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• Unsupervised clustering algorithms can be used for crop type identification on remote images. • Results of performance of clustering algorithms and distance measures are analysed. • 26 datasets built with all the combination of 5 crops and more than 45000 parcels. • Best performances are obtained for Spectral clustering method. • Manhattan and Euclidean are in general the best distance measures. With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in the use of these technologies has to do with the ongoing increment on the spatial–temporal resolution together with the free availability of some of these services. Typically time series are used as a pre-processing technique and combined with supervised learning techniques in order to build models for crop type identification in remote images. However, these models suffer from the lack of labelled data sets needed to train them. Unsupervised classification can overcome this limitation but has been less frequently used in this research field. This paper proposes to test and analyse the performance of several unsupervised clustering algorithms towards crop type identification on remote images. In this manner combinations of clustering algorithms and distance measures, a key element in the behaviour of these algorithms, are studied using an experimental design with more than twenty datasets built from the combinations of five crops and more than 45000 parcels. Results highlight better clustering methods and distance measures to create accurate and novel crop mapping models for remote sensing images. [ABSTRACT FROM AUTHOR]

Smart grids can use energy stored in electric vehicles (EVs) through vehicle-to-grid (V2G) technology. When the mobility of electric vehicles is combined with V2G technology, electric vehicles can form an energy transmission network called the EV energy network. The EV energy network relies heavily on renewable energy, but due to the unstable nature of renewable energy, sometimes the energy supply is insufficient. If no adjustment is made for energy shortage, the EV energy network will soon collapse. This paper studies how to make the EV energy network operate normally as long as possible when the energy shortage comes. In order to ensure the survivability of the system in the event of energy shortage, a global optimization algorithm is proposed. New terms and criteria are defined for this scenario to evaluate algorithms' performance. The global optimization algorithm is simulated on real bus line data from Manhattan and the Pioneer Valley Transportation Authority (PVTA). Simulation results show that the algorithm is effective. [ABSTRACT FROM AUTHOR]