Artículos

In this paper we present a method to detect edges in images. The method consists of using a 3x3 pixel mask to scan the image, moving it from left to right and from top to bottom, one pixel at a time. Each time it is placed on the image, an agglomerative hierarchical cluster analysis is applied to the eight outer pixels. When there is more than one cluster, it means that window is on an edge, and the central pixel is marked as an edge point. After scanning all the image, we obtain a new image showing the marked pixels around the existing edges of the image. Then a thinning algorithm is applied so that the edges are well defined. The method results to be particularly efficient when the image is contaminated. In those cases, a previous restoration method is applied.

We give a new and more manageable characterization for Cyclic Pólya Frequency functions of order 3 (CPF3). Our result also improves present knowledge concerning smoothness properties in CPF. In particular, a conjecture of Mairhuber, Schoenberg, and Williamson, On variation-diminishing transformations on the circle, Rend. Circ. Mat. Palermo (2) 8 (1959), 1-30, about discontinuous CPF functions is established.

Jointing Condition-Based Maintenance (CBM) with the Proportional Hazards Model (PHM), asset-intensive industries often monitor vital covariates to predict failure rate, the reliability function, and maintenance decisions. This analysis requires defining the transition probabilities of asset conditions evolving among states over time. When only one covariate is assessed, the model’s parameters are commonly obtained from expert opinions to provide state bands directly. However, the challenge lies within multiple covariate problems, where arbitrary judgment can be difficult and debatable, since the composite measurement does not represent any physical magnitude. In addition, selecting covariates lacks procedures to prioritize the most relevant ones. Therefore, the present work aimed to determine multiple covariate bands for the transition probability matrix via supervised classification and unsupervised clustering. We used Machine Learning (ML) to strengthen the PHM model and to complement expert knowledge. This paper allows obtaining the number of covariate bands and the optimal limits of each one when dealing with predictive maintenance decisions. This novel proposal of an ML condition assessment is a robust alternative to the expert criterion to provide accurate results, increasing the expectation of the remaining useful life for critical assets. Finally, this research has built an enriched bridge between the decision areas of predictive maintenance and Data Science.

The design of a neural network is presented for the recognition of six kinds of minerals (chalcopyrite, chalcosine, covelline, bornite, pyrite, and energite) and to determine the percentage of these minerals from a digitized image of a rock sample. The input to the neural network corresponds to the histogram of the region of interest selected by the user from the image that it is desired to recognize, which is processed by the neural network, identifying one of the six minerals learned. The network’s training process took place with 160 regions of interest selected from digitized photographs of mineral samples. The recognition of the different types of minerals in the samples was tested with 240 photographs that were not used in the network’s training. The results showed that 97% of the images used to train the network were recognized correctly in the percentage mode. Of the new images, the network was capable of recognizing correctly 91% of the samples.

The control law of electronically-interfaced distributed energy resources (DERs) must be able to maintain the stability and voltage regulation of the host microgrid in the two modes of operation. Ideally, this should be achieved by a decentralized primary control strategy that is independent of any communication infrastructure in order to increase the resilience of the microgrid. This is challenging as the primary control objectives in islanded and grid-connected modes of operation are conflicting. This paper proposes a decentralized control law for DER units based on state feedback and disturbance rejection. The controller provides an integral action which enables output current reference tracking. An ad-hoc partial input saturation technique is also proposed in order to prevent the integral action from having an adverse impact on the voltage amplitude and frequency regulation in the islanded mode of operation. The effectiveness of the proposed control strategy is demonstrated via a time-domain simulation of a medium-voltage distribution network with three embedded DER units, as well as through an experimental three-bus microgrid with two DER units. The results demonstrate the robustness of the proposed control strategy to transitions between the modes of operation and other network topological changes.