9,698 research outputs found
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
Nature-Inspired Adaptive Architecture for Soft Sensor Modelling
This paper gives a general overview of the challenges present in the research field of Soft Sensor
building and proposes a novel architecture for building of Soft Sensors, which copes with the identified challenges. The
architecture is inspired and making use of nature-related techniques for computational intelligence. Another aspect,
which is addressed by the proposed architecture, are the identified characteristics of the process industry data. The data
recorded in the process industry consist usually of certain amount of missing values or sample exceeding meaningful
values of the measurements, called data outliers. Other process industry data properties causing problems for the
modelling are the collinearity of the data, drifting data and the different sampling rates of the particular hardware
sensors. It is these characteristics which are the source of the need for an adaptive behaviour of Soft Sensors. The
architecture reflects this need and provides mechanisms for the adaptation and evolution of the Soft Sensor at different
levels. The adaptation capabilities are provided by maintaining a variety of rather simple models. These particular
models, called paths in terms of the architecture, can for example focus on different partition of the input data space, or
provide different adaptation speeds to changes in the data. The actual modelling techniques involved into the
architecture are data-driven computational learning approaches like artificial neural networks, principal component
regression, etc
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Data mining as a tool for environmental scientists
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
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