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Item type:Publication, Dimensionality reduction via an orthogonal autoencoder approach for hyperspectral image classification(International Society for Photogrammetry and Remote Sensing, 2020-08-06)Abstract. Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the Pavia University, the Kennedy Space Center, and Botswana hyperspectral datasets evidence such representation power of our approach, leading to better classification performances compared to traditional hyperspectral dimensionality reduction algorithms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Urban road network resilience assessment on freight logistics by simulating disruptive events(Springer, 2022-01-01)The assessment of resilience in port road networks under disruptive events is a key issue related to urban logistics. This paper addresses an original simulation method to evaluate resilience using macro and micro simulation based on stochastic theory. The results provide insight into the resilience index of the network. This paper specifies the most influential network links around the area of influence produced by a logistics transport avenue in Lima-Peru. A function that includes redundancy and robustness of the system as a performance measure is proposed to measure resilience. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Near-optimal decentralized diagnosis via structural analysis(Institute of Electrical and Electronics Engineers Inc., 2022-12-01)Health monitoring of current complex systems significantly impacts the total cost of the system. Centralized fault diagnosis architectures are sometimes prohibitive for large-scale interconnected systems, such as distribution systems, telecommunication networks, water distribution networks, or fluid power systems. Confidentiality constraints are also an issue. This article presents a decentralized fault diagnosis method that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The method, implemented in the decentralized diagnoser design ($D^{3}$) algorithm, is based on structural analysis and can advantageously be applied to high-dimensional systems, linear or nonlinear. Using the concept of isolation on request, a hierarchy is built according to diagnostic objectives. The resulting diagnoser is based on analytical redundancy relations (ARRs) generated along the hierarchy. Their number is optimized via binary integer linear programming (BILP) while still guaranteeing maximal diagnosability at each level.$D^{3}$proves of lower time complexity than its centralized equivalent. It is successfully applied to a nonlinear combined cycle gas-turbine power plant.