Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control)
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Inductive transfer for learning Bayesian networks
Machine Learning
Evaluating probabilistic models learned from data
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
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Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.