Abstract
This paper proposes a stochastic decision making problem for a wind power producer (WPP) in the day-ahead (DA) and balancing markets. In this problem, bidding strategy of the WPP in a competitive electricity market and also its participation to supply demand response (DR) and electric vehicle (EV) aggregators is determined to achieve the maximum profit. In this model, DR and EV aggregators are able to choose the most competitive WPP in such a way that their energy payments be minimized in the scheduling horizon. Therefore, the problem is formulated as a stochastic bi-level programming model with conflict objectives of the WPP and the aggregators. Moreover, owing to the uncertainties associated with market prices, offered prices by rival WPPs, demand of DR and EV aggregators, conditional value at risk (CVaR) is applied to the proposed model. The attained stochastic bi-level problem is transformed to a linear stochastic single level problem with equilibrium constraints using Karush Kuhn Tucker (KKT) optimality conditions. The proposed model is evaluated on a realistic case study and the impacts of risk-averse behavior and demand response participants on the decision making problem of the WPP are investigated. Numerical results indicate that with increasing DR participants of 0%, 60% and 100%, CVaR of WPP increases 33.81%, 40.79% and 46.99%, respectively. This means that if the loads are more responsive, the WPP tries to control the profit variability due to the uncertainties of loads.

Abstract
In this article a new Data-Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor-limited AUVs, where typical scenarios often include non-negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data-Driven approach.

Abstract
The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.

Abstract
Nowadays, the power quality has become not only an important competitive factor for industrial users but also a crucial factor in the energy efficiency of the facilities. Seen paradoxically, the increase we have seen in the energy efficiency of the electrical loads, leads to the intensive use of power electronics resulting in a very high injection of harmonics in the distribution networks, thus causing a lack in the power quality leading a significant voltage waveform deformation. An electrical network containing a high harmonic content is synonymous of a low-level quality of the distributed energy, resulting in an increase of losses and low power factors, decreasing the energy efficiency of the installations. This paper presents a model that allows predicting the harmonic content present in an electrical installation, depending on the type of loads. Hence, it will be possible to know several qualitative parameters such as the voltage and current total harmonic distortion (THD), which will help to predict and size the mitigation measures to improve the power quality and energy efficiency of the facilities. © 2019 IEEE.

Abstract
Chord progressions play an important role in Western tonal music. For a novice composer, the creation of chord progressions can be challenging because it involves many subjective factors, such as the musical context, personal preference and aesthetic choices. This work proposes ChordAIS, an interactive system that assists the user in generating chord progressions by iteratively adding new chords. At each iteration a search for the next candidate chord is performed in the Tonal Interval Space (TIS), where distances capture perceptual features of pitch configurations on different levels, such as musical notes, chords, and scales. We use an artificial immune system (AIS) called opt-aiNet to search for candidate chords by optimizing an objective function that encodes desirable musical properties of chord progressions as distances in the TIS. Opt-aiNet is capable of finding multiple optima of multi-modal functions simultaneously, resulting in multiple good-quality candidate chords which can be added to the progression by the user. To validate ChordAIS, we performed different experiments and a listening test to evaluate the perceptual quality of the candidate chords proposed by ChordAIS. Most listeners rated the chords proposed by ChordAIS as better candidates for progressions than the chords discarded by ChordAIS. Then, we compared ChordAIS with two similar systems, ConChord and ChordGA, which uses a standard GA instead of opt-aiNet. A user test showed that ChordAIS was preferred over ChordGA and Conchord. According to the results, ChordAlS was deemed capable of assisting the users in the generation of tonal chord progressions by proposing good-quality candidates in all the keys tested.

Abstract
In multi-label classification problems an example can be simultaneously classified into more than one class. This is also a challenging task in Data Streams (DS) classification, where unbounded and non-stationary distributed multi-label data contain multiple concepts that drift at different rates and patterns. In addition, the true labels of the examples may never become available and updating classification models in a supervised fashion is unfeasible. In this paper, we propose a Multi-Label Stream Classification (MLSC) method applying a Novelty Detection (ND) procedure task to update the classification model detecting any new patterns in the examples, which differ in some aspects from observed patterns, in an unsupervised fashion without any external feedback. Although ND is suitable for multi-class stream classification, it is still a not well-investigated task for multi-label problems. We improve a initial work proposed in [1] and extended it with a new Pruned Sets (PS) transformation strategy. The experiments showed that our method presents competitive performances over data sets with different concept drifts, and outperform, in some aspects, the baseline methods.