The Industry 4.0 National Technology Platform was established under the leadership of the Institute for Computer Science and Control (SZTAKI), Hungarian Academy of Sciences, with the participation of research institutions, companies, universities and professional organizations having premises in Hungary, and with the full support and commitment of the Government of Hungary, and specifically that of the Ministry of National Economy.
The aim of the present study is to develop and evaluate a computer-based methods for automated and improved detection and classification of different colorectal lesions, especially polyps. For this purpose first, pit pattern and vascularization features of up to 1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and subsequently removed for histological analysis. The polyp images are analyzed by a newly developed deep learning computer algorithm.
PREdictor for HUman-RObot COllaboration (PREHUROCO) utilizes interactive technologies in a new innovative way to create a cobot independent pre-collision approach for reducing these cumulative delays. PREHUROCO technology creates a shared virtual reality workspace over the Internet/Intranet for humans and robots. The human operator will be digitally immersed into the real-time digital twin of the manufacturing cell by XR visualization and/or haptic feedback.
The HydroCobotics H2020 cascade consortium is coordinated by industrial robotics company Hepenix kft., while hydroponics company Green Drops Farm kft. is also a member. Within ELKH SZTAKI, the project is coordinated by Research Laboratory on Engineering & Management Intelligence research fellow Imre Paniti. The research and development project starts on the 1st of January, 2021 and presumably concludes on the 30th of September, 2021.
Knowledge graphs have become the most important tools of sharing and connecting research or industry information and contacts. These graphs enable, in a flexible way, the access, use and publication of data as a distributed system.
The DIGITbrain project aims to enable customised industrial products and to facilitate cost-effective distributed and localised production for manufacturing SMEs, by means of leveraging edge-, cloud- and HPC-based modelling, simulation, optimisation, analytics, and machine learning tools and by means of augmenting the concept of digital twin with a memorising capacity towards a) recording the provenance and boosting the cognition of the industrial product over its full lifecycle, and b) empowering the network of DIHs to implement the smart business model "Manufacturing as a Service".
The goal of the project is to establish a Hungarian research data repository supporting the COVID-19 pandemic related research initiatives. The research data repository serves as a data store and repository solution both for international and national uses, and provides a definite (peer-to-peer) controlled reliable data sharing platform.
The system builds on the secure data storage and well defined access rights existing in the MTA Cloud services, which is jointly maintained by SZTAKI and Wigner research institutes.
The DigiPrime H2020 consortium groups 8 research centers and 25 companies of 36 European countries that plays major role in the digitization of circular economy. SZTAKI joins in with AI and Digital Twin solutions, among other technical and scientific expertise.
NEANIAS will promote Open Science practices and play active role in the materialization of the EOSC ecosystem by efficiently engaging large scientific and professional communities; actively contributing to the technological, procedural, strategic and business development of EOSC.
Az MTA SZTAKI korábban részt vett a Vízügyi Digitális Tudástár (VDT) tervezésében és létrehozásában, amelynek eLearning-es része 40 szaktárgyat tartalmaz. A szaktárgyak elektronikus oktatását biztosító eLearning tananyagok szöveges anyagai mellett a tananyagok több ezer multimédiás elemet (képek, videók és animációk) és rengeteg matematikai képletet tartalmaznak. Az újrafelhasználhatóság miatt az elektronikus tananyagok szabványos formátumban (SCORM) készültek el.