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Cross modeling of embedded systems using SysML and Petri Nets

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Szmuc W. , Szmuc T.

International Journal of Microelectronics and Computer Science

2018

tom Vol. 9, nr 2

60--65

Cross modeling in embedded systems development is proposed in the paper. The main idea consists in a translation of SysML artifacts into the related Coloured Petri Net (CPN) models, which may be verified directly or using other tools, e.g. Temporal Logic Provers. The paper is an extension of [20] mainly by insertion of sequence diagrams (SysML) and presentation of their mapping into CPN models. The additional part describes communication features and completes the cross modeling approach.

Applications of rough sets in big data analysis: An overview

100%

Pięta P. , Szmuc T.

International Journal of Applied Mathematics and Computer Science

2021

tom Vol. 31, no. 4

659--683

Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.

A novel method for fast generation of 3D objects from multiple depth sensors

70%

Szmuc T. , Mrówka R. , Brańka M. , Ficoń J. , Pięta P.

Journal of Artificial Intelligence and Soft Computing Research

2023

tom Vol. 13, No. 2

95--105

Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at https://github.com/SATOffice/improved_scanner3D.