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- Automatic tunning of Okumura-Hata model on railway communicationsPublication . Beire, Ana Rita; Cota, Nuno; Pinheiro Pita, Helder Jorge; Rodrigues, AntónioThis paper presents the Genetic Algorithms (GA) as an efficient solution for the Okumura-Hata prediction model tuning on railways communications. A method for modelling the propagation model tuning parameters was presented. The algorithm tuning and validation were based on real networks measurements carried out on four different propagation scenarios and several performance indicators were used. It was shown that the proposed GA is able to produce significant improvements over the original model. The algorithm developed is currently been used on real GSM-R network planning process for an enhanced resources usage.
- Temporal patterns of TV watching for Portuguese viewersPublication . Datia, Nuno; Moura-Pires, J.; Cardoso, A.; Pinheiro Pita, Helder JorgeAudiometer systems provide enormous amounts of detailed TV watching data. Several relevant and interdependent factors may influence TV viewers' behavior. In this work we focus on the time factor and derive Temporal Patterns of TV watching, based on panel data. Clustering base attributes are originated from 1440 binary minute-related attributes, capturing the TV watching status (watch/not watch). Since there are around 2500 panel viewers a data reduction procedure is first performed. K-Means algorithm is used to obtain daily clusters of viewers. Weekly patterns are then derived which rely on daily patterns. The obtained solutions are tested for consistency and stability. Temporal TV watching patterns provide new insights concerning Portuguese TV viewers' behavior.
- Optimizing propagation models on railway communications using genetic algorithmsPublication . Beire, Ana Rita; Pinheiro Pita, Helder Jorge; Cota, NunoAlthough the Okumura-Hata prediction model has been a widely used model to estimate radio network coverage, its application in railways environment requires calibration. The objective of this work is to present Genetic Algorithms as a solution in optimizing propagation models, proving that it can be used for optimizing the Okumura-Hata model on railway communications in order to improve its prediction of radio coverage. Several tests were carried out using different conditions allowing to establish the conditions that maximize the gain of the algorithm for this particular problem. The algorithm was applied to training samples and the resulting parameters were applied to different scenarios, showing improvements in the prediction results.
- Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz LogicsPublication . Leandro, Carlos; Pinheiro Pita, Helder Jorge; Monteiro, LuísThis work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.