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The huge industrial data recorded by several years in copper bioleaching operations represents an opportunity for the technology improvement. A systematic approach is being developed to get insights from the field data from an industrial process and to deliver the obtained knowledge with the aim to serve as the foundation for optimal industrial decision making even in presence of inherent process variations. The development of this Decision Support System (DSS) considers a Q-PCR array, a database for data logging and storage, the application of suitable statistical and computational tools for data analyses and knowledge acquiring and finally the creation of a system of knowledge translation to transform it into action (operational suggestions). The user can accurately retrieve data and design similar matches to the historic operation to get e.g. the expected metallurgical performance of a strip based on its mineralogical parameters. In addition, the user can get computed information and recommendations that should be analyzed. We will discuss the process followed to construct the base of knowledge of the DSS.

We present a technique for face recognition in videos. We are able to recognise a face in a video sequence, given a single gallery image. By assuming that the face is in an approximately frontal position, we jointly model changes in facial appearance caused by identity and illumination. The identity of a face is described by a vector of appearance parameters. We use an angular distance to measure the similarity of faces and a probabilistic procedure to accumulate evidence for recognition along the sequence. We achieve 93.8% recognition success in a set of 65 sequences of 6 subjects from the LaCascia and Sclaroff database.