Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

This work presents an electricity consumption-forecasting framework configured automatically and based on an Adaptative Neural Network Inference System (ANFIS). This framework is aimed to be implemented in industrial plants, such as automotive factories, with the objective of giving support to an Intelligent Energy Management System (IEMS). The forecasting purpose is to support the decision-making (i.e. scheduling workdays, on-off production lines, shift power loads to avoid load peaks, etc.) to optimize and improve economical, environmental and electrical key performance indicators. The base structure algorithm, the ANFIS algorithm, was configured by means of a Multi Objective Genetic Algorithm (MOGA), with the aim of getting an automatic-configuration system modelling. This system was implemented in an independent section of an automotive factory, which was selected for the high randomness of its main loads. The time resolution for forecasting was the quarter hour. Under these challenging conditions, the autonomous configuration, system learning and prognosis were tested with success.     

A case based reasoning model for multilingual language generation in dialogues

The process of Natural Language Generation for a Conversational Agent translates some semantic language to its surface form expressed in natural language. In this paper, we are going to show a Case Based Reasoning technique which is easily extensible and adaptable to multiple domains and languages, that generates coherent phrases and produces a natural outcome in the context of a Conversational Agent that maintains a dialogue with the user.     

Brazilian articles in international journals on Limnology

Summary  We assessed the contribution of Brazilian limnologists (freshwater ecologists) in international journals in the period 1970-2004. Brazilian contribution was low and regular in the 1970's, but increased steeply after 1980 with no signs of stabilization until the present. Articles authored by Brazilians tend to be less cited than articles authored by non-Brazilians, although this difference is reduced in co-authored articles with international researchers.  Brazilian articles are not distributed homogenously among the sub-areas of Limnology, but present some biases that can be explained by intellectual legacy. Brazil has invested since the 1970's in establishing postgraduate courses in Brazil and in the last years has turned the focus to a better qualification of these courses. We believe these are the main reasons for the conspicuous development of Brazilian Limnology.     

A weight function methodology for the assessment of embedded and surface irregular plane cracks

A low cost numerical tool for the calculation of mode I stress intensity factors K in embedded and surface irregular cracks is presented in this paper. The proposed tool is an extension of the O-integral algorithm due to Oore and Burns for the assessment of embedded plane cracks using the weight function methodology. The performance of the O-integral is assessed first by comparing its K results to exact solutions for embedded elliptical and rectangular cracks. From the analysis of this data it is found that the error in the K results systematically depends on the crack aspect ratio and the local crack front curvature. Based on this evidence a corrective function is derived in order to remediate the limitations of the O-integral. Solutions due to Newman and Raju are used to account for the effects of free surfaces and finite thickness. The accuracy of the proposed procedure is assessed by solving a number of examples and by comparing the obtained results to those available in the literature.     

Influence diagnostics in regression with complex designs through conditional bias

One of the areas of Statistics in which the influence analysis has been widely studied is the multiple linear regression model. Nevertheless, the influence diagnostics proposed in this context cannot be applied to regression in complex survey, under randomized inference, since the i.i.d. case does not incorporate any probability weighting or population structure, such as clustering, stratification or measures of size into the analysis. In this paper we introduce some influence diagnostics in regression in complex survey. They are built on the conditional bias concept (Moreno-Rebollo et al., 1999). We emphasize the similarities and differences of the proposed measures with respect to the existing ones for the i.i.d. case. Research partly supported by the Spanish MCYT grant BFM2001-3844 and Spanish MEC grant MTM2004-0111433.     

Modeling and formal verification of embedded systems based on a Petri net representation

In this paper we concentrate on aspects related to modeling and formal verification of embedded systems. First, we define a formal model of computation for embedded systems based on Petri nets that can capture important features of such systems and allows their representation at different levels of granularity. Our modeling formalism has a well-defined semantics so that it supports a precise representation of the system, the use of formal methods to verify its correctness, and the automation of different tasks along the design process. Second, we propose an approach to the problem of formal verification of embedded systems represented in our modeling formalism. We make use of model checking to prove whether certain properties, expressed as temporal logic formulas, hold with respect to the system model. We introduce a systematic procedure to translate our model into timed automata so that it is possible to use available model checking tools. We propose two strategies for improving the verification efficiency, the first by applying correctness-preserving transformations and the second by exploring the degree of parallelism characteristic to the system. Some examples, including a realistic industrial case, demonstrate the efficiency of our approach on practical applications.     

On optimal pairwise linear classifiers for normal distributions: the d-dimensional case

We consider the well-studied pattern recognition problem of designing linear classifiers. When dealing with normally distributed classes, it is well known that the optimal Bayes classifier is linear only when the covariance matrices are equal. This was the only known condition for classifier linearity. In a previous work, we presented the theoretical framework for optimal pairwise linear classifiers for two-dimensional normally distributed random vectors. We derived the necessary and sufficient conditions that the distributions have to satisfy so as to yield the optimal linear classifier as a pair of straight lines.In this paper we extend the previous work to d-dimensional normally distributed random vectors. We provide the necessary and sufficient conditions needed so that the optimal Bayes classifier is a pair of hyperplanes. Various scenarios have been considered including one which resolves the multi-dimensional Minsky’s paradox for the perceptron. We have also provided some three-dimensional examples for all the cases, and tested the classification accuracy of the corresponding pairwise-linear classifier. In all the cases, these linear classifiers achieve very good performance. To demonstrate that the current pairwise-linear philosophy yields superior discriminants on real-life data, we have shown how linear classifiers determined using a maximum-likelihood estimate (MLE) applicable for this approach, yield better accuracy than the discriminants obtained by the traditional Fisher's classifier on a real-life data set. The multi-dimensional generalization of the MLE for these classifiers is currently being investigated.     

Interactive structural learning of Bayesian networks

We propose an hybrid approach for structure learning of Bayesian networks, in which a computer system and a human expert cooperate to search for the best structure. The system builds an initial tree structure which is graphically presented to the expert, and then the expert can modify this structure according to his knowledge of the domain. The system has several tools for aiding the human in this task: it allows for graphical editing (adding, deleting, inverting arcs) of the network, it shows graphically the correlation between variables, and it gives a measure of the quality and complexity for each structure. A measure which combines both quality and complexity, that we call quality, is defined. We have tested the tool in two domains: atmospheric pollution and car insurance, with good results.     

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.