Abstract
In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
Forest fires can have devastating consequences if not detected and fought before they spread. This paper presents an automatic fire detection system designed to identify forest fires, preferably, in their early stages. The system pipeline processes images of the forest environment and is able to detect the presence of smoke or flames. Additionally, the system is able to produce an estimation of the area under ignition so that its size can be evaluated. In the process of classification of a fire image, one Deep Convolutional Neural Network was used to extract, from the images, the descriptors which are then applied to a Logistic Regression classifier. At a later stage of the pipeline, image analysis and processing techniques at color level were applied to assess the area under ignition. In order to better understand the influence of specific image features in the classification task, the organized dataset, composed by 882 images, was associated with relevant image metadata (eg presence of flames, smoke, fog, clouds, human elements). In the tests, the system obtained a classification accuracy of 94.1% in 695 images of daytime scenarios and 94.8% in 187 images of nighttime scenarios. It presents good accuracy in estimating the flame area when compared with other approaches in the literature, substantially reducing the number of false positives and nearly keeping the same false negatives stats.

Abstract
Aerospace structures are often submitted to air-load tests to check possible unstable structural modes that lead to failure. These tests induce structural oscillations stimulating the system with different wind velocities, known as flutter test.An alternative is assessing critical operating regimes through simulations. Although cheaper, modelbased flutter tests rely on an accurate simulation model of the structure under investigation. This chapter addresses the data-driven flutter modeling using state-space linear parameter varying (LPV) models. The estimation algorithm employs support vector machines to represent the functional dependence between the model coefficients and the scheduling signal, which values can be used to account for different operating conditions. Besides versatile, that model structure allows the formalization of the estimation task as a linear least-squares problem. The proposed method also exploits the ensemble concept, which consists of estimating multiple models from different data partitions. These models are merged into a final one, according to their ability to reproduce a validation data segment.A case study based on real data shows that this approach resulted in a more accurate model for the available data. The local stability of the identified LPV model is also investigated to provide insights about critical operating ranges as a function of the magnitude of the input and output signals. © The Institution of Engineering and Technology 2019.

Abstract
PV-based renewable energy systems are integrated in microgrids (MGs) throughout power electronic converters. During external disturbances of abrupt load variation and PV generation fluctuation, controllers play the most key role in regulating the system performance. In this paper, a proposed combination control method of model predictive control (MPC) and sliding mode control (SMC) for moderating the power converters is presented. The interlinking inverter is controlled via applying MPC while DC-DC converter is controlled by SMC. MPC is well known as most reliable and modern approach for non-linear systems to compensate noise and handle modeling inaccuracies for efficient load voltage profile. On the other hand, SMC is considered as the most stable controller with highly robust operation for DC-DC converters. Intermittency nature of renewable energy systems and abrupt load variation have a severe impact on load voltage quality. Keeping in view the sophisticated control attributes of MPC and SMC, inner and outer control loops with primary droop control are designed for interlinking converter by using MPC while SMC is designed for the DC-DC boost converter. Controller performance is analyzed through simulations with fluctuating generation, and variable loads. Unlike conventional cascaded PI controller, proposed MPC strategy is simple, robust with fast dynamic response.

Abstract
Purpose Collaborative consumption emerges from social practices such as sharing, lending and gifting. It is becoming more common among consumers, boosted by the internet, which facilitates the collaboration process with both strong and weak ties. This paper aims to examine collaborative consumer experience, delving into the factors that contribute to the adoption and the perceived benefits of this alternative form of consumption. Design/methodology/approach A total of 12 phenomenological interviews were conducted o explore the theme from an individual perspective, attested by the consumers' narratives and experiences. Findings The results highlight collaborative consumption as being influenced by family practices, social relations and the current economic scenario. Also, noteworthy is the evidence that collaborative consumption enables consumers to select from a more diversified portfolio of products and services, especially the ones featured by the internet and social media. Consumers perceive financial, emotional, social, environmental and increased consumption benefits, depending on their practices of collaborative consumption, and also on their role as providers, consumers or exchangers. Originality/value Through the phenomenological approach, based on individual reports of experiences related to collaborative consumption, it was possible to highlight some aspects relevant to better understanding the behavior of collaborative consumers.