This puts focus on the scheduling policy of the system, which is the algorithm that decides which task to execute at a given time. The presence of a scheduling policy introduces a
A current trend in granite processing plants located in northwest Spain is the implementation www.selleckchem.com/products/crenolanib-cp-868596.html of traceability systems to better control and manage stocks of slabs and end products resulting from the processing of a wide range of lithological materials, all considered as granite varieties from a commercial perspective. Cut and processed blocks have different mineralogical characteristics and origins, and this enormously complicates control over end products. Blocks are initially identified in the Inhibitors,Modulators,Libraries quarry by marks indicating type and origin and the edges of slabs, once sawn, are colour coded for identification purposes.
The market, however, demands end products of specific sizes and shapes (square and rectangular) and finishes with arrises and perpendicular edges. Consequently, Inhibitors,Modulators,Libraries the marks made on the edges of slabs are inevitable lost when these Inhibitors,Modulators,Libraries are cut using a diamond-disk saw.We describe an expert system for identifying different types of granite by spectrophotometer-based colour characterization applied in all the processing phases until perfectly shaped and squared slabs are obtained, with the ultimate aim of improving the current discontinuous control and management system used in plants. The effectiveness of this approach in terms of analysing and characterizing stone types on the basis of colour has already been reported by other authors [1�C4].
The classical Inhibitors,Modulators,Libraries methodology for classifying and identifying different varieties of granite is to analyse textural aspects in direct petrography studies of thin laminates. Other approaches are to study photomicrographs of rock in thin sections using digital image processing and texture analysis [5], to analyse images for colour and texture attributes [6,7] and to make quantitative colour measurements from scanner-captured digital images [8].Our identification methodology is based on: (1) objectively characterizing stone colour using a spectrophotometer; (2) discretely transforming reflectance data (collected by the spectrophotometer sensors in various sections of the visible-light region) into spectral curves in a smoothing process; and (3) resolving the classification problem using machine learning techniques for functional data.
Our approach ensures objectivity and minimizes possible human error in the identification process associated with different perceptions of colour, observation times and Batimastat object sizes.The functional spectral selleck chemicals CHIR99021 information was processed using a functional linear regression model and a functional support vector machine (SVM) with a PUK kernel (see [9,10] for classification problems successfully solved using SVMs with a PUK kernel and [4,11�C13] for functional problems resolved by SVMs and functional linear regression).