Sirzat Kahramanli

An Off-Cubes Expanding Approach to the Problem of Separate Determination of the Essential Prime Implicants of the Single-Output Boolean Functions

The goal of this study is the avoidance of excessive amount of temporary results produced during the minimization process of the two-level single-output Boolean functions of many variables. In this paper we have proposed an off-set based-direct cover minimization method that uses a single on-cube oriented expanding of the off-cubes on which the essential prime implicants are identified in a one by one manner and are used for iterative covering the function being minimized. The amount of temporary results produced by this method does not exceed the size of the offset. The proposed algorithm is up to 3 times faster and uses significantly less amount of memory than well known ESPRESSO.

Thermographical Investigation of Crack Initiation Using Artificial Neural Networks

In this study, a thermographic infrared imaging system was used to detect the temperature rise of AISI37 steel specimen under reverse bending fatigue. Fatigue behavior of metals shows temperature profiles with three stages: an initial increase of the specimen mean temperature region, a constant (equilibrium) temperature region, an abrupt temperature increase region at end of which the specimen fails and its temperature falls instantly. In order to recognize critical third region, it is necessary to observe endurance state of the specimen being tested. In this study, the temperature profiles of the specimen under testing are recorded by thermal camera and transferred to the image processing program. The artificial neural networks obtain spot temperatures of the inspected specimen by using its temperature profiles. By analyzing the values of obtained data, we detect spots of highest temperatures as ones that are exposed to most intensive deformation. These regions considered to be probable crack initiation sites.

The logic transformations for reducing the complexity of the discernibility function-based attribute reduction problem

The basic solution for locating an optimal reduct is to generate all possible reducts and select the one that best meets the given criterion. Since this problem is NP-hard, most attribute reduction algorithms use heuristics to find a single reduct with the risk to overlook for the best ones. There is a discernibility function (DF)-based approach that generates all reducts but may fail due to memory overflows even for datasets with dimensionality much below the medium. In this study, we show that the main shortcoming of this approach is its excessively high space complexity. To overcome this, we first represent a DF of

A Boolean function approach to feature selection in consistent decision information systems

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ATTRIBUTE REDUCTION BY PARTITIONING THE MINIMIZED DISCERNIBILITY FUNCTION

n attributes by a bit-matrix (BM). Second, we partition the BM into no more than

A Novel Essential Prime Implicant Identification Method for Exact Direct Cover Logic Minimization

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