Catálogo de publicaciones - libros

The shortage of human organs for transplantation is a serious problem, and is exacerbated by the fact that current organ selection and assignment processes discard a significant number of organs deemed non-viable (not suitable) for transplantation. However, these processes ignore the fact that medical specialists may disagree as to whether an organ is viable or not. Therefore, in this paper we propose a novel organ selection process in which transplant physicians, who may be geographically dispersed, deliberate over the viability of an organ. This argument-based deliberation is formalized in a multi-agent system – CARREL – that requires the deliberation to adhere to formal rigorous standards acknowledging the safety critical nature of the domain. We believe that this new selection process has the potential to increase the number of organs that current selection processes make available for transplantation, and thus reduce the increasing gap between the demand for and supply of human organs.

This paper presents an autonomous intelligent agent with a human thinking reasoning model, based on past experiences. The agent is developed to assist medical staff in geriatric residences. The health care process is a vital function, requiring nature-inspired solutions imitating the residence staff behaviours. An autonomous deliberative Case-Based Planner agent, AGALZ (Autonomous aGent for monitoring ALZheimer patients), is developed and integrated into an environment-aware multi-agent system, named ALZ-MAS (ALZheimer Multi-Agent System), to optimize health care in geriatric residences. ALZ-MAS is capable of obtaining information about the environment through RFID technology.

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation. This challenging operation is very important since it is directly linked with the produced optical component’s quality. In order to automate this repetitive and difficult task, microscopy based inspection system is aimed. After a defects detection phase, a classification phase is mandatory to complete optical devices diagnosis because a number of correctable defects are usually present beside the potential “abiding” ones. In this paper is proposed a processing sequence, which permits to extract pertinent low-dimensional defects features from raw microscopy issued image. The described approach is validated by studying MLP neural network based classification on real industrial data using obtained defects features.

Anthropocentrism of computational systems is totally justified when the task concerns to natural language. Computational linguistics systems use to rely on mathematical and statistical formalisms, which are efficient and useful but far from human procedures and therefore not so skilled. The presented work proposes a computational model of natural language reading, called Cognitive Reading Indexing Model (CRIM), inspired by some aspects of human cognition, trying to become as psychologically plausible as possible. The model relies on a semantic neural network and it produces not vectors but nets of activated concepts as text representations. The experimental evaluation shows that the system is suitable for real applications and also to model human reading, and it provides a framework to validate hypothesis from other Cognitive Science fields.

This paper presents an approach to extracting stock market trading rules from stock market data. Trading rules are based on two multi-layer perceptrons, one generating buy signals and one generating sell signals. Inputs of these perceptrons are fed with values of technical indicators computed on historical stock quotations. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of trading rules is reasonable and the trading rules are able to generate reasonable trading signals, not only over a training period, used in the training process, but also over a test period, unknown during constructing trading rules. Moreover, trading strategies defined by such trading rules are profitable and often outperform the simple Buy&Hold strategy.

In this paper, we describe two different hand image databases. One has been acquired in laboratory condition with a document scanner, and the other one in operational conditions using a webcam and an infrared filter. The experimental part describes some verification experiments and extracts relevant conclusions about image acquisition and biometric classification.

Particle Swarm Optimization (PSO) is a new nature-inspired evolutionary technique simulated with bird flocking and fish schooling. However, the biological model of standard PSO ignores the different decision process of each bird. In nature, if one bird finds some food, generally, it will continue to fly surrounding this spot to find other food, and vice versa. Inspired by this phenomenon, a new swarm intelligent methodology– perceptive particle swarm optimization is designed, in which each particle can apperceive its current status within the whole swarm, and make a dynamic decision by adjusting its next flying direction. Furthermore, a mutation operator is introduced to avoid unsuitable adjustment. Simulation results show the proposed algorithm is effective and efficiency.

This paper reviews one nonlinear and two linear projection architectures, in the context of a comparative study, which are used as either alternative or complementary tools in the identification and analysis of anomalous situations by Intrusion Detection Systems (IDSs). Three neural projection models are empirically compared, using real traffic data sets in an IDS framework. The specific multivariate data analysis techniques that drive these models are able to identify different factors or components by studying higher order statistics - variance and kurtosis - in order to display the most interesting projections or dimensions. Our research describes how a network manager is able to diagnose anomalous behaviour in data traffic through visual projection of network traffic. We also emphasize the importance of the time-dependent variable in the application of these projection methods.

Data incompleteness and data scarcity are common problems in agroecological modelling. Moreover, agroecological processes depend on historical data that could be fed into a model in a vast number of ways. This work shows a case study of modelling in agroecology using artificial neural networks. The variable to be modelled is sugar cane yield and for this purpose we used climate, soil, and other environmental variables. Regarding the data precision issue, we trained different neural models using monthly and weekly data in order to compare their performance. Furthermore, we studied the influence of using incomplete observations in the training process in order to include them and thus use a larger quantity of input patterns. Our results show that the gain in observations due to the inclusion of incomplete data is preferable in this application.

This paper shows a strategy based on simulated annealing for the optimal tuning of a PID controller to deal with time-varying delay. The main goal is to minimize the integral time absolute error (ITAE) performance index and the overshoot for a drilling-force control system. The proposed strategy is compared with other classic tuning rules (the Ziegler-Nichols and Cohen-Coon tuning formulas). Other tuning laws derived from genetic algorithms and the Simplex search algorithm for unconstrained optimization are also included in the comparative study. The results demonstrate that simulated annealing provides an optimal tuning of the PID controller, which means better transient response (less overshoot) and less ITAE than with other methods.