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Due to the advantages of indicating variable structure and efficient reasoning, Bayesian Networks (BN) have been widely used in data-driven soft sensor applications. However, restricted to linear and conditional Gaussian property, BN-based soft ...

In this paper, we developed a dynamic Bayesian network (DBN) model to quantify uncertainties on battlefields. The model consists of the enemy's intention prediction model and the intelligence, surveillance, and reconnaissance (ISR) reliability ...

Failures of the pipelines can not only result in economic losses, but also potentially lead to serious safety accidents. Therefore, it is important to assess the failure risk of pipelines in order to prevent and mitigate pipeline failure ...

Human–robot collaboration (HRC) systems integrate the strengths of humans and robots to improve joint system performance. In particular, human–robot cooperative manipulation (co-manipulation), a prominent area within the field of HRC, in which ...

• A dynamic Bayesian network model for human trust in co-manipulation
• A normalized continuous Baum–Welch algorithm for parameter learning of the trust model
• Variable impedance control for co-manipulation based on trust and intent ...

• Developing a resilience-centered robust maintenance optimization model that caters to different decision-making levels.
• Identifying the optimal allocation plan for backups, and the optimal maintenance strategy.
• Addressing the ...

In this paper, we developed a new model that combines resilience concept with robust maintenance resource allocation, and team assignment strategies to evaluate system performance after disruption. This model offers insights for proactive ...

• Developed a dynamic Bayesian network to investigate the consequences of natural gas pipeline failures.
• Considered seven parameters and twelve consequence factors to analyze the overall loss.
• Can handle both static and dynamic ...

Modern natural gas pipeline failures constitute devastating disasters, as they can result in cascading secondary crises. Therefore, reduction of buried gas pipeline's reliability, has become a major concern among stakeholders and researchers in ...

A dynamic Bayesian Network (DBN)-based fault prognosis framework is proposed in this study to predict the future fault probabilities of gradual faults. The proposed framework utilizes the trend in prediction error generated from data driven forecasting ...

Water losses become a major problem that threatens Water Distribution System (WDS) and require serious investigations and sophisticated control process. Water utilities and industries are mainly concerned to understand the cause of ...

• We proposed a DBN-based dynamic operational water leakage risk assessment system

Sepsis is among the leading causes of morbidity, mortality and high costs in the ICU. The early prediction and intervention of sepsis is a challenging task under strict time and cost constraints. In this paper, a novel High-order Markov Dynamic ...

Typically, delay tolerant network (DTN) suffers from frequent disruption, high latency, and lack of stable connections between nodes. As a special case of DTN, vehicular delay tolerant network (VDTN) has particular spatial-temporal ...${}^{}$

• Project portfolio risks are assessed dynamically from the life cycle perspective.
• A fuzzy-dynamic Bayesian network is proposed to assess project portfolio risks.
• Risks are assessed considering causality and time dependency.
• The ...

Project portfolio risks (PPRs) are mostly considered in terms of interdependency between projects, ignoring the time dependency and causality between risks. This may lead to inappropriate risk assessments and reduced efficacy in risk treatments. ...

• Dynamic Bayesian networks reveal trajectories to COVID-19 outcomes.
• We obtain conditional probability maps over time and visualise trajectories.
• Trajectories visualised via resampling, dynamic time warping, and prototyping.
• ...

COVID-19 severity spans an entire clinical spectrum from asymptomatic to fatal. Most patients who require in-hospital care are admitted to non-intensive wards, but their clinical conditions can deteriorate suddenly and ...

• A calculation method of the matching degree between the vehicle and cargo is proposed.
• The matching combination resulted from the environment influence is analysed.
• It is shown that the matching result of the current time slice is ...

The problem of vehicle-cargo matching is a key issue in highway freight logistics transportation, and many investigations have been achieved for this problem. However, current research is limited to ideal environment, small dataset, static ...

Railway emergencies have the characteristics of unobvious precursors and complex secondary derivatives, which is difficult for decision-makers to make effective emergency response solutions. This paper develops a scenario-response ...

This study was aimed to discuss the predictive value of infectious disease dynamics model (IDD model) and dynamic Bayesian network (DBN) for scenario deduction of public health emergencies (PHEs). Based on the evolution law of PHEs and ...

• A scenario deduction model of DBN was established based on evolution law.
• An ...

Causality study investigates cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-driven ...

The time-hopping direct sequence spread spectrum (TH-DSSS) signal has been widely used in Pseudolites Positioning Systems to overcome the near-far problem. To capture the TH-DSSS signal, an additional parameter representing the time-hopping (TH) ...

In order to improve the accuracy of the state prediction model, a dynamic Bayesian network state prediction model based on the relationship of prediction variables is designed. The prediction model of dynamic Bayesian network structure learning ...

Considerable attention has been paid to content adaptation in ITS. However, process-oriented adaptation has been neglected and none of ITS addressed the correlation between the learning and the teaching process. Indeed, uncertainty coming ...

This work deals with robust dynamic probabilistic modeling and fault classification for process data. In dynamic processes, observed variables can be numerous in amount and correlated with each other in both variable-wise and time-...