatment patterns are available, but their relative efficacy is obscure because there are no head-to-head randomized controlled trials, especially in children. We conducted this network meta-analysis to evalute the effectiveness of these therapies in improving defecation function based on their direct comparisons with standard medical care.Methods: Medline, Embase, and Cochrane Central were searched for randomized controlled trials (RCTs) published in English from inception to October 2020, assessing the efficacy of auxiliary therapies (behavior therapy, physiotherapy, biofeedback, or anorectal manometry) in children with CIC. We extracted data for endpoints, risk of bias, and evidence quality. Eligible studies in the meta-analysis reported the data of a dichotomous assessment of overall response to treatment (response or not) or defecation frequency per week after treatment. The hierarchical Bayesian network meta-analysis was used in the study. We chose a conservative methodology, random effects model, to pool data which could handle the heterogeneity well. The relative risk (RR) with 95% confidence intervals (CIs) was calculated for dichotomous outcomes. For continuous results, weighted mean difference (WMD) with related CIs was calculated. The included treatments were ranked to define the probability of being the best treatment.Results: Seven RCTs (838 patients) met inclusion and endpoint criteria. Based on an endpoint of the absence of constipation (Rome criteria) with laxatives allowed, physiotherapy plus standard medical care (SMC) had the highest probability (84%) to bethe most effective therapy. When the treatment response was defined as an absence of constipation with not laxatives allowed, biofeedback plus SMC ranked first (probability 52%). Physiotherapy plus SMC ranked first when the endpoint was based on defecation frequency per week with laxatives allowed (probability 86%).Conclusion: Almost all auxiliary therapies are effective complementary therapies for treating CIC, but they needed to be used simultaneously with SMC. Nevertheless, because of the small number of eligible studies and their small sample sizes, the differences in treatment duration and the endpoints, large sample RCTs with long-term follow-up are required for further investigation.

From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach.

In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.