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The list of national projects SAS

Institute of Informatics
Algorithm of collective intelligence: Interdisciplinary study of swarming behaviour in bats
Algoritmus kolektívnej inteligencie: Interdisciplinárne štúdium swarmového správania netopierov.
Program: SRDA
Project leader: Ing. Zelenka Ján PhD.
Annotation:Various algorithms of artificial intelligence inspired by real biological mechanisms are successfully applied in military and civil sector. In this proposed project, cooperative research of four scientific institutions having different basis, methodology and the object of study (biology, computer science and technology) focuses on interdisciplinary study of social self-organizational behaviour of tree-dwelling bats with the aim to develop new meta-heuristic method capable of space exploration. The project has a great potential to bring new scientific knowledge about mechanisms of collective intelligence in social structures of biological organisms, which will contribute to the field of theoretical biology, behavioural nad evolutionary ecology. Additionally, it will contribute to the field of artificial intelligence as it focuses on swarming behaviour of individuals/agents with higher nervous activity and well developed cognitive skills. The model organisms (bats) on which this project is focused are using advanced biological mechanism capable of state space exploration and at the same time preventing group disintegration. This specific characteristic has a great potential for development of new biologically inspired algorithms and methods applicable e.g. in movement coordination of unmanned aerial vehicles. Distinguished contribution of the project is R&D of ultra-light sensors applicable not only in biological research. The project originality is in interconnection of different research areas. Therefore it is important to review it interdisciplinary.
Duration: 1.8.2018 - 31.7.2022
Analysis of environmental influences on power industry equipment by the methods of artificial intelligence and cloud computing (ARIEN|
Analýza vplyvu prostredia na zariadenia energetického priemyslu metódami umelej inteligencie a cloudového počítania
Program: SRDA
Project leader: Ing. Kvassay Marcel PhD.
Annotation:The main goal of project ARIEN is to develop & verify a new innovative method for the estimation of local pollution deposition for power industry needs based on the data collected by the SHMU Institute. It will rely on advanced techniques of artificial intelligence, cloud computing and data integration and was not yet applied in Slovak power industry. It will help to prevent outages on 110 kV and 400 kV overhead lines caused by flashovers. The results obtained by this method will help to optimize the design of insulators and improve the quality of maintenance during operation. Artificial intelligence is the logical choice, because so far we have not been able, due to their complexity, to describe the processes of pollution dispersion, deposition and chemical interaction adequately by standard physical and chemical models. Techniques of artificial intelligence and neural networks can find new relationships between the input and the output data. Neural networks, however, require large training sets. Therefore, the second project goal is to develop a method for obtaining a sufficiently large set of synthetic data for their training and testing. The third project goal is to research & develop a new method for measuring the insulator pollution by the analysis of their acoustic emissions captured by contactless detectors placed in their vicinity. Here we rely on the experience of the project partner VUJE with measurement systems installed on overhead lines and able to work during their normal operation. These goals reflect current priorities of European research as well as pressing needs of Slovak power industry: in 2015, the standard STN 33 0405 regulating the assessment of high-voltage insulator pollution deposition and their required cleaning frequency, was withdrawn. The new methodology will help replace the tedious & demanding process mandated by this standard by a new one relying on readily available information and the latest computing techniques.
Duration: 1.7.2021 - 31.12.2023
Early Warning of Alzheimer
Early Warning of Alzheimer
Project leader: Ing. Rusko Milan PhD.
Duration: 1.9.2020 - 31.8.2023
Electron beam lithography of nanometer structures for 2D materials on the base of metal sulfides
Elektrónová litografia nanometrových štruktúr pre 2D materiály na báze sulfidov kovov
Program: VEGA
Project leader: Ing. Mgr. Andok Robert PhD.
Annotation:Motivation of this project comes out the research of new 2D materials such as metal sulfides and from the present state of development of e-beam lithography (EBL) as one of the alternative methods of 2D structures preparation in electronics. This project is focused on obtaining new scientific results in the patterning of nano-scale structures (10-100nm) in electron resists. Attention will be paid to the research of the influence of the electron lithography processes on the resulting nanometric patterns patterned in electron resits in term of resolution, dimensions accuracy and edge roughness of the structures in the resist. We will study the parameters influencing the profile of the nanometric patterns in polymeric resists. The simulations of lithographic parameters of electron resists on thin semiconductor layers, nitride membranes and layers with 2D materials, are an important part of this project. Based on the simulations of lithographic parameters, we will prepare nanometric structures on these materials.
