Hold dig opdateret på de nyeste publikationer fra HUGIN EXPERT, 
som står bag HUGIN PredictIT. Her finder du vores projekter, cases og artikler: 

HUGIN SelSus Project

The role of HUGIN in SelSus is to deliver technology for decision support based on Bayesian networks and to take part in the development of a decision support system.

Projektperiode: 1. september 2013 til  31. august  2017.

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CISC is a Marie Curie Training Network funded by the European Commission to hire and train Early Stage Researchers (ESR) or PhD student as Collaborative Intelligence Scientists with the expertise and skillset necessary to carry-out the major tasks required to develop a Collaborative Intelligence system.

Projektperiode: 1. januar 2021 til 1. januar 2025.

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ABB Industrial IT Platform supports Bayesian Networks

The ABB Industrial IT platform is an automation and information platform that integrates diverse standardization of global processes and has a greater return on process assets. ABB is utilizing the advanced Hugin tool from Hugin Expert A/S for decision support. 

Publiceret: 2016

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Online Updating of Conditional Linear Gaussian Bayesian Networks

This paper presents a method for online updating of conditional distributions in Bayesian network models with both discrete and continuous variables. 

Publiceret: 2022

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Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

A Bayesian network was developed for predicting the acute toxicity intervals of chemical substances to fish, based on information on fish embryo toxicity (FET) in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of juvenile fish. 

Publiceret: 2020

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An empirical study of Bayesian network inference with simple propagation

We propose Simple Propagation (SP) as a new join tree propagation algorithm for exact inference in discrete Bayesian networks.

Publiceret: 2018

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A component framework as an enabler for industrial cyber physical systems

In this paper we show how Component Based Software Engineering (CBSE) concepts were applied to design the Sensor SelComp. Therefore, a component framework to address the abstraction of Component Level within the SelSus European Project ICPS.

Publiceret: 2018

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Parameter learning algorithms for continuous model improvement using operational data

In this paper, we consider the application of object-oriented Bayesian networks to failure diagnostics in manufacturing systems and continuous model improvement based on operational data. 

Publiceret: 2017

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Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level

To support health monitoring and life-long capability management for self-sustaining manufacturing systems, next generation machine components are expected to embed sensory capabilities combined with advanced ICT.

Publiceret: 2017

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A Methodology for Developing Local Smart Diagnostic Models Using Expert Knowledge

This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. 

Publiceret: 2015

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SelSus: Towards A Reference Architecture for Diagnostics and Predictive Maintenance Using Smart Manufacturing Devices

We propose a reference architecture, SelSus (SELf-SUStaining Manufacturing Systems) that aims to enable the provisioning of diagnostic and prognostic capabilities in manufacturing systems that utilize the notions of “smart” automation devices.

Publiceret: 2015

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Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes

The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required.

Publiceret: 19 juli 2005

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