Entropy of multidimensional cellular automata

Since the topological entropy of a vast class of two-dimensional cellular automata (CA) is infinite, of interest is the possibility to renormalize it so that to obtain a positive finite value. We find the asymptotics of the information function of a multidimensional CA and, accordingly, introduce the renormalized topological entropy as a coefficient of this asymptotics. We describe some properties of the introduced quantity, in particular, its positivity for CA of the type of “The Game of Life.” Also, we give an example of an explicit evaluation of this parameter for a particular cellular automaton.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.     

Probability-Theoretic and Statistical Analysis of Dictionary Texts

We consider probability-theoretic and statistical models and methods for computing the characteristics of dictionary structures. Results of a statistical analysis of several commonly used dictionaries are presented in order to test the adequacy of the computing methods proposed.     

Neural-Net Analysis of the Plasticity of ÉP-450 Ferrite–Martensite Steel with Different Alloying Element Concentrations

The results of a computational experiment with a neural-net model simulating the effect of the chemical composition on the plastic properties of ÉP-450 steel are presented. It is shown that computer-aided development of reactor steel with prescribed properties is, in principle, possible.