Abstract

Abstract

Abstract
This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in Sao Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

Abstract

Abstract
The ever-growing nature of user generated data in online systems poses obvious challenges on how we process such data. Typically, this issue is regarded as a scalability problem and has been mainly addressed with distributed algorithms able to train on massive amounts of data in short time windows. However, data is inevitably adding up at high speeds. Eventually one needs to discard or archive some of it. Moreover, the dynamic nature of data in user modeling and recommender systems, such as change of user preferences, and the continuous introduction of new users and items make it increasingly difficult to maintain up-to-date, accurate recommendation models. The objective of this workshop is to bring together researchers and practitioners interested in incremental and adaptive approaches to stream-based user modeling, recommendation and personalization, including algorithms, evaluation issues, incremental content and context mining, privacy and transparency, temporal recommendation or software frameworks for continuous learning.

Abstract
In this paper a real-time laboratorial experiment is presented, intended to validate a 'grey-box' equivalent model for medium voltage active distribution networks with high presence of converter-connected generation, considering the latest European grid codes requirements, in response to severe faults at the transmission network side. A hybrid setup was implemented at INESC TEC's laboratory (Porto, Portugal), relying on a real-time digital simulator to provide the interface between simulation and physical assets available at the laboratory, in a power-hardware-in-the-loop configuration. The study considered the laboratory's internal network to be operating (virtually) as a medium voltage distribution network with converter-connected generation (fault ride through compliant), connected to a fully-detailed transmission network model. The aggregated reactive power response of the laboratory's network was fitted by the dynamic equivalent model, recurring to an evolutionary particle swarm optimization algorithm. The methodology adopted, testing conditions and respective results are presented. © 2019 IEEE.