Thanks to the emerging technologies of e-Learning, the analysis of data offers professionals in the education sector, advantages with the ability to impact and revolutionize in the future the way we analyze and evaluate the learning experience.
In line with the global tendency of automation of industrial processes, Alherpell has identified the need to automate the visual inspection of the raw lambskins that today is carried out by the hands of experienced operators and through which it is identify the defects present in the skins to be able to classify them according to the quality. The technical objective of the project is to design and develop the machine that automatically classifies the skins, making operators intervene only in the placement and removal of skins.
To achieve these objectives, Alherpell, with the collaboration of the knowledge transfer group DataScience@UB, has decided to design and prototype a machine that includes an intelligent vision system based on semantic segmentation using convolutional neural networks; being necessary a system of transport of the skins, the mechanical vision system (camera and controller) and the development of intelligent software for detecting defects.
This automation will allow, on the one hand, to increase the productive capacity of the leather warehouses and, on the other, to define criteria of categorization that can be extrapolated to other agents in the value chain, thereby reducing discrepancies and, therefore, the number of transactions not fruitful. This unification of criteria will translate into a reduction of the resources used in the sector and a reduction in the transport of unaccompanied lots and of the respective polluting emissions.
Within the framework of the Cartographic Plan of Catalonia, the Cartographic and Geological Institute of Catalonia is working on the elaboration of the technical specifications of the land covers Map of Catalonia. The ICGC has commissioned us a study to explore the viability of the application of deep learning techniques in the generation of maps.
A company in the online sector has generated a large volume of data that provides a history to be able to extract information from clients and develop a predictive model based on the behavior of users. This behavior include the navigation through the platform, the interaction with others channels, messages to the community, etc. The ultimate goal is to provide a better customer service by recommending products based on the specific time of the customer journey.