Confidence estimation of feedback information using dynamic bayesian networks

Confidence estimation of feedback information for logicdiagnosis

An engineering change (EC) is an alteration made to a system that has been released following a system design process. EC propagation is a series of ECs occurring due to dependencies among components of a product. ECs can consume up to 50% of the overall engineering efforts during the development of a complex system. Therefore, EC propagation prediction received considerable attention in past decades as the product development industries started to suffer from the negative impacts of change propagation. This paper evaluates the current approaches to EC propagation prediction and presents a dynamic Bayesian networks (DBNs) approach to estimate change propagation risk (CPR) as well as a novel approach to estimate EC durations. Literature research shows that although some studies have used design structure matrices to estimate CPR and the total redesign duration (TRD) due to change propagation, an approach that allows iteration while accounting for the conjunction of all impacts has not been explored. This paper aims to fill the gaps for calculating CPR using DBN and evaluating change propagation paths from a Split-and task outcome logic, which accounts for the conjunction of all component relationships. This paper compares the proposed method results with the existing CPR and engineering change duration estimation methods using a real-world dataset from a U.S. Navy shipbuilding program. The results indicate that the CPR can be calculated using the proposed method without the shortcomings of the existing method and the accuracy for estimating engineering change durations is increased.

Long-term projections for commodity prices are a key challenge in science as well as in business environment. This paper proposes a new mathematical approach for future projections of prices for time horizons larger than 10 years using a Dynamic Bayesian Network (DBN). The DBN approach is verified at the crude oil price example.

This research underscores the critical role of seagrass as a primary producer, essential for biodiversity and vulnerable to climate change, particularly due to increased marine heatwaves. Utilizing a Dynamic Bayesian Network (DBN) approach, this study models the dynamic processes affecting seagrass meadows and provides a framework for adapting DBNs to diverse ecological settings, enhancing model reuse. Key contributions include a Monte Carlo method for integrating climate projections with DBNs to predict long-term seagrass health. The studies culminate in a toolset that facilitates effective visualization, analysis, and management of seagrass ecosystem resilience under varying climate scenarios, advancing ecological modelling and management.

A Reinforcement Learning Variable Neighborhood Search for the Robust Dynamic Bayesian Network Optimization Problem under the Supply Chain Ripple Effect

In this article, we develop and evaluate three Dynamic Bayesian Network (DBN) models for assessing temporally variable learner scientific inquiry skills (Hypothesis Generation and Variable Identification) in INQPRO learning environment. Empirical studies were carried out to examine the matching accuracies and identify the models’ drawbacks. We demonstrate how the insights gained from a preceding model have eventually led to the improvement of subsequent models. In this study, the entire evaluation process involved 6 domain experts and 61 human learners. The matching accuracies of the models are measured by (1) comparing with the results gathered from the pretest, posttest, and learner’s self-rating scores; and (2) comments given by domain experts based on learners’ interaction logs and the graph patterns exhibited by the models.KeywordsScientific Inquiry SkillsDynamic Bayesian Networks

Manufacturing equipments should be integrated to construct CIM (Computer Integrated Manufacturing) environment, but several economical and technological difficulties may be arisen in case heterogeneous systems are integrated into each other. MMS (Manufacturing Message Specification) can be effective in such a case, but the problem is that MMS-related products are comparatively expensive and the existing manufacturing equipments mostly do not support MMS. This study was focused on developing a gateway suitable to a non-MMS-compatible CNC machine-tool, and on implementing OSI (Open System Interconnection) upper layer on TCP/IP. The development system was applied to a cell controller by means of heterogeneous equipments under CIM environment, and to evaluate interoperability and portability.

On the formal specification and verification of CIM architectures using LOTOS

Dynamic Bayesian network (DBN) theory provides a valid tool to estimate the risk of disruptions, propagating along the supply chain (SC), i.e. the ripple effect. However, in cases of data scarcity, obtaining perfect information on probability distributions required by the DBN is impractical. To overcome this difficulty, a new robust DBN approach is, for the first time, proposed in this study to analyse the worst-case oriented disruption propagation in the SC. This work considers an SC with multiple suppliers and one manufacturer over several time periods, in which only probability intervals of the suppliers' states and those of the related disruption propagations are known. The objective is to acquire the robust performance of risk estimation, measured by the worst-case probability in the disrupted state for the manufacturer. We first establish a nonlinear programming formulation to mathematically materialise the proposed robust DBN, which can be used to solve small-size problems. To overcome the computational difficulty in solving large-size problems, an efficient simulated annealing algorithm is further designed. Numerical experiments are conducted to validate its efficiency.

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.

The economic justification of the sequential adoption of a new manufacturing system

Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach

Computational Intelligence methods have been applied to the automatic discovery of predictive models for the diagnosis of Hepatocellular Carcinoma (a.k.a liver cancer). Evolutionary algorithms have lent themselves as efficient and robust methods for evolving best parameter values that optimize feature selection methods. Different computational methods for discovering more robust set of molecular features for liver cancer have been proposed. These include methods combining other nature-inspired evolutionary algorithms such as Particle Swarm Optimization, with classifiers like Support Vector Machine (SVM). In this paper, we apply different variants of Differential Evolution algorithm to optimize the parameters of feature selection algorithms using a proposed two-stage approach. Stage one fine-tunes the parameters of the feature selection methods and selects high quality features. In stage two, Dynamic Bayesian Network (DBN) is applied to infer temporal relationships of the selected features. We demonstrate our method using gene expression profiles of liver cancer patients. The results show that the SVM-based predictive model with the radial basis function kernel yielded a predictive accuracy of 100%. This model and a sub-set of the features consist of only 8 features (genes) that have been regarded as most informative set for the diagnosis of the disease. In addition, among all these eight genes, the DBN model of the selected features reveals that SPINT2 gene inhibits HGF activator which prevents the formation of active hepatocytes growth factor, which makes up over 80% of liver cells.

Dynamic Bayesian Network Approach to improve the Preventive Maintenance of Multi-State Systems

Summary form only given. Productive and cost effective use of semiconductor manufacturing equipment is a primary concern in semiconductor fabs today. As equipment costs continue to escalate, semiconductor manufacturers are focusing their efforts on Computer Integrated Manufacturing (CIM) to bolster productivity, improve yields, and reduce the cost of ownership. Equipment Integration can play a key role in these sophisticated CIM systems by bridging the gap between factory computer systems and process equipment. To implement efficient and effective equipment integration, the semiconductor manufacturer must overcome the following obstacles: high implementation costs; difficult technical implementation; long development cycles; finite resources; limited operational functionality. Specifically, the equipment's communication interface (CI) contributes to those obstacles through: Limited communication interface functionality; Unique equipment communication interfaces; Unpredictable equipment communication interfaces; Poor reuse of integration practices and software from factory to factory. Motorola is addressing these obstacles by implementing a program to improve equipment communication interface consistency, predictability, and functionality. To improve equipment communication predictability, Motorola utilizes improved CI characterization tools techniques that thoroughly exercise the equipment's CI. To improve interface consistency and functionality, Motorola is establishing partnerships with equipment vendors and characterization software suppliers. These partnerships provide the foundation for improved CI quality and adherence to SEMI standards before a product is shipped to Motorola. Mr. Guy Davis will share his perspective on how standards, procedures, and software tools can improve the quality and reduce the cost and cycletime involved in equipment integration. He will discuss the benefits realized when software suppliers, system integrators, equipment providers, and semiconductor manufacturers are working together.