Spatio-Temporal Gaze Regularity Guided Dynamic Fuzzy Bayesian Network for Intention Inference

The supply chain is the central organizing unit in today's global industries and has gained significance as one of the 21st century manufacturing paradigms for improving organizational competitiveness. In this paper, we propose a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology, a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation.

A Hybrid Modelling Framework for Ecosystem-Based Climate Change Adaptation Using System Dynamics and Bayesian Networks

In order to find out the problems of students' learning in the online learning process in time and ensure that students can successfully complete the online learning course, the dynamic Bayesian network is used to predict students' online learning scores. First, the online course Bayesian network is established, and then the dynamic Bayesian network is established by combining the Bayesian network and hidden Markov according to the time slice. The maximum expectation algorithm is used for Bayesian parameter learning, and the scores are predicted according to video learning, chapter test and other influencing factors. The results show that the dynamic Bayesian network can effectively predict the online learning results, the prediction results provide teachers with decision-making, and students can carry out targeted reinforcement learning according to the prediction results in the learning process.

Nowadays, several components and systems are designed and produced based on reliability. Since the reliability criterion has an important role in purchasing and implementation of these systems. In the design of a reliable system, fault and failure analysis must be carried out in order to reduce fault probability of the system. When dependency and the relation between components of a complex system are important and should be mentioned, determination of system reliability is very difficult. In this paper, dynamic fault tree is used to evaluate the systems reliability that their behavior is varied with time. Dynamic fault tree is constructed and then it converted to dynamic bayesian network. In this paper, the principle of dynamic fault tree gates and their mapping into dynamic bayesian are explained and some new relations between events and gates for this mapping are proposed. GeNIe package is used to determine dynamic bayesian network based on stochastic sampling algorithms. Four systems (cardiac assist system, hypothetical cascaded priority-and system, inertial navigation system/ global positioning system integrated, and emergency detection system) are investigated; reliability and fault probability of these systems are calculated. Comparison of the results with those obtained by other researches shows the proposed method effectiveness for systems reliability modeling and assessment via dynamic bayesian network.

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

Bayesian network (BN) inference is an important statistical tool with additive symmetry. However, BN inference cannot deal with the uncertain, fuzzy, random, and conflicting information from experts’ knowledge in the process of conducting a risk assessment. To tackle this issue, a new fuzzy BN inference approach for risk assessments was proposed based on cloud model (CM), interval type-2 fuzzy set (IT2 FS), interval type-2 fuzzy logic system (IT2 FLS), modified Dempster–Shafer (D-S) evidence theory (ET), and Latin hypercube sampling (LHS) methods along the following lines. Firstly, CM was integrated into IT2 FS, and CM-based IT2 FS (CM-IT2 FS) was defined in the IT2 FLS. Secondly, modified D-S ET was utilized to determine the CM-IT2 FS-based a priori probabilities, and the CM-IT2 FS-based BN model was established. Thirdly, the CM-IT2 FS-based a priori probabilities were reduced to the CM-IT1 FS-based ones using a type reducer in the IT2 FLS, LHS was applied to propose a new fuzzy BN inference algorithm, and then, the new algorithm was used in a typical case to perform the fuzzy BN positive inference for risk prediction and the fuzzy BN reverse inference for risk sensitivity analysis. Finally, the BN inference results were analyzed using the proposed algorithm and the two common BN inference algorithms, and the effectiveness of the proposed approach was validated. It can be concluded that the proposed approach was both accurate and promising.

Fuzzy Bayesian networks (FBNs) are the variant of standard/classical Bayesian networks (BNs), which have intrinsic capability of handling ambiguity due to lack of expert knowledge and eventually reduces the epistemic uncertainty when used as computational models. Of late, FBNs have gained substantial research interest to be applied for time series prediction in both non-spatial and spatial domains . This chapter discusses a number of fuzzy BN models that have recently been proposed in literature. The central attention is paid on how the discrete Bayesian analysis in the previously discussed enhanced BN models can be further improved through incorporated fuzziness so as to make them more realistic for dealing with various contexts of spatial time series prediction. A comparative study, at the end of the chapter, demonstrates superiority of the fuzzified enhanced BN models, compared to those having no incorporated fuzziness.

