Factory production control (FPC) and its requirements for the metallurgical industry

The article the requirements of the Factory Production Control (FPC) dedicated to construction products (including metal, i.e. ribbed bars, steel pipes, shapes, sheets and metal constructions) introduced to the single market of the European Union, was presented. Meeting the requirements of legal regulations with regard to these products is an important issue for metallurgical companies placing their products on the EU market. These enterprises are required to effectively implement and supervise the FPC system, supervised by a party independent of the manufacturer (third party). The aim of the article is to present the requirements of the FPC and refer them to the requirements of industry standards for metal products – shapes

Factory production control (FPC) and its requirements for the metallurgical industry

The article the requirements of the Factory Production Control (FPC) dedicated to construction products (including metal, i.e. ribbed bars, steel pipes, shapes, sheets and metal constructions) introduced to the single market of the European Union, was presented. Meeting the requirements of legal regulations with regard to these products is an important issue for metallurgical companies placing their products on the EU market. These enterprises are required to effectively implement and supervise the FPC system, supervised by a party independent of the manufacturer (third party). The aim of the article is to present the requirements of the FPC and refer them to the requirements of industry standards for metal products – shapes

On the Finite Dimensional Laws of Threshold GARCH Processes

In this chapter we establish bounds for the finite dimensional laws of a threshold GARCH process, X, with generating process Z. In this class of models the conditional standard deviation has different reactions according to the sign of past values of the process. So, we firstly find lower and upper bounds for the law of \left ({X}_{1}^{+},-{X}_{1}^{+},\ldots,{X}_{n}^{+},-{X}_{n}^{+}\right), in certain regions of R^{2n}, and use them to find bounds of the law of \left ({X}_{1},\ldots,{X}_{n}\right). Some of these bounds only depend on the parameters of the model and on the distribution function of the independent generating process, Z. An application of these bounds to control charts for time series is presented

New neighborhood based rough sets

Neighborhood based rough sets are important generalizations of the classical rough sets of Pawlak, as neighborhood operators generalize equivalence classes. In this article, we introduce nine neighborhood based operators and we study the partial order relations between twenty-two different neighborhood operators obtained from one covering. Seven neighborhood operators result in new rough set approximation operators. We study how these operators are related to the other fifteen neighborhood based approximation operators in terms of partial order relations, as well as to seven non-neighborhood-based rough set approximation operators

A Dispersion Operator for Geometric Semantic Genetic Programming

Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. However, there are still many open issues on how to improve their search mechanism. One of these issues is how to get around the fact that the GSGP crossover operator cannot generate solutions that are placed outside the convex hull formed by the individuals of the current population. Although the mutation operator alleviates this problem, we cannot guarantee it will find promising regions of the search space within feasible computational time. In this direction, this paper proposes a new geometric dispersion operator that uses multiplicative factors to move individuals to less dense areas of the search space around the target solution before applying semantic genetic operators. Experiments in sixteen datasets show that the results obtained by the proposed operator are statistically significantly better than those produced by GSGP and that the operator does indeed spread the solutions around the target solution