This piece of research is focused on providing a review of the software solutions that exist when it comes to mechanisms that govern the management of intellectual property. It takes a deeper look at requirements within the university transfer office domain. Universities are a genuine source of knowledge. They have been identified not just as knowledge hubs but also as the spaces where innovations are born. These innovations then make their way into the market through the different industries they serve, becoming products that gain the attention of actual consumers. Given the magnitude of the innovations being developed in different universities around the world, it is imperative that mechanisms for the safety of this knowledge also be put into place. The world has evolved into a knowledge economy, where knowledge is an asset and something that can create profitability. This means that not protecting the knowledge that is being created can only lead to a loss in the future. Managing intellectual property, therefore, is not only a matter of procedure but one of great importance. Solutions that are easily accessible, cost-effective, and time-effective are essential. Thus, the goal of this article is to provide an overview of existing software (SW) solutions suitable for managing technology and knowledge transfer at universities based on requirements from the technology transfer office at university and specified using the model of the whole process from inventor until patent office. University Technology Transfer (TT) is a bit different in comparison to TT at companies. This gap is shown in the article using modelling of process, states, and class diagrams of a university Technology Transfer Office (TTO). Based on process definition and TTO responsibilities, a review of available SW solutions is done for 10 selected examples, as well as a related literature analysis. Findings and implications are summarized at the end of article in the context of specific needs of a university TTO, while major implications are shown as a problem of priority definition of every university TTO, namely, in the sense of value of SW solutions for intellectual property (IP) management, reporting possibilities, and representing IP and know-how.

No abstract

The article deals with using computational method and digital filters in power grid quality analysis in metallurgical industry. Almost all of metallurgical factories using large engines, arc furnace, induction heating and so on. This amount of non-linear loads which are connected to power grids cause heavy disturbances, which can cause big problems like damage or even destruction of machines or devices connected near the point, where disturbances occur, for examples near cities. Identification of disturbances is then very important for elimination of these problems. The article describe usage of digital filters which are applied on measured signal from power grid. Using these types of digital filters is necessary for identification problem in power grid.

The article deals with the automatic selection of the binarization method using advanced methods of artificial intelligence. The input images to the algorithms are images of serial numbers from industrial environments, for example on iron and steel billets, slabs, etc. The surface of these products is in most cases severely damaged by industrial processes, such as traces of cut, rust, noise, surface roughness, etc. Text recognition is a very common topic nowadays. All investigated solutions are based on the fact that each image is binarized by a single defined method and the accuracy of recognition is given only by the quality of learning of the neural network. Especially in an industrial environment, it is difficult to create a universal method for unambiguous methods for text recognition. The innovation described in this article is the automatic selection of the binarization method (from the Bradley, Niblack, Sauvola methods etc.), which increases the accuracy already in the phase before the text recognition itself, which with the subsequent correct combination of filters leads to an overall increase in accuracy.

No abstract