Self-heating function of carbon nanofiber cement pastes

The viability of carbon nanofiber (CNF) composites in cement matrices as a self-heating material is reported in this paper. This functional application would allow the use of CNF cement composites as a heating element in buildings, or for deicing pavements of civil engineering transport infrastructures, such as highways or airport runways. Cement pastes with the addition of different CNF dosages (from 0 to 5% by cement mass) have been prepared. Afterwards, tests were run at different fixed voltages (50, 100 and 150V), and the temperature of the specimens was registered. Also the possibility of using a casting method like shotcrete, instead of just pouring the fresh mix into the mild (with no system’s efficiency loss expected) was studied. Temperatures up to 138 °C were registered during shotcrete-5% CNF cement paste tests (showing initial 10 °C/min heating rates). However a minimum voltage was required in order to achieve a proper system functioning.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.