Hector Alaiz-Moreton

Modeling of Bicomponent Mixing System Used in the Manufacture of Wind Generator Blades

The clean energy use has increased during the last years, especially, electricity generation through wind energy. Wind generator blades are usually made by bicomponent mixing machines. With the aim to predict the behavior of this type of manufacturing systems, it has been developed a model that allows to know the performance of a real bicomponent mixing equipment. The novel approach has been obtained by using clustering combined with regression techniques with a dataset obtained during the system operation. Finally, the created model has been tested with very satisfactory results.

Coupling the PAELLA Algorithm to Predictive Models.

This paper explores the benefit of using the PAELLA algorithm in an innovative way. The PAELLA algorithm was originally developed in the context of outlier detection and data cleaning. As a consequence, it is usually seen as a discriminant tool that categorizes observations into two groups: core observations and outliers. A new look at the information contained in its output provides ample opportunity in the context of data driven predictive models. The information contained in the occurrence vector is used through the experiments reported in a quest for finding how to take advantage of that information. The results obtained in each successive experiment guide the researcher to a sensible use case in which this information proves extremely useful: probabilistic sampling regression.

Conceptual Model Development for a Knowledge Base of PID Controllers Tuning in Open Loop

Regardless of this increasing rhythm of discovery of different possibilities, it has been impossible at this moment to oust relatively popular techniques, as can be the ’traditional’ PID control. Since the discovery of this type of regulators by Nicholas Minorsky Mindell (2004) Bennett (1984) in 1922 to this day, many works have been carried out about this controller. In this period of time there was an initial stage, in which the resolution of the problem was done analogically and in it the advances were not as remarkable as have been since the introduction of the computer, which allows to implement the known structure of direct digital control Auslander et al. (1978), illustrated in figure 1.

Development of a conceptual model for a knowledge-based system for the design of closed-loop PID controllers

This paper describes the methodology used in the development of a ruled-based conceptual model for a knowledge based system aimed at the designing of closed-loop or feedback PID (proportional, integral, derivative) controllers. The paper shows the organization of the existing rules and an explanation about a new way of obtaining specific rules for discriminating between different methods of optimizing the parameters of the controller, by using an automatic classification of a huge set of data obtained as the result of applying these methods to an extended collection of representative systems.

Sensor Fault Detection and Recovery Methodology for a Geothermal Heat Exchanger

This research addresses a sensor fault detection and recovery methodology oriented to a real system as can be a geothermal heat exchanger installed as part of the heat pump installation at a bioclimatic house. The main aim is to stablish the procedure to detect the anomaly over a sensor and recover the value when it occurs. Therefore, some experiments applying a Multi-layer Perceptron (MLP) regressor, as modelling technique, have been made with satisfactory results in general terms. The correct election of the input variables is critical to get a robust model, specially, those features based on the sensor values on the previous state.

PAELLA as a Booster in Weighted Regression

This paper reports the use of the PAELLA algorithm in the context of weighted regression. First, an experiment comparing this new approach versus probabilistic macro sampling is reported, as a natural extension of previous work. Then another different experiment is reported where this approach is tested against a state of the art regression technique. Both experiments provide satisfactory results.

Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data

Public health in developed countries is heavily affected by pollution specially in highly populated areas. Amongst the pollutants with greatest impact in health, ozone is particularly addressed in this paper due to importance of its effect on cardiovascular and respiratory problems and their prevalence on developed societies. Local authorities are compelled to provide satisfactory predictions of ozone levels and thus the need of proper estimation tools rises. A data driven approach to prediction demands high quality data but those observations collected by weather stations usually fail to meet this requirement. This paper reports a new approach to robust ozone levels prediction by using an outlier detection technique in an innovative way. The aim is to assess the feasibility of using raw data without preprocessing in order to obtain similar or better results than with traditional outlier removal techniques. An experimental dataset from a location in Spain, Ponferrada, is used through an experimental stage in which such approach provides satisfactory results in a difficult case.

Techniques and Utilities to Improve the Design, Development and Debugging of Multiagent Applications with Agile Principles

Construction and use of software agents for industrial and business computer systems is a well-known subject for professional development teams. But the full potential of Agent-Oriented programming usually remains hidden to these groups and agents are usually not exploited to their full potential. This study shows the description and implementation of a number of techniques created to face the construction of agents composed of a number of behaviors that may be debugged and revised in a step-by-step fashion, both at design and at runtime. The set of techniques also include a mechanism for managing the orchestration of behaviors, what gives the development teams insight on the final behavior resulting from the combination of the individual ones. These techniques allow the application of agile software development principles to multiagent environments, making it easier to integrate these kind of software artifacts into complex projects using this paradigm. The paper also describes two practical sample use cases where these techniques are employed: the application of the techniques to the simple JADE DummyAgent and to complex rule-based agents that run behaviors based on CLIPS or JESS rules.

PID-ITS: An Intelligent Tutoring System for PID Tuning Learning Process

A new developed tool for PID learning is described on this work. The main contribution is the possibility to assist non-experimented users on the PID tuning task. For its implementation, knowledge engineering was used by the conceptual model creation and its next formalization on the described tool. Very good results have been achieved in general terms when it was validated with users without expertise on the control field. Both aims were achieved, the right learning of the traditional PID tuning by empirical methods and the assistance during tuning over systems.