Duration: 1.1.2018 - 31.12.2021
Intelligent Cloud Workflow Management for Dynamic Metric- Optimized Application Deployment (ICONTROL)
Inteligentné riadenie tokov práce v cloude pre dynamické a metrikami optimalizované nasadzovanie aplikácií
Program: SRDA
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:ICONTROL is a platform for dynamic and intelligent function-based workflow application deployment and run-time redeployment in hybrid edge cloud computing environments. ICONTROL will automatically construct and deploy complex workflow-based cloud applications in an end-to-end manner. ICONTROL will use semantic information on available cloud-based application functionalities to automatically generate complex workflows of functions, which will be executed across edge and cloud resources. It will help users to automate function selection, configuration and deployment, dealing with run-time failure or performance issues through automated redeployment. To remove obstacles for non-cloud expert users to use complex function-based workflow applications, ICONTROL will support a separation of tasks among 3 categories of specialists. Cloud application developers will develop application and backend functions, semantically annotate and describe them. AI-powering algorithms will assist domain application experts to create application workflows using functionalities provided by the cloud application developers, with semi-automated workflow construction techniques by leveraging semantic descriptions. Application users will be able to use their application workflows to instantiate, deploy and run their applications using the automation capabilities of the proposed system in complex hybrid edge and cloud environments, not burdened by complexities related to workflows selection, application deployment, fault resolution, resource elasticity provision, to name just a few. ICONTROL will significantly improve composability and adaptability of workflow-based applications spanning the entire edge-cloud continuum (from remote wireless IoT sensors, through personal devices, to large computing centers), by creating a semantically-enriched, platform-agnostic, and secured FaaS workflow development and execution platform, with automatic infrastructure resource management and provisioning.
Duration: 1.7.2021 - 31.12.2023
New Methods and Approaches for Distributed Scalable Computing
Nové metódy a prístupy pre distribuované škálované počítanie
Program: VEGA
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:Nowadays, there is a growing number of heterogeneous data from distributed sources, which brings great challenges to extract valuable knowledge from them. Current solutions are computationally-intensive modeling and simulations in a variety of science, industry and commercial areas through machine learning, neural networks, and deep learning. Due to the extremely multi-faceted dynamic data it is highly necessary to design novel methodologies, robust methods and approaches for scalable analytics in conjunction with scalable data collection, processing and management. High-performance platforms also need to be upgraded with the latest cloud technology knowledge for flexible management of large-scale systems. The project will also include research and development of appropriate tools and services for distributed and scalable information processing with the support for high-performance platforms. The proposed project builds on the results achieved in the VEGA 2/0167/16 project and in the four H2020 projects.
Duration: 1.1.2020 - 31.12.2022
Ontology representation for security of information systems
Ontologická reprezentácia pre bezpečnosť informačných systémov
Program: SRDA
Project leader: Ing. Budinská Ivana PhD.
Annotation:According to the action plan of the government of the Slovak Republic, one of the priorities is to “support research in cyber security”. This project proposal responds to the action plan's requirements by setting research objectives and goals in the field of cybersecurity. The project proposal focuses on new approaches in sharing knowledge about security incidents and indicators. There are several initiatives that seek to formalize and standardize security incidents descriptions. However, it is neither realistic nor desirable to assume that there will be one common standard for describing security incidents. The solution can be offered by creating a core ontology, which semantically integrates various approaches to describing threats and attacks, thus enabling the integration of several standards and knowledge repositories. The tools for representation and processing of ontologies will be used for this purpose. It will be a combination of procedures for collecting data from network communication, creating the necessary ontologies eg. semiautomatic extraction of ontologies from text in natural language – ontology learning, use of selected advanced methods of ontological knowledge representation, e.g. contextualized representation or methodology of ontological metamodeling. A significant shift will be that the ontology of the model can be represented by a graph. The results can be expected to contribute to more efficient sharing, representation, storage, and use of cybersecurity knowledge.
Duration: 1.7.2020 - 30.6.2024
Computer modelling of fire dynamics and effects
Počítačové modelovanie dynamiky požiaru a jeho dôsledkov
Program: VEGA
Project leader: RNDr. Glasa Ján CSc.
Annotation:Research in the suggested project is oriented on formulation of new scientific knowledge in the field of computer modelling of fire dynamics and effects. Huge societal damages on property, environment, destroyed buildings and infrastructure and threat for human lives and health caused by fires create acute need for such research of all-society importance. The research will utilize the authors‘ results and experiences on experimental testing of fires and investigation of applicability of models and program systems capable to simulate complex processes related to fire. The research will focus on computer simulation of fire course and consequences of chosen fire scenarios and its efficient parallel realization on high-performance computer systems.
Duration: 1.1.2020 - 31.12.2022
Processing of sensor data via Artificial Intelligence methods.
Spracovanie údajov zo senzorov prostriedkami umelej inteligencie
Program: VEGA
Project leader: Ing. Malík Peter PhD.
Annotation:The project will propose new methods and algorithms for processing of multi-sensor data for tasks of object diagnostics, areas of interest evaluation, secure communication and simplification of the new intelligent models creation. The research will focus on advanced methods of artificial intelligence with emphasis on deep learning. Artificial Intelligence algorithms show significantly better results than traditional methods, and an example of this is the tremendous progress of refining semantic image segmentation through deep learning over the last five years. Modern, low-cost, miniature electro-mechanical structures allow simple integration and group deployment of multiple sensors, resulting in the production of a huge amount of multi-sensor data that can not be manually processed. The project output will include new models of accurate semantic image segmentation, precision object modeling, multiple sensor collaboration models, and secure data transfer, especially for the Internet of Things and Industry 4.0.
Duration: 1.1.2019 - 31.12.2022

The total number of projects: 9