Coordinately embedding machine learning and optimization in the Monte Carlo simulation is proposed as a new framework for reliability evaluation, which is becoming more challenging due to the joining of variability and intermittency of the renewable generation and components’ ageing. As a machine learning method, the dynamic Bayesian belief network predicts the renewable outputs by generating their probability distributions through historical data to overcome the defect of rarely grasping the low-probability events in the traditional methods, which either predict a single-point value or presume a parametric probability distribution. As the internal operation module of the framework, the rolling-horizon unit commitment maintains the complexity of the operation model and meantime punctually updates the predicted renewable generation and the components’ ageing. The dynamic Bayesian belief network and the rolling-horizon unit commitment traverse time step after time step throughout the horizon, and thus form one bid of the Monte Carlo simulation. In each time step, the dynamic Bayesian belief network extracts the probability distributions of the renewable generation, from which the boundary of the commitment is sampled, and the impact of scheduling the generators on their ageing is then accumulated from the commitment. The proposed framework has its effectiveness proved on a microgrid.

To take full advantage of the historical information, we adopt dynamic Bayesian networks and discrete-time Bayesian networks to describe the event trees or dynamic fault trees. On this basis of the regulation, corresponding dynamic Bayesian networks and discrete-time Bayesian networks models are established for safety assessment in timing system. When the discrete-time time Bayesian network of dynamic fault trees is acquired, the Bayesian network is used to study the consequences probability, key degree and computation method. A simulated banking transaction system is analyzed and assessed by above models. The results show that the safety assessment scheme can provide model without subjectivity, which raises the credibility of model. In addition, it can obtain general assessment results and acquire more extensive diagnostics and information.

Unmanned Combat Aerial Vehicle (UCAV) is an inevitable trend of the future intelligent and unmanned flight platform. Situation assessment is an effective method to solve the battlefield autonomous decision-making problem. The process of situation assessment and the concept and inference of static Bayesian networks are introduced. Then a Bayesian blackboard method with knowledge sources modify Bayesian networks is analyzed. Structure-variable dynamic Bayesian networks is put forward to deal with situation changes. Finally, the hybrid Bayesian blackboard and structure-variable dynamic Bayesian network is proposed for the highly dynamic air combat situation assessment and the model is proposed. The simulation results show that the inference results are preferable and consistent with the analysis.

Bayesian network based dynamic operational risk assessment

Bayesian network models provide an attractive framework for multimodal sensor fusion. They combine an intuitive graphical representation with efficient algorithms for inference and learning. However, the unsupervised nature of standard parameter learning algorithms for Bayesian networks can lead to poor performance in classification tasks. We have developed a supervised learning framework for Bayesian networks, which is based on the Adaboost algorithm of Schapire and Freund. Our framework covers static and dynamic Bayesian networks with both discrete and continuous states. We have tested our framework in the context of a novel multimodal HCI application: a speech-based command and control interface for a Smart Kiosk. We provide experimental evidence for the utility of our boosted learning approach.

Although n-gram models are still the de facto standard in language modeling for speech recognition, it has been shown that more sophisticated models achieve better accuracy by taking additional information, such as syntactic rules, semantic relations or domain knowledge into account. Unfortunately, most of the effort in developing such models goes into the implementation of handcrafted inference routines. What lacks is a generic mechanism to introduce background knowledge into a language model. We propose the use of dynamic Bayesian networks for this purpose. Dynamic Bayesian networks can be seen as a generalization of the n-gram models and hmms traditionally used in language modeling and speech recognition. Whereas those models use a single random variable to represent state, Bayesian networks can have any number of variables. As such they are particularly well-suited for the construction of models that take additional information into account. In this paper language modeling with belief networks is discussed. Examples of belief network implementations of well-known language models are given and a new model is presented that models dependencies between the content words in a sentence.KeywordsBayesian NetworkSpeech RecognitionLanguage ModelContent WordDynamic Bayesian NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In order to reduce the risks caused by congestion to scenic spot management and tourist safety, a dynamic Bayesian network model based on K-means++ clustering is proposed to realize the prediction of tourist transfer volume between scenic spots. Firstly, the K-means++ method is used to cluster the tourist transfer volume between scenic spots, we select the best number of clustering by the elbow rule, and the grade interval is determined by clustering results. Secondly, we consider the passenger transfer volume and tourist flow as the nodes of the dynamic Bayesian network, which can estimate the probability of tourist transfer from the upstream scenic spots to the target scenic spot, and the tourist volume of the target scenic spot is predicted. Finally, the confusion matrix is used to verify the validity of the proposed model. The case study shows: 1.) The prediction accuracy of the model can reach about 96%, which indicates that the model is suitable for tourist flow prediction. 2.) Compared to ARIMA, SVR, K-means + BN, and K-means + DBN, the proposed model has better prediction accuracy. 3.) The Bayesian network model outperforms deep learning models in interpretability.

Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S.Cerevisiae cell cycle gene expression data. The experimental results demonstrate that the accuracy of our method outperforms the previous work