About one principle to identification of shape of object

Process of recognition of the shape of graphic objects consists of several stages. At the first stage as a result of processing images allocate some set of characteristic properties of some object, and on the second, make identification of object by means of comparison of these properties with properties of the sample. Presence of noise on real images often leads to disturbance of quantity and values in a set of such characteristic properties. In job methods of preliminary processing of images for receiving of set characteristic properties of objects and present methods of the identification are stated, allowing identifying the shape of graphic objects in conditions when the part of a set of characteristic properties of object concerning the sample is absent or is deformed by noise. Feature of the offered methods is their invariance to affine to transformations of the shape of object, and also high speed of identification, not dependent on complexity of identified object. Copyright © 2018 for the individual papers by the papers' authors

Improving the energy efficiency of the smart buildings with the boosting algorithms

In the promotion of modern real estates with a high level of energy consumption are the most important assessment of their energy efficiency. Increasing the level of technology in commercial buildings with digital infrastructure of accounting, control and management energy consumption has led to increased availability of data produced by the digital sensors. All this opens up huge opportunities for using of advanced mathematical models and machine learning methods that would improve the accuracy of forecasts of electricity consumption by commercial buildings, and thus improve estimates of energy saving. One of the most powerful algorithms in machine learning is gradient boosting (GBM). In this paper on GBM basis a method of the energy consumption profile modeling is proposed both for a separate building and for business centers. To evaluate its effectiveness advanced computer experiments were performed on real data of the energy consumption of commercial buildings. For this purpose different periods of model training were used, and its prediction accuracy was analyzed by several criteria. The results showed that the use of our model improved the accuracy forecasts of energy savings in more than 80 percent of cases compared to regression and random forest models. Copyright © 2018 for the individual papers by the papers' authors