INTELLECTUAL PROCEDURES FOR ORDERING SEQUENCE ORDERS BY INHOMOGENEOUS FORMING OPERATORS

The researches results of discrete optimal planning problems of a wide range of production-technological, logistic and other service processes are presented. The planning methods are based on new intelligent procedures for ordering (IPO) sequences of elements (orders), which are implemented by means of constructive modeling. Purpose of procedures is to increase the efficiency of ordering receiving of orders, taking into account the complexity of the formation operations, as well as resource constraints. The article considers the models and methods of IPO application, which are focused on the processes of disbandment-formation (DF) of multigroup railway trains at sorting stations. Formally, such processes are represented by new models of ordering multi-sequences of orders taking into account the complexity of operations (OMSCE). In the search for optimal solutions, models of Hamming's associative memory are used, which allow to classify the current situations of OMSCE processes. In them, each class of certain states (taking into account the incompleteness and data perturbation) corresponds to one or more rational operators from among the possible ones. IPO procedures reduce the number of analysis options and increase the numerical efficiency of the optimizing multi-sequence orders method. The article presents the formalization of multilayer constructive models of OMSCE processes, intelligent procedures for methods of their implementation, the formation of the procedure for operations classification based on models of Hemming neural networks. At the same time, an improved structure of DF information technology with the use of intelligent procedures is also developed, examples of their application are given.

Weighted Quasi-Interpolant Spline Approximations of Planar Curvilinear Profiles in Digital Images

The approximation of curvilinear profiles is very popular for processing digital images and leads to numerous applications such as image segmentation, compression and recognition. In this paper, we develop a novel semi-automatic method based on quasi-interpolation. The method consists of three steps: a preprocessing step exploiting an edge detection algorithm; a splitting procedure to break the just-obtained set of edge points into smaller subsets; and a final step involving the use of a local curve approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), chosen for its robustness to data perturbation. The proposed method builds a sequence of polynomial spline curves, connected C0 in correspondence of cusps, G1 otherwise. To curb underfitting and overfitting, the computation of local approximations exploits the supervised learning paradigm. The effectiveness of the method is shown with simulation on real images from various application domains.

A